Two investment themes bookended 2023. In January, the European Union backed a $100m venture capital fund, managed by Momenta Partners. In December, Softbank announced its EUR473m ($514m) investment for a 51% stake in Cubic Telecom. This development more than drew the eye as exemplified by the analyst commentary around the high (16x) revenue to implied enterprise value multiple.

In between, the level of corporate activity in the IoT sector continued at roughly the same pace in prior years, albeit down on the years of heightened activity going back five or so years ago. There were several developments among the vendor and network operator communities, but less so among the IoT platform providers. Governments became more active with an emphasis on security and protections for the consumer sector.

Against the backdrop of 5G developments and 6G pathfinding, IoT is becoming a part of the fabric of enterprise operations and national infrastructure. Established players continue to emphasize connectivity, a relatively small portion of IoT value chains, while enterprises focus on quick-to-market solutions enabled by cloud providers and systems integrators. Both approaches risk leaving ‘system of systems’ issues for later consideration.

Investment Splash

As announced, the European Union’s VC funding effort targets Industry 5.0 or, “Industry 4.0 with-a-conscience” prospects because the Industry 4.0 movement is perceived to be overly tech-focused, and one that has failed to prioritise people and the planet. Consumer protection and sustainability are themes that reappear in other developments covered by this review.

SoftBank’s stake in Cubic Telecom drew attention for its high multiples and am implied valuation of over EUR900m for Cubic Telecom. This is a business that raised $124m over a period of some ten years. After beginning life as a company offering an over-the-air software management for M2M applications, it switched to explore the connected car space (working with Tele2) around 2014. An EUR18m investment from Audi and Qualcomm followed in 2015. SoftBank’s investment rationale into Cubic Telecom is to pioneer the future of software-defined connected vehicles. This does not look like an IoT connectivity deal given SoftBank’s 2022 equity investment into 1NCE, the latter being characterised as “the only company that can deliver global IoT”.

One insight on SoftBank’s investment can be gleaned from an even larger IoT investment from several years ago. In 2016, Cisco invested US1.4bn to acquire Jasper Wireless. Compared to Cubic Telecom, Jasper had raised a cumulative investment of $205 million over seven rounds. At the time, Cisco’s acquisition provided it with an entry point into the IoT sector as well as a channel comprising some 3,500 customers including big names such as Ford, GM, Heineken, and Boston Scientific. The acquisition seems to have helped Cisco over subsequent months as it brokered IoT deals with SalesForce.com, IBM and several mobile network operators internationally. Whether SoftBank can achieve the same market gains with Cubic Telecom remains to be seen.

With over 90% of Cubic Telecom’s revenues concentrated in Volkswagen Group, there remains a challenge to diversify the customer base. Of course, SoftBank’s relationships might help with Japanese vehicle manufacturers. This will take time and a greater investment in resources and coalition building. There should also be scope for product and service innovation involving connected car, intelligent transport, and electric vehicle charging systems. It’s worth noting that several months after its Jasper Wireless acquisition, Cisco’s continued foray into the IoT sector led to an additional $3.7bn acquisition of AppDynamics which was active in application performance monitoring, end-user monitoring and infrastructure visibility. Expanding the addressable market might be one factor in SoftBank’s investment calculus.

Incumbents’ Dynamics

Across network operators, connectivity platforms and vendors, the sharpest rise in corporate initiatives points to the ways in which vendors are trying to ease adoption and reduce the friction of developing solutions. For example, ST Microelectronics wants to make it easier to connect devices to cloud providers. It now offers microcontroller software and developer tools targeting Microsoft’s Azure IoT Hub and AWS cloud. ST Microelectronics also partnered with CommScope to integrate the latter’s PKIWorks IoT security platforms to align with align with the Connectivity Standards Alliance’s Matter standard. Making adoption easy applies to another strategic incumbent, Qualcomm. It launched a new platform called Qualcomm Aware comprising Qualcomm silicon and an ecosystem of hardware and software partners all wrapped in a cloud-friendly bundle to simplify the process of “getting into the IoT game”.

Mobile and low-power network operators continued at about the same level of corporate activity as 2022 with two themes apparent. One involved the launch of solutions for distinct verticals. In the utilities sector, for example, Vodafone launched its Water Metering solution for water management companies. Also targeting the water sector, UnaBiz (formerly SigFox) entered into a strategic partnership with KAIFA, a utility sector business digitalisation solution provider. In Australia, Telstra launched an end-to-end industrial automation capability, following its acquisition of industrial IoT providers Aqura Technologies and Alliance Automation. AT&T, one of the forerunners of the IoT industry even decided to relaunch its old “Connected Solutions” business unit. Beginning with connected cars, it wants to help customers navigate the 5G and IoT, by putting dedicated technology and sales executives alongside each other instead of separating them across different AT&T units.

The other theme involved horizontal, or extended connectivity, initiatives. Some of these combined licensed and unlicensed terrestrial network providers (e.g., Bouygues with Netmore Group, UnaBiz with The Things Industries to interwork SigFox and LoRa technologies). Others involved the combination of terrestrial and satellite communications means (e.g., Sateliot with Transatel, Skylo with Telefonica, EchoStar with the Things Industries and, Intelsat with Deutsche Telekom).

Platform providers were less in evidence as far as corporate initiatives are concerned. A marketing report by Analysys Mason for floLive, one of several to publish on eSIM and iSIM developments, suggested industry motivations are driven by a strategy of embedding connectivity earlier in the IoT value chain. For 2023, the requirements associated with this industry change, focused on flexible connectivity, outweighed M&A and platform innovation developments.

Government’s Growing Role

As the sector grows, IoT offerings are starting to expose externalities that purely market-based systems are not geared up to address. That is one explanation for the EU getting involved in VC funding for people and planet issues as noted earlier. In Scotland, the government sees the nation as expanding in a global market valued at $600bn. The country is investing in an innovation hub targeting IoT and related technologies such as sensing and imaging to help Scottish businesses explore opportunities “presented through advanced digital technologies”.

Cybersecurity and consumer protection are other areas where governments can address adverse externalities and set a positive path forward through regulatory and certification measures. For example, the UK government is enacting regulations for Security Requirements for Relevant Connectable Products targeting password management, vulnerability disclosures and software update support. In the USA, the Biden-Harris Administration announced a cybersecurity certification and labelling program via a “U.S. Cyber Trust Mark” to help consumers choose among smart devices that are safer and less vulnerable to cyberattacks. In Asia, the governments of Singapore and South Korea launched an initiative to develop a mutual recognition of IoT security certification schemes. These developments expose market gaps that individual companies and industry alliances are ill-positioned or unwilling to address.

Watching the Horizon

Whether they are labelled opportunities or challenges, other market gaps will shape the IoT industry over 2024 and beyond. Professional media sites such as LinkedIn and Medium are starting to fill up with individuals offering their IoT implementation services, a sure sign that supply and demand are rising up a notch.

Connectivity continues to dominate. To borrow a 1990’s marketing phrase that was commonly applied to sell the commercial Internet, connectivity is analogous to the ‘on-ramp’ for the IoT. However, connectivity represents one of a growing number of elements that contribute to an IoT solution. As the population of connected devices grows it will require both a structured framework and a suite of management services to interoperate at scale. This might emerge as the communications and cloud industries converge on 3GPP planning and a shift in emphasis to massive machine type (mMTC) use cases.

Governments and society are coming to terms that easy access to the Internet results in an asymmetric relationship between users and infrastructure and application providers. As an illustration of the challenges ahead, the Matter protocol set out to make connectivity simple and straightforward for consumers. While homeowners can mix and match devices from a growing ecosystem of suppliers, they still have to choose a home platform for management functions. This element, sitting above the connectivity layer seems to be dysfunctional and not just in a technical sense. As one reviewer put it, “now that Matter is here, these companies are wholly unmotivated to ensure their platforms work well with their direct competitors”. This is an appealing business scenario for system integrators and large platform (or ‘gatekeepers’ in competition law terminology) providers. The supply side of the industry will need to address issues of security, interoperability, certification and possibly data rights now that the wheels of government are rolling.

Another facet of the connectivity discussion is about interworking. How will deployments in large spaces function when they combine wide-area (cellular, satellite) and short-range devices (Bluetooth, Wi-Fi, Zigbee)? Edge computing concepts are applicable so that gateways aggregate short-range connectivity devices, for example. However, there is still a need for additional functionality to provide oversight and management functions and to make these capabilities appealing to developer communities. This dynamic will persist as the number of connected devices grows because many of these will be constrained by factors such as their energy envelope, power budget and sleep-modes of operation. Expect to see an extension of GSMA and TMForum efforts to define APIs that make intelligent management functions accessible to IoT system operators and developers.

A final observation relates to the scope of IoT. Many associate the term with connectivity and connected devices, as if connectivity is the biggest hurdle to overcome. Business users have progressed beyond connectivity and are increasingly adding IoT data management and remote-control capabilities as they deploy solutions for priority or business-critical use cases. Over the longer term, however, users will need to view IoT through a ‘system of systems’ lens. There will be situations that require cross-silo interoperability involving multiple IoT solutions and service providers working together. In addition to business model innovation, the technical challenges associated with improved decision-making will rely on making IoT work with digital twins as well as AI and ML algorithms in a systematic way. Today’s quick and easy solution is to concentrate IoT data in a cloud environment where all processing, intelligence and reporting are centralized. However, quite apart from ceding value to the cloud provider, there will be longer-term requirements for data provenance tracking and causal reasoning that call for bi-directional data flows. The proliferation of constrained IoT devices will call for edge processing and the coordination of distributed information processing and intelligence. These are reasons why notions of IoT connectivity and solutions, in multi-stakeholder settings, need to embrace system of systems approaches.

The post 2023 in Review: Connectivity dominates but IoT-system gaps remain appeared first on IoT Business News.

2023 was an eventful year in the tech sector, where AI went mainstream with the explosion of language learning models.

As we progress into 2024, the integration and evolution of artificial intelligence in various domains are not just changing; they are set to revolutionise the way we approach design, development, and deployment in these sectors.

With advancements in artificial intelligence accelerating at an unprecedented pace, we stand at the cusp of a new era where AI’s influence extends beyond mere automation to become a cornerstone of innovation and efficiency.

The engineering team at ByteSnap Design has been reflecting on the future of AI in the technology and electronics design industries.

Here is the team’s forecast for the pivotal AI trends likely to emerge during 2024 to redefine industry standards and drive forward a new age of technological excellence.

AI to trigger a battle of the Smart Assistants?

AI assistant tools will continue to be integrated into existing tools to make tasks easier. An example of this happening this year is Zoom’s AI companion which can summarise meetings into notes.

Expect to see development tools such as an IDE integration which can generate GIT commit notes, and release notes automatically during 2024.

We also predict companies will be trying to increase their profits with more monetisation from smart assistants such as Alexa and this will drive techy people towards open source alternatives, such as Home Assistant which run locally.

Rise of the Robotaxi

We anticipate that the first un-geofenced electric Robotaxis will become operational and start accepting paying customers over the coming months.

This is likely to scale steadily over the next few years to replace Uber as the transport medium of choice, providing legal issues can be overcome.

Ultimately, this will make car ownership a questionable decision because travelling this way could be cheaper than running a car.

Apple to enter the Generative AI race

We look forward to Apple unveiling a product to join the AI arms race with their own large language model. Other companies are embracing AI faster and already implementing it; for example, Bard into Google Assistant, and Microsoft’s push for AI in their Office 365 products. Nevertheless, Apple have a stronger than most in-house development philosophy, and it’s hard to see them allowing these products to go unchallenged.

Expect announcements in late 2024 from Apple around its generative AI offering.

Further AI disruption for the Smart Home market

Expect to see more innovation in the smart home market as consumers continue to look for ways to reduce their energy bills, with smart thermostats and TRVs becoming ever more popular. Nest are apparently trying to use AI to help understand consumers behaviour around energy consumption and we anticipate that this trend will continue.

With Apple releasing their Pro Vision headset in 2024, we also expect to see some manufacturers trying to compete with a cheaper product. Apple are excellent at design and are sometimes seen as a trend setter, but in this case are quite late to the party With Meta already well-established leaders. However, Apple have a history of knocking out incumbent leaders so this could be an interesting space to watch.

How much of a consumer appetite there is for this type of technology, however, remains open to question.

AI-enabled Integrated Circuits

We’re likely to see the greater emergence of AI on integrated circuits from companies such as Altered Carbon.

Computer chip manufacturers, Intel, for example, are incorporating AI cores into their CPUs.

AI algorithms in our view are mostly used for detection/categorisation. The classic example is using AI to detect whether an image contains a cat or a dog. However, even the way that the likes of Tesla use AI is similar – detecting images of signs for speed limits, or an image of the lines of the road – but the output is different in that it translates it into braking, accelerating or turning.

One of the projects we’ve worked on at ByteSnap sent accelerometer data into the cloud to detect people falling over. We see a scenario where a fall detector algorithm could be generated by AI and embedded within the sensor device, so that the huge amount of data does not need to be sent, allowing the product to consume less power.

Greater AI in Supply Chain Management

AI-powered forecasting is providing businesses with intelligence to prevent mishaps in the future, overcoming demand-supply mismatches to prevent overstock or understock of inventory.
This minimises costs and improves customer experience. We expect to see more of this across 2024. Additionally, AI-based algorithms are automating goods retrieval from warehouses for smooth order fulfilment, and AI-powered autonomous vehicles are reducing driver costs for delivery.

AI in software and electronics design

Software development and electronics design are both areas that AI vendors are targeting, as developers are expensive and timescales can be long. We can see initially the AI could be best at optimising PCB layouts in the hardware side and writing generic functions within software, albeit with dubious copyright infringement.

The work of translating very abstract requirements into real electronics still seems a very long way off though. This is partly due to lack of freely available models to train against in what is a fast-moving industry and little way for the circuits that are available to be assessed.

In addition, electronic engineering is actually quite a person-centred job; dealing with suppliers, customers, manufacturers, colleagues. Software AI trainers have raided github and ChatGPT was able to train linguistic models against the huge wealth of the World Wide Web.

However, for electronics, it will take another generation of AI development before engineering jobs are threatened.

The post ByteSnap Electronic Industry Predictions for 2024 appeared first on IoT Business News.

IoT Analytics published an analysis based on the “Generative AI Market Report 2023–2030” report and highlights the landscape with its top players in the data center GPU, foundational model and platform, and generative AI services markets.

Key insights:

• The generative AI market went from nearly nothing to a hot market within a year, as shown by IoT Analytics’ latest research report.
• IoT Analytics analyzed 3 interconnected markets for generative AI: 1) data center GPUs, 2) foundational models and platforms, and 3) generative AI services. Each has distinct aspects and market players.
• NVIDIA leads the data center GPU segment with a 92% market share, while OpenAI and Microsoft have a combined share of 69% in the foundational models and platforms market. The services market is more fragmented, with Accenture currently seen as the leader with a 6% market share.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “The speed of generative AI innovation with new offerings coming on the market on a weekly basis is fascinating monitor. Nvidia with 92% market share for data center GPUs as well as Microsoft and OpenAI with a combined 69% market share in the models and platforms segment are firmly in the lead in their respective market segments. With hyperscalers developing their own data center chips, with the availability of powerful open-source models and with giants like AWS and Google looking to differentiate with their new offerings, it will be interesting to watch how much the early lead is worth for the current market leaders. I personally do expect both Microsoft and Nvidia to maintain their strong positions in the coming years but the gap to the competition will likely close a bit.”

Philipp Wegner, Principal Analyst at IoT Analytics, adds that:

“The Generative AI market is rapidly evolving, with established leaders and a growing number of startups. In 2024, it’s a make-or-break year for Gen AI vendors, as they navigate a crowded field of competitors.”

The leading generative AI companies

The rise of generative AI

Following its release of ChatGPT in 2022, OpenAI experienced an impressive one-year, zero-to-$1 billion revenue bump—surpassed only by US-based chipmaker NVIDIA, which managed to increase its data center GPU sales from $3.6 billion in Q4 2022 to an expected $16 billion in Q4 2023. When it comes to generative AI companies, these two stand out.

The generative AI foundational models and platforms market is expected to reach nearly 5% of global software spending by 2030

According to IoT Analytics’ Generative AI Market Report 2023–2030 (published December 2023), the generative AI software and services market reached $6.2 billion in 2023. Although it is still very early to forecast where things are going from here, the IoT Analytics research team expects the generative AI foundational models and platforms market to make up nearly 5% of global software spending by 2030 due to its disruptive nature and tremendous value potential.

However, this does not include the market for individual generative AI solutions. The team believes generative AI will become standard within most software in the near future. This also does not include the hardware market, such as for data center GPUs, since this market is looked at separately from software but is discussed below.

In this article, we dive into the data center GPU, generative AI foundational model and platform, and generative AI services markets, discussing what aspects of the generative AI field make up each market and highlighting the leading generative AI companies within them.

Market segment 1: Data center GPU market

a.) Market overview

The data center GPUs market refers to specialized GPUs designed to handle the extensive computation demands of modern data centers, which are the backbone of generative AI. Originally designed for rendering graphics, GPUs excel at parallel processing, which is fundamental for deep learning computations used in generative AI.

The report shows the data center GPUs market reached $49 billion in 2023—a booming increase from 2022 (+182%), mostly driven by one company alone: NVIDIA. Although the market for data center GPUs has seen steep price increases and is undergoing severe supply constraints, there is currently no reason to believe demand will decline in the next two years.

b.) Leading data center GPU companies

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

1. NVIDIA

NVIDIA leads the data center GPU market by a long shot, owning 92% of the market share. In 2023, the company’s quarterly revenue jumped 272%, from $4.3 billion in Q1 to a forecasted $16 billion in Q4.

The NVIDIA A100 Tensor Core GPU is the de facto standard for data center GPUs. However, as discussed in the report, hardware is not the only differentiator for NVIDIA. Some consider their developer ecosystem, CUDA, as NVIDIA’s biggest moat, and it is often cited as the key reason why NVIDIA is not set to lose its dominant position anytime soon.

2. AMD

The Data Center segment of US-based semiconductor AMD player, NVIDIA’s first real GPU challenger, grew by 21% from Q2 2023 to Q3 2023 and shared 3% of the market. However, AMD has big ambitions in 2024 to eat into NVIDIA’s market share. In early December 2023, it announced the release of its Instinct MI300 Series accelerators, which are cheaper than NVIDIA’s comparable accelerators and, as AMD claims, faster. AMD’s CEO, Dr. Lisa Su, forecasted at least $1 billion in revenue in 2024 through this chip alone, and Microsoft, Meta, and OpenAI stated they would use the Instinct MI300X in their data centers. AMD also recently launched ROCm 6.0 to provide developers with an ecosystem that is equally attractive to CUDA.

3. Intel and others

US-based chipmaker Intel, the traditional competitor to NVIDIA and AMD, has lagged behind on the data center GPU front. In May 2022, Intel’s Habana Labs released its second generation of AI processors, Gaudi 2, for training and inferencing. Though not as fast as NVIDIA’s popular H100 GPU, it is considered a viable alternative when considering price to performance.

Meanwhile, in July 2023, startup chipmaker Cerebras announced it had built its first of nine AI supercomputers in an effort to provide alternatives to systems using NVIDIA technology. Cerebras built the system, Condor Galaxy 1, in partnership with the UAE, which has invested in AI research in recent years.

Market segment 2: Generative AI foundational models and platforms market

a) Market overview

The foundational models and platforms market comprises two related areas. Foundational models are large-scale, pre-trained models that can be adapted to various tasks without the need for training from scratch, such as language processing, image recognition, and decision-making algorithms.

Generative AI platforms, in turn, refer to software that enables the management of generative AI-related activities outside of foundational models. Notably, IoT Analytics identified six platform types: 1) development, 2) data management/databases, 3) AI IaaS/GPU as-a-service, 4) middleware & integration, 5) MLOps, and 6) user interface and experience (UI/UX).

The foundational models and platforms market exploded with the public release of ChatGPT in late 2022, reaching $3.0 billion in 2023. This is substantial growth over 2022, which saw next to nil in terms of revenue. IoT Analytics’ analysis projects strong market growth in the coming years as enterprises invest billions in—and report real value from—generative AI implementations and continuous improvements.

b.) Leading generative AI foundational model and platform companies

Unsurprisingly, the foundational model and platform market are currently led by OpenAI, with several well-known technology companies trying to catch up.

1. OpenAI

With the November 2022 launch and subsequent success of ChatGPT, OpenAI leads in the share of the foundational model and platform vendors market with 39%. Since the release of ChatGPT, OpenAI’s generative pre-trained transformer (GPT) models went from GPT-3.5 to GPT-4 to GPT-4 Turbo, showcasing the continued development of the model. OpenAI’s models continue to impress in independent model assessments and rankings—often coming out in the top three of all tested models. Although many experts expect the foundational model space to become a commodity over time, at this point, OpenAI’s flagship models remain the top foundational model on the most common benchmarks.

According to IoT Analytics’ What CEOs Talked About series, in 2023, ChatGPT skyrocketed in boardroom discussions in Q1, but as other foundational models and generative AI applications became available, mentions of ChatGPT steadily declined as “generative AI” separated and continued to rise. (The What CEOs Talked About in Q4 2023 report and blog is expected to be released mid-December 2023.)

2. Microsoft

On OpenAI’s heels at 30% market share is Microsoft, its largest shareholder. Microsoft’s platform, Azure AI, offers Azure OpenAI, which uses OpenAI’s LLMs but goes beyond the public ChatGPT offering by promising greater data security and custom AI apps. This is suited for enterprises who want to secure their proprietary data when leveraging the benefits of generative AI since ChatGPT’s terms of use state that they can store and use content (both input and output) to improve their services. In November 2023, Microsoft reported over 20,000 active paying customers for its Azure AI platform, adding that 85% of Fortune 100 companies used it in the past year.

Despite Microsoft’s strong partnership with OpenAI, Microsoft also heavily promotes the usage of other models, such as Llama 2, via its platform, thereby enabling customers to freely choose and test different models and providers. Another key priority for Microsoft is integrating AI capabilities into its existing product portfolio, such as Azure, Microsoft/Office 365, and Bing.

3. AWS

AWS has an 8% share of this market. Its Bedrock service, publicly released in September 2023, provides access to models from several AI companies, such as Anthropic, AI21 labs, and Cohere (each with a 2% share of this market), and combines them with developer toolsets to help customers build and scale generative AI applications.

AWS has quickly claimed the third spot in this market because the company is the market leader in public cloud services and quickly got its existing customer base excited about its differentiated approach to Generative AI. In contrast to Google and Microsoft, AWS Bedrock focuses on providing a platform service that gives users access to a number of both general and domain-specific foundational models from a variety of vendors—providing choice, flexibility, and independence.

4. Google

In 2022, most experts credited Google as being the one tech company at the forefront of AI. Many experts interviewed by the IoT Analytics team continuously praised Google for its AI and its data products and innovations. In 2023, the picture is different, and Google is fighting to defend its position as an AI leader.

Vertex AI is Google Cloud’splatform focused on machine learning (ML) ops. It is integrated with other Google Cloud services, such as BigQuery and Dataproc, and offers a Jupyter-based environment for ML tasks. In early December 2023, Google released a preview version of its new multi-modal flagship model, Gemini. The related technical report states that the largest of the Gemini family outperformed other existing models in 30 out of 32 common ML benchmarks. Initially, the announcement of Gemini was widely received as positive, but a popular demo video released by Google later turned out to be staged.

Market segment 3: Generative AI services market

a) Market overview

The generative AI services market represents a specialized segment dedicated to consulting, integration, and implementation support for organizations aiming to integrate generative AI capabilities. With generative AI having risen as one of the top discussion points in boardrooms, services companies are sensing a large opportunity in helping companies formulate their generative AI strategies (e.g., what use cases to implement), advising them on technical architecture choices (e.g., which models to use) and helping them implement and build individual solutions.

IoT Analytics assesses that the generative AI services sector’s opportunity is now. Due to the novelty of generative AI, organizations often lack skills and experience, and the only option is to look for professional services firms that have or are in the process of building up the required expertise.

b) Leading generative AI services companies

The generative AI services market is more dispersed than the other two markets highlighted here.

1. Accenture

Accenture is estimated to have the largest generative AI services market share at 6%. The company announced in June 2023 that it is investing $3 billion in data and AI practice over three years to double its AI talent and develop new capabilities. Additionally, Accenture disclosed in its Q4 2023 earnings press release that its revenue for generative AI projects grew to $300 million for 2023.

In November 2023, Accenture announced plans to launch a network of generative AI studios in North America where companies can explore ways to integrate generative AI applications. These studies are expected to be at Accenture Innovation Hubs in Chicago, Houston, New York, San Francisco, Toronto, and Washington, DC.

2. IBM

US-based technology corporation IBM makes up 2% of this market. To position itself for the opportunities that generative AI brings, the company announced it had established a “Center of Excellence (CoE) for generative AI,” which as of May 2023, already had over 1,000 consultants specialized in generative AI. The CoE operates alongside IBM’s AI and Automation practice, which includes over 21,000 data and AI consultants.

3. Capgemini

France-based IT services company Capgemini also has a 2% share in this market, offering consulting services intended to help clients adopt key technologies such as the cloud and AI. In July 2023, Capgemini announced the launch of a portfolio of generative AI services, including in the following areas:

• Strategy
• Customer experience
• Software engineering
• Custom solutions for enterprise

One of Capgemini’s current customers is London Heathrow Airport which aims to improve traveler experiences through its “Generative AI for Customer Experience” offer. Heathrow’s Director of Marketing and Digital, Pete Burns, stated that the project is intended to “assist, empower and delight passengers” with tailored customer service solutions.

4. The many others

Past this point, the remaining 86% of the market becomes a cornucopia of specialized generative AI services providers and larger general consulting and system integration companies, each taking a bite of the rapidly growing segment.

As an example, in April 2023, UK-based professional services company PwC announced plans to invest $1 billion over three years to not only grow its AI offerings but also transform how it works by using generative AI. Additionally, in July 2023, global consultancy firm McKinsey & Company announced it partnered with AI startup Cohere to provide customized AI solutions to its enterprise clients.

Generative AI company landscape outlook

The enterprise generative AI market is roughly a year old, and already, the generative AI companies landscape appears vast.

IoT Analytics released its first generative AI report, the Generative AI Trend Report, in March 2023. Since then, more foundational models and platforms have emerged, e.g., OpenAI’s GPT4 Turbo, Google’s Gemini, or Microsoft’s Phi-2. At the same time, the demand for data center GPUs exploded, which is also mirrored in NVIDIA’s stock performance (+231% year-to-date as of 14 December 2023). Finally, consulting giants have made investments to position themselves in the generative AI services market, such as Accenture’s $3B investment in AI and its pledge to double “AI talent.”

As part of this research, we talked to 30+ experts in the field and gathered information on 270+ generative AI projects and analyzed which industries and departments are fastest to adopt and which vendors are most selected today.
The coming months will reveal how many of those projects will deliver value besides just being a marketing coup or how many of those currently in the proof-of-concept stage will move forward. Most companies are only now forging their generative AI strategies and considering whether to build foundational models from scratch based on industry-specific data, use an out-of-the-box propriety model, or fine-tune open-source models. All of this comes as generative AI companies release new products at unprecedented speed.

There is still a lot of movement in the generative AI company landscape, and there will be more in the foreseeable future. IoT Analytics will stay on top of this space, with a follow-up report expected in 2024.

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The global smart city movement represents a major shift in how urban environments are designed, experienced and navigated. This monumental change is driven in part by digital transformation and Internet of Things (IoT) technologies, which are reshaping urban infrastructure and cityscapes into hubs of intelligent connectivity. Central to this trend is the emergence of advanced wireless technologies that align with the unique demands of smart cities. Among these emerging technologies, Wi-Fi CERTIFIED HaLow^TM stands out as an ideal wireless protocol for smart city connectivity.

Wi-Fi HaLow, an evolution of conventional Wi-Fi, is purpose-built to serve the needs of IoT applications. Incorporating the IEEE 802.11ah standard, it was released as a new certification by the Wi-Fi Alliance in November 2021. Wi-Fi HaLow operates in the sub-GHz band and surpasses traditional Wi-Fi in the 2.4, 5 and 6 GHz bands in terms of range, coverage and power efficiency, redefining the boundaries of wireless connectivity for smart city and IoT applications. Wi-Fi HaLow has the capacity to connect more than 8,000 devices from a single access point, providing long range connectivity beyond 1 km, low power consumption, advanced Wi-Fi CERTIFIED WPA3^TM security, and massive network density – precisely the attributes demanded by smart cities.

Building on the strengths of the IEEE 802.11ah standard, Morse Micro is developing next-generation Wi-Fi HaLow solutions that extend 10 times farther and cover 100x the area of traditional Wi-Fi networks. These advancements further the goals of smart city application developers, facilitating long-range connectivity, automating urban services and promoting environmental sustainability.

A prime example of this innovation is the potential impact of Wi-Fi HaLow networks on smart city transit systems. Traditional network infrastructure upgrades often reach bottlenecks due to the high cost and complexity of expanding wireline networks, underscoring the need for new forms of long range wireless technology. In such scenarios, Wi-Fi HaLow’s superior range, penetration and performance offer a transformative solution, far surpassing the range limitations of conventional Wi-Fi in the 2.4, 5 and 6 GHz bands while outperforming the low data rates of low-power wide-area networks (LPWANs) such as LoRa.

Wi-Fi HaLow’s versatility enables it to combine diverse building automation systems into a unified connectivity platform that provides an optimal balance of speed and range, and allows innovative IoT applications that may combine video with sensors, for example. It provides seamless connectivity between real-time operational data and the people and systems managing smart buildings, data centers, industrial processes, and other urban utilities. Its extended range and advanced security make it ideal for connecting a plethora of subsystems, from HVAC and smart lighting to microgrids and edge AI cameras.

By using standards-based Wi-Fi HaLow, the total cost to deploy and manage network services for smart cities is lower than other wireless solutions. Wi-Fi HaLow uses license-free radio spectrum in its operation, and Wi-Fi HaLow enabled equipment can be sourced from multiple OEMs. Unlike cellular service providers, which charge fees to use their networks, there is no recurring cost to use Wi-Fi HaLow connectivity. Expert personnel who understand Wi-Fi technology are plentiful and can use well-established methodologies to operate and maintain Wi-Fi HaLow networks. These economic benefits help reduce smart city operating costs, and the savings can trickle down to a municipality’s citizens.

On an enterprise level, Wi-Fi HaLow supercharges a wide array of smart city applications including security and safety systems, energy management, maintenance, occupant services, utility billing, demand management, indoor air quality (IAQ) monitors and compliance systems. With its distinct advantages in range, power efficiency, network capacity and security, Wi-Fi HaLow can equip these applications with the capacity to handle amounts of IoT device connectivity, significantly enhancing operations and services within a smart city.

Wi-Fi HaLow’s unique blend of long range, low-power consumption, advanced security and high-density connectivity is transforming smart city applications. Whether in support of automated transit systems, streamlined building operations or enhanced enterprise applications, Wi-Fi HaLow is a powerhouse protocol capable of addressing the myriad needs of a smart city. Its ability to connect thousands of IoT devices across sprawling urban landscapes enables efficient data sharing and automation, driving improved city services, environmental sustainability, and a higher quality of life for residents.

As cities worldwide transition to smart, connected environments, advanced wireless protocols like Wi-Fi HaLow have become key enablers of technology innovation. By providing a connectivity solution tailored to the distinct requirements of IoT applications, Wi-Fi HaLow isn’t merely contributing to the development of smart cities – it’s setting a higher standard of wireless communications. Wi-Fi HaLow’s growing market momentum represents a significant leap toward a smarter, safer, and more connected future, reshaping our urban landscapes one city at a time.

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The Internet of Things (IoT) landscape in 2024 is set for transformative changes, driven by advancements in cybersecurity, artificial intelligence (AI), and a plethora of emerging technologies, as IoT systems become increasingly integrated into critical infrastructure.

In this article, I shall delve into the various aspects of this transformation, exploring the impact of AI and machine learning (ML) in creating intelligent IoT systems, the rise of edge computing, the integration of blockchain for enhanced security, the introduction of ultra-thin smart shipping labels, the incorporation of the SGP.32 standard, and IoT’s burgeoning role in sustainability.

Increased focus on IoT cybersecurity

In 2024, the integration of IoT devices into vital systems like Smart Cities, coupled with the increased adoption of technologies such as 5G, eSIM, iSIM, and satellite connectivity, has emphasised the importance of robust cybersecurity measures. These advancements have made IoT devices more versatile and efficient, but they also necessitate enhanced focus on safeguarding data integrity and device security.

To address these needs, there’s a growing emphasis on deploying advanced encryption and rigorous security protocols. These measures ensure the protection of data transmitted between IoT devices and central systems. Additionally, continuous monitoring and real-time threat detection, powered by AI and ML, may well become standard practices. They help in promptly identifying and responding to potential security breaches, maintaining the integrity and reliability of IoT networks.

AI and ML enabling intelligent IoT systems

AI and ML are revolutionising almost everything, including IoT. By analysing massive amounts of data instantaneously, AI enhances IoT applications such as predictive maintenance and energy management. This synergy, combined with centralised IoT management platforms, leads to unparalleled operational efficiency.

In 2024, the integration of AI and ML will become much more embedded in IoT infrastructures. The blend of AI’s analytical capabilities with IoT’s data collection and monitoring functions creates an ecosystem where operational insights are gathered more efficiently and effectively, leading to smarter, more responsive IoT systems.

Edge computing enhancing IoT performance

Edge computing is revolutionising IoT performance by processing data closer to its source. This method significantly reduces latency, crucial for real-time applications such as autonomous vehicles, industrial automation, and augmented reality. These advancements are particularly pertinent in smart cities, healthcare, manufacturing, and retail, where they facilitate immediate data analysis and improve service quality.

Looking forward, the integration of AI and machine learning with edge computing is expected to increase, enabling edge devices to independently make complex decisions. The expansion of 5G networks will enhance communication between these devices, promoting faster, more efficient data processing. Furthermore, edge computing’s role in reducing energy consumption and carbon emissions underscores its significance in fostering a more sustainable IoT ecosystem.

Blockchain for IoT security

As IoT devices increasingly handle sensitive data, the role of blockchain in bolstering IoT security is becoming more prominent. Blockchain’s decentralised nature offers enhanced data integrity, making it a key player in protecting against the growing cybersecurity threats in the IoT landscape. Its integration with AI and ML is particularly noteworthy, representing a significant leap forward in building a resilient IoT infrastructure.

This combination promises to shape a stronger, more secure IoT ecosystem for 2024 and beyond, especially as the attack surface of IoT expands. Blockchain’s ability to ensure the authenticity and security of data transactions across the network is vital in this context, presenting a robust solution to the evolving challenges in IoT security.

Ultra-thin, low-power smart shipping labels

The ultra-thin, low-power smart shipping labels, first seen in early 2023 with our very own Smart Shipping Label, which is equipped with a printed, eco-friendly battery, features an eSIM, and supports up to 1000 messages across LTE-M, NB-IoT, and 2G networks.

Such labels will become much more prolific in 2024, due to their function as advanced tracking devices for items both large and small. They are capable of real-time monitoring of location, temperature, and package integrity, ensuring secure and efficient transit.

Thanks to their adaptability for various logistical needs, from tracking small documents to larger assets, these smart labels not only enhance supply chain efficiency but also align with sustainability goals, representing a significant advancement in IoT-driven asset management.

Integrating SGP.32 into the IoT ecosystem

The integration of the SGP.32 standard into the IoT ecosystem in 2024 heralds a significant advancement in device capabilities and application efficiency. SGP.32 is pivotal for use cases that demand high location accuracy, like precision agriculture, by providing superior geolocation services.

Moreover, the incorporation of SGP.32 plays a key role in the expanded use of eSIMs within IoT devices. This is particularly beneficial for global IoT deployments, as it simplifies the complexities associated with device management across different regions. Features like remote provisioning and profile swapping inherent in eSIM technology are instrumental in enhancing operational efficiency.

This development is not just a technological leap; it’s a strategic enabler for more efficient, globally connected, and responsive IoT ecosystems. The impact of integrating SGP.32 will be felt across various sectors, significantly contributing to the overall evolution and effectiveness of IoT applications.

IoT’s sustainability drive intensifies

Finally, in 2024 IoT will continue playing its pivotal role in driving sustainability across various sectors. Advanced, energy-efficient sensors, coupled with AI, are revolutionising resource management by enabling precise monitoring and control. This technological synergy is significantly reducing waste and optimising energy use.

In industries like manufacturing, IoT adoption is being accelerated by tightening global regulations, which are mandating more sustainable practices and better ecological footprints. IoT technologies are not only enhancing operational efficiencies but also promoting environmental stewardship. The implementation of smart systems in areas such as energy management and waste reduction are evidence of IoT’s growing influence in creating a more sustainable future.

As the world grapples with environmental challenges, the integration of IoT in sustainability efforts is becoming increasingly crucial, marking a new era where technology and ecology harmoniously intersect.

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In today’s fast-paced industrial and manufacturing sectors, safety and efficiency are paramount concerns. Companies are increasingly turning to innovative technologies to transform their workplace culture, with the Wearable Internet of Things (WIoT) taking center stage. This cutting-edge technology is not only overhauling traditional practices but also revolutionizing the way companies approach worker safety and productivity. In this article, we will delve into the ways wearable technology is currently reshaping the industrial and manufacturing landscape, explore the myriad benefits of WIoT, and shed light on the software solutions that are propelling this revolution.

A Shift in Workplace Culture

Industrial and manufacturing environments have long been associated with rigorous physical demands and safety concerns. However, as technology advances, so too does the ability to safeguard workers and improve overall efficiency. Wearable technology, in particular, has emerged as a game-changer. From smart helmets to augmented reality glasses, these devices are revolutionizing the way workers interact with their environment.

One of the most significant shifts brought about by WIoT is the move towards a more proactive approach to safety. Traditionally, safety measures were often reactive, focusing on addressing incidents after they occurred. With wearable devices, companies now have access to real-time data that enables them to identify potential hazards before they become accidents. For example, smart vests equipped with sensors can monitor environmental conditions, such as temperature and air quality, alerting workers and management to unsafe conditions instantly.

The Multifaceted Benefits of WIoT

The adoption of WIoT is not solely driven by safety concerns; it also promises a host of other benefits. One of the most compelling advantages is its ability to reduce worker fatigue. In physically demanding industries, fatigue can lead to accidents and decreased productivity. WIoT devices can monitor a worker’s biometrics, such as heart rate and body temperature, in real-time. When fatigue is detected, alerts can be sent to both the worker and their supervisor, prompting necessary breaks or adjustments to tasks.

Furthermore, WIoT is facilitating the digital transformation of facilities. These devices are no longer just tools for monitoring workers; they are becoming integral components of interconnected systems that optimize operations. For instance, by equipping machinery with IoT sensors, it becomes possible to track equipment performance, anticipate maintenance needs, and reduce downtime. This seamless integration of WIoT technology results in cost savings and improved efficiency.

Enhancing Body Mechanics with Wearable Devices

The realm of body mechanics in the workplace is also being revolutionized by WIoT. Wearable devices, such as exoskeletons and wearable sensors, are designed to support workers and help them maintain correct postures and motions. These devices are equipped with sensors that can provide real-time feedback to workers, guiding them to adopt ergonomic positions that reduce the risk of musculoskeletal injuries.

Additionally, the data and understanding collected by these wearable devices is a goldmine of information. To harness this potential, companies are turning to sophisticated software solutions. These solutions aggregate data from various wearable devices and integrate it into a centralized platform. This allows for comprehensive analysis and insights that were previously unattainable.

For example, advanced analytics can identify patterns of movement and posture that may lead to injuries over time. By utilizing this data, companies can implement targeted training programs to improve worker ergonomics and reduce the risk of chronic injuries. Furthermore, the data can be used to engineer workflows, optimize the allocation of tasks and resources for maximum efficiency.

The Power of Integration

Integration is key to unlocking the full potential of WIoT. By consolidating data from wearable devices into a single platform, companies can achieve a holistic view of their operations. This data-driven approach enables predictive maintenance, real-time safety monitoring, and workflow optimization, all within one cohesive system.

Moreover, the benefits of WIoT extend beyond the factory floor. Office-based employees can also benefit from wearable technology, as it can monitor posture and sedentary behavior, promoting better health and well-being. For instance, smart wristbands can remind office workers to take breaks, stretch, or adjust their sitting positions, reducing the risk of long-term health issues.

Embracing Innovation: WIoT’s Role in Shaping Tomorrow’s Workplace

The Wearable Internet of Things is ushering in a new era of workplace culture, where safety, efficiency, and worker well-being take center stage. Companies that embrace WIoT are not only reducing the risk of injuries but also driving digital transformation, reducing worker fatigue, and optimizing operations. With the integration of advanced software solutions, the potential for improvement is boundless.

As more companies recognize the transformative power of WIoT, it is clear that this technology is here to stay. It is no longer a matter of if, but when, organizations will adopt WIoT to enhance worker safety and productivity. The future of industrial and manufacturing workplaces is being shaped by wearable technology, and those who embrace it are poised to lead the way in the evolving landscape of worker safety and efficiency.

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In the face of escalating climate challenges, technology has emerged as a beacon of hope. The Internet of Things (IoT) stands out as a particularly powerful tool in the global effort to promote environmental sustainability. With its network of interconnected devices and sensors, IoT offers innovative solutions to monitor, understand, and address environmental issues, contributing significantly to the fight against climate change.

IoT: A Game-Changer for Climate Monitoring

Climate change is a complex beast, with a multitude of variables that must be tracked and analyzed. IoT technologies offer unprecedented granularity in environmental monitoring, with sensors capable of providing real-time data on everything from atmospheric CO2 levels to the health of ocean ecosystems. This data is invaluable for researchers and policymakers alike, offering up-to-the-minute insights that can inform responsive and effective environmental policy.

Energizing Renewables with IoT

Renewable energy sources like solar and wind power are crucial in the transition away from fossil fuels. IoT is instrumental in optimizing the performance of these energy sources. Smart sensors can track wind patterns and sunlight exposure, adjusting the positioning of turbines and solar panels to maximize energy capture. Moreover, IoT systems help in predicting maintenance needs, reducing downtime, and enhancing the overall efficiency of renewable energy infrastructures.

Smart Agriculture: Growing More with Less

Agriculture consumes a vast amount of our planet’s resources, but IoT is helping to change that. Precision farming techniques, underpinned by IoT, enable farmers to monitor soil moisture levels and crop health with pinpoint accuracy, leading to more judicious use of water and pesticides. This not only helps in conserving precious resources but also results in higher yields and better-quality produce.

Waste Not: IoT for Waste Reduction

Waste management is another area where IoT shines. Smart waste bins can signal when they are full, optimizing collection routes and frequencies. IoT systems also play a crucial role in the recycling industry, where they can sort materials more efficiently and identify contaminants that can hinder the recycling process.

The Smart Grid: An IoT-Enabled Energy Network

One of the most significant applications of IoT in sustainability is the development of smart grids. These intelligent energy distribution networks can balance supply and demand in real time, reduce energy wastage, and integrate a higher percentage of renewable energy sources. Consumers can play an active role in energy conservation through smart meters that provide real-time feedback on energy consumption, encouraging more responsible usage patterns.

Challenges to Overcome

Despite its vast potential, the widespread adoption of IoT for environmental sustainability is not without challenges. The energy consumption of IoT devices themselves is a concern; thus, it is imperative that these devices are designed to be as energy-efficient as possible. Additionally, the production of IoT devices must become greener, employing sustainable materials and minimizing waste.

Data privacy and security are also critical issues. The vast amounts of data collected by IoT devices must be kept secure to protect against breaches that could undermine public trust in these technologies.

Policy Implications and the Path Forward

To fully harness the potential of IoT for environmental sustainability, collaborative efforts are needed. Policymakers must create frameworks that encourage the development and deployment of sustainable IoT solutions. This includes investing in infrastructure, funding research and development, and setting industry standards that prioritize sustainability.

Cross-sector partnerships are equally important. The technology sector must work with environmental scientists, urban planners, and agricultural experts to create IoT solutions that are both technologically advanced and environmentally sound.

Conclusion

IoT offers a powerful arsenal of tools in the fight against climate change, from optimizing renewable energy to enabling smarter agriculture and waste management. However, the journey to a sustainable future requires more than just technology; it demands a collective commitment to innovation, responsible usage, and global cooperation. As we continue to harness the potential of IoT, we move closer to a more sustainable world where technology and the environment exist in harmony, combating climate change one smart solution at a time.

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The Global AI Safety Summit 2023, held at Bletchley Park and chaired by the UK, was a ground-breaking event that brought together 150 global leaders from various sectors to discuss the future of Artificial Intelligence (AI).

The agreement on the Bletchley Declaration marked the Summit, emphasising collaborative action for AI safety and the need for a shared understanding of AI risks and opportunities. A significant development was the initiation of the State of the Science Report, led by Turing Award-winning scientist Yoshua Bengio, aimed at providing a science-based perspective on the risks and capabilities of frontier AI.

During the Summit, there was a strong focus on the necessity of state-led testing of AI models, and the importance of setting international safety standards was highlighted. The UK’s announcement of launching the world’s first AI Safety Institute underlined its commitment to leading in AI safety research and testing. Summit participants also recognised the need to address current and future AI risks, emphasising standardisation and interoperability to mitigate these risks effectively.

While the majority of current conversations surrounding the impact of AI remain broad and high-level, it’s crucial to acknowledge the significant influence it will have in the realm of IoT. As we delve into this specific area, it is evident that not only will AI play a pivotal role in shaping IoT’s evolution, but the reverse is also true.

The data generated from IoT applications will not only feed into AI systems, enhancing their capabilities, but also emphasise the importance of data integrity. This mutual influence underscores a dynamic relationship where both IoT and AI will significantly shape each other’s development, making it imperative to recognise and address the intertwined futures of these technologies.

In fact, the evolution of AI’s capabilities in processing the vast data generated by IoT devices is propelling a transition from reactive to proactive and predictive operations across various sectors. This paradigm shift is not only about efficiency and reliability but also about establishing trusted and authentic data sources, which is where the Identity of Things (IDoT) comes into play.

Moving from basic identifiers to unique digital identities, IDoT ensures the authenticity of data and strengthens the trust in IoT ecosystems. Implementing technologies such as embedded SIM (eSIM) and integrated SIM (iSIM) is instrumental in this process. They enable better security through robust access control, enhanced data integrity, and reduced vulnerabilities while also addressing privacy concerns.

By ensuring compliance with regulatory standards, eSIM and iSIM contribute to standardisation and reliability, which are critical for scalable and interoperable IoT networks. These technologies support personalisation and accountability, leading to enhanced traceability and the capacity for more advanced predictive analytics.

As AI and IoT continue to converge, the focus on unique digital identities through IDoT will become a cornerstone in achieving a secure, reliable, and adaptable technological ecosystem, ready for the future of interconnected devices.

However, a critical aspect of integrating AI with IoT is ensuring the data integrity of the inputs. The data sourced for AI processing must be not only authentic and secure but also trustworthy. This is because the decisions made by AI are only as reliable as the data upon which they are based. Any security vulnerabilities at the point of data collection or transmission could lead to significant, potentially catastrophic, consequences.

It is, therefore, essential for multi-party IoT ecosystems to establish and maintain data integrity to prevent such risks. Technologies such as SIGNiT by G+D are addressing this critical need by employing digital signing of data generated by IoT devices, coupled with blockchain technology, to create a secure and trustworthy data environment. Ensuring the fidelity of data at its source is fundamental to building AI systems that can be trusted to make sound decisions.

The path forward is fraught with challenges, particularly concerning data privacy, AI’s decision-making transparency, and the reliability of AI algorithms. A significant concern is ensuring that AI integration does not inadvertently create vulnerabilities within IoT systems. To significantly mitigate these risks, we can harness advanced cryptographic techniques.

For instance, elliptic curve cryptography (ECC) is one such technique that provides high levels of security with smaller key sizes, making it more efficient for IoT devices which often have limited computational power. By incorporating blockchain technology and employing advanced cryptography like ECC, we can establish robust security protocols to protect data integrity and maintain the trustworthiness of AI-driven IoT systems.

In essence, the integrity of the entire data stream can be maintained by securing data right at the source – the IoT sensor – and using private keys on secure elements like SIM cards. However, integrating AI into existing IoT systems presents issues beyond data integrity alone. Such integration is a complex endeavour that demands a multifaceted and sophisticated approach to tackle various technical and operational challenges.

On the technical front, it involves ensuring compatibility between AI algorithms and diverse IoT devices, managing the vast data streams generated by these devices, and maintaining the responsiveness and reliability of the systems. The integration must be seamless, ensuring that AI algorithms can effectively interpret and act on the data from IoT devices without causing system lags or errors.

Moreover, this integration significantly impacts business models and operational workflows. For businesses, incorporating AI into IoT systems often means rethinking how they collect, analyse, and utilise data for decision-making. It requires shifting from traditional business processes to a more dynamic, data-driven approach.

Operationally, there’s a need for continuous monitoring and maintenance of these integrated systems, ensuring they operate efficiently and effectively. This shift also necessitates training and upskilling of staff to manage and leverage these advanced systems.

The overarching goal is to ensure that AI acts as a catalyst for enhancing IoT functionalities, not as a barrier. It should streamline operations, provide deeper insights, and open new avenues for innovation and efficiency rather than complicate or hinder existing processes. Thus, integrating AI into IoT systems is not just a technological upgrade but a transformative process that reshapes how organisations operate and interact with technology.

The successful implementation of this integration hinges on a careful balance – leveraging the advanced capabilities of AI to enhance IoT functionalities while also adapting to the new challenges and opportunities this fusion presents, with a clear and necessary focus on data integrity.

As we stand at the cusp of a technological revolution with AI and IoT at its core, balancing the immense opportunities with the inherent challenges is imperative. Ensuring data integrity, securing IoT ecosystems, and maintaining a controlled integration of AI are essential steps towards harnessing the full potential of these technologies.

The AI Safety Summit is just the beginning of a critical journey. The real challenge lies ahead in our industry’s hands. In the IoT sector, we must actively drive the development of responsible and effective strategies for AI integration. While the Summit set the stage, it’s now our responsibility to act.

The post AI and IoT: Post-AI Summit reflections on safe integration and data integrity appeared first on IoT Business News.

IoT Analytics published an analysis based on the “Predictive Maintenance & Asset Performance Market Report 2023–2028” report and highlights 5 key insights related to the $5.5 billion predictive maintenance market.

Key insights:

• The global predictive maintenance market grew to $5.5 billion in 2022–a growth of 11% from 2021—with an estimated CAGR of 17% by 2028, according to the Predictive Maintenance and Asset Performance Market Report 2023–2028.
• With median unplanned downtime costs larger than $100,000 per hour, the importance of accurately predicting failures of large assets has never been higher.
• This article shares 5 key highlights of the predictive maintenance market: 1) The market is valued at $5.5 billion, 2) there are 3 different types of predictive maintenance, 3) predictive maintenance software tools share 6 features, 4) predictive maintenance is commonly being worked into the maintenance workflow, and 5) successful standalone solutions vendors specialize in an industry or asset.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “Predictive Maintenance continues to be one of the leading use cases for Industry 4.0 and digital transformation, especially in process industries where asset failures can quickly go into the hundreds of thousands of dollars. It is great to see that the market is moving ahead with AI integration into existing APM and CMMS systems and that prediction accuracies are improving. Nonetheless, we still have a long way to go as false alerts remain commonplace.”

Fernando Brügge, Senior Analyst at IoT Analytics, adds that “Predictive maintenance is reaching new heights of maturity and sophistication thanks to the rapid advancements in artificial intelligence, hardware, and data engineering. We are at the point where these technologies enable us to collect, process, and analyze massive amounts of data from multiple sources, and use them to build more accurate and reliable models of machine health and behavior, as well as to determine potential courses of action to fix machine issues. In this way, predictive maintenance is not only a smart way to optimize equipment performance and lifecycle, but also a strategic way to enhance operational efficiency and competitiveness in a rapidly evolving industrial space.”

Predictive maintenance market: 5 highlights for 2024 and beyond

One accurately predicted failure of a large asset is worth more than $100,000 in many industries.

Our latest research highlights, among many other things, that the median unplanned downtime cost across 11 industries is approximately $125,000 per hour. With critical unplanned outages in facilities in industries such as oil and gas, chemicals, or metals occurring several times a year, an investment into predictive maintenance can amortize with the first correct prediction.

Unfortunately, there is a flip side: the accuracy of many predictive maintenance solutions is lower than 50%. This creates headaches for maintenance organizations that often run to an asset to find it is perfectly fine, eroding trust in the entire solution.

That said, vendors have been making strides to increase prediction accuracy, with more data sources and better analysis methods becoming available, including AI-driven analysis. There are positive signs that this determination for better prediction accuracy is helping end users: our research indicates that 95% of predictive maintenance adopters reported a positive ROI, with 27% of these reporting amortization in less than a year.

General search interest in predictive maintenance and related concepts has been on the rise for the last 12 years. Online searches for the term have grown nearly threefold since we initiated coverage on the topic in 2017 and have outgrown condition-based maintenance and asset performance management (APM) related searches.

Indeed, predictive maintenance appears to be well on track to be the must-have killer application we made it out to be in 2021.

In this fourth installment of our predictive maintenance market coverage, we look at 5 important highlights to note about the market going into 2024:

1. The predictive maintenance market is valued at $5.5 billion (2022)
2. There are 3 different types of predictive maintenance, with anomaly detection on the rise
3. Predictive maintenance software tools share 6 common features
4. Integration into the maintenance workflow is becoming important
5. Successful standalone solutions vendors specialize in an industry or asset

Highlight 1: The predictive maintenance market is valued at $5.5 billion

The predictive maintenance market reached $5.5 billion in 2022. Uncertain economic conditions and other manufacturing priorities in the last 2 years resulted in 11% market growth between 2021 and 2022. With companies reinvesting in efficiency, safety, and operational performance, we expect the market for predictive maintenance to grow to 17% per year until 2028.

Our research indicates that industries with heavy assets and high downtime costs are driving the adoption of predictive maintenance solutions (e.g., oil & gas, chemicals, mining & metals).

Highlight 2: There are 3 different types of predictive maintenance, with anomaly detection on the rise

As the market has evolved, 3 noticeable predictive maintenance types have developed:

1. Indirect failure prediction
2. Anomaly detection
3. Remaining useful life (RUL)

The difference between these largely comes down to the objectives, methods of data analysis, and type of output/information they provide. RUL is the hardest to achieve due to resource demands and environmental factors that make it difficult to scale. Indirect failure prediction has been the most used approach, but our research indicates that anomaly detection is on the rise.

1. Indirect failure prediction

The indirect failure prediction approach generally takes a machine health score approach based on a function of maintenance requirements, operating conditions, and running history. This approach often relies on general analysis to yield this score, though supervised machine learning methods can be used if a significant amount of data is available.

Benefits:

• Scalability – Indirect failure prediction can be more easily scaled since they rely on equipment manufacturers’ specifications that are more or less the same across machines of the same type.
• Cost effective – Indirect failure prediction can use existing sensors and data, reducing the need for additional instrumentation.

Limitations:

• Failure time-window accuracy – Indirect failure prediction does not give a timeline of when machines will fail. This can be a problem for organizations with very costly downtimes (e.g., heavy equipment industries).
• Dependent on historical data – Indirect failure prediction’s effectiveness relies on the availability of extensive historical data for accurate modeling.

2. Anomaly detection

Anomaly detection is the process of finding and identifying irregularities in the data (i.e., data points that deviate from the usual patterns or trends). While the indirect failure prediction and RUL approaches use failure data to predict future failures, anomaly detection uses the “normal” asset profile to detect deviations from the norm. These deviations can indicate potential problems, such as faults, errors, defects, or malfunctions, that need to be detected and addressed before they cause serious damage or downtime.
This approach makes it easier when there is not a good repository of failure data, and it often relies on unsupervised machine learning.

Benefits

• Low data and hardware requirements – Anomaly detection models can identify issues without being trained on failure data. Further, since these models need less data, they do not demand high computing power.
• High scalability and model transferability – Anomaly detection models are trained on normal operation data, so they can easily be applied to different machines without retraining or adaptation.

Limitations

• Failure time-window accuracy – As with indirect failure prediction, anomaly detection models do not give a timeline of when machines will fail, which can be a problem for organizations with very costly downtimes.
• Presence of false positives – While most solutions in the market can distinguish between critical and noncritical anomalies, the choice of unsupervised machine learning models is still important as it can affect how well this distinction can be made (e.g., autoencoders and generative adversarial networks do not capture the complexity of normal operations).

3. Remaining useful life (RUL)

RUL is the expected machine life or usage time remaining before the machine requires repair or replacement. Life or usage time is defined in terms of whatever quantity is used to measure system life (e.g., distance traveled, repetition cycles performed, or the time since the start of operation).

This approach relies on condition indicators extracted from sensor data—that is, as a system degrades in a predictable way, data from the sensors match the expected degradation values. A condition indicator can be any factor useful for distinguishing normal operations from faulty ones. These indicators are extracted from system data taken under known conditions to train a model that can diagnose or predict the condition of a system based on new data taken under unknown conditions.

Predictions from these RUL models are statistical estimates with associated uncertainty, resulting in a probability distribution.

Benefits

• Failure prediction time-window – RUL is especially useful for industries where maintenance is very costly and needs advanced planning, such as heavy-equipment industries.
• Output robustness – Since RUL estimates rely on high-quality and detailed data, they tend to be more robust and reliable.

Limitations

• Resource demand – Training large models requires powerful computing hardware, especially if done on-premises.
• Model transferability and scalability – Different environments and usage patterns can cause different failure modes for the same type of equipment. This means the model needs to be retrained for each specific case, reducing its scalability and generalizability.

Highlight 3: Predictive maintenance software tools have 6 common features

Software is the largest segment of the predictive maintenance tech stack, making up 44% of the predictive maintenance market in 2022.

Our report shows that even though most successful predictive maintenance software vendors specialize in industries or assets, there are 6 common features between their various solution software suites:

1. Data collection
2. Analytics and model development
3. Pre-trained models
4. Status visualization, alerting, and user feedback
5. Third-party integration
6. Prescriptive actions

We will delve further into these features and offer an example snapshot for each from various software vendors. The examples are to help readers understand some approaches to these common features.

Feature 1: Data collection

Data collection tools within predictive maintenance software collect, normalize, and store data on asset health/condition parameters. They also collect other data types needed to identify and predict upcoming issues, such as business and process data.

Snapshot:

US-based predictive maintenance software vendor Predictronics offers PDX DAQ, an application that allows users to synchronize data collection from multiple sources for any given period of time. The solution creates a database that harmonizes all the timestamps from different sensors, which Predictronics claims yields the necessary information for analysis and producing real-time, impactful results.

Feature 2: Analytics and model development

Analytics and model development tools within Predictive Maintenance software analyze, interpret, and communicate data patterns, including analytics discovery (e.g., RCA, AD modules) and modeling (e.g., feature engineering and model selection and testing).

Snapshot:

US-based predictive maintenance software vendor Falkonry (recently acquired by IFS) offers Workbench within its Time Series AI platform, a low-code ML-based solution aiming to help users—specifically, operational practitioners, including production, equipment, or manufacturing engineers—discover patterns such as early warning or stages of deterioration in complex physical systems. It also aims to enable users to analyze large amounts of data and build predictive models.

Feature 3: Pre-trained models

Pre-trained models are just that: ready-to-use models typically designed for specific assets in specific industries. These models include capabilities and references for specific assets or failure modes (e.g., fouling for heat exchangers, wear and corrosion for fans, or valve leakage for compressors). These are meant to help end users see examples of models so they can build on them or develop custom predictive maintenance algorithms.

Snapshot:

US-based asset management software vendor AspenTech (recently acquired by Emerson), offers Mtell, an application that includes pre-populated, industry-specific asset templates to help users select sensors for common asset categories and AI functionality to create and deploy models quickly for PdM applications (e.g., for specific compressors, turbines, and blowers).

Feature 4: Status visualization, alerting, and user feedback

Status visualization, alerting, and user feedback tools within predictive maintenance software automatically communicate asset-related data/insights for various personas. These insights often include status dashboards and automatic alerts that trigger work orders or corrective actions, maintenance planning, and optimization. These tools also enable users to provide feedback concerning the accuracy of alerts.

Snapshot:

US-based analytics software vendor SAS Institute offersAsset Performance Analytics, which includes status dashboards and automatic alerts intended to notify operations staff and managers of impending failure so that organizations have time to identify and fix issues before they become costly problems.

Feature 5: Third-party integration

Third-party integration enables users to connect their predictive maintenance software to other software systems and workflow management tools, such as ERP, MES, CMMS, APM (more on APM integration in Highlight 4), and Field Service.

Snapshot:

SKF, a Swedish bearing and seal manufacturing company also offering maintenance products, offers a condition monitoring and predictive maintenance solution that interfaces with existing plant control systems (e.g., MES or SCADA) and other external dashboards (e.g., ERP). It also provides insights to operators in the field via alarms and visualization on handheld devices.

Feature 6: Prescriptive actions

Prescriptive action features typically suggest the optimal actions to take in case of an (upcoming) failure. These actions are typically prioritized by criteria that are set when the algorithm is designed.

The actions that are prescribed by the software vary depending on the nature and urgency of the issue. They may require multiple steps or interventions. For instance, some actions may involve automatically adjusting the equipment parameters or informing the maintenance and operation teams about the necessary procedures to ensure equipment efficiency.

Snapshot:

Marathon, a predictive maintenance software solution from Norway-based Arundo, provides a feature known as Investigations that aims to provide the workflow and instructions to resolve equipment problems according to prescribed corporate standards.

Highlight 4: Integration into the maintenance workflow is becoming important

In its early days, predictive maintenance was mostly a standalone solution developed by startups to address specific customer needs. However, our report highlights a notable trend of sophisticated predictive maintenance solutions integrating into larger APM and computerized maintenance (CMMS) solutions.

APM is a strategic equipment management approach designed to help optimize the performance and maintenance efficiency of individual assets and entire plants or fleets. APM aims to improve asset efficiency, availability, reliability, maintainability, and overall life cycle value.

Various APM vendors are introducing predictive maintenance software tools within their APM offerings. The solutions aim to tie the different capabilities into 1 thread:

• Knowing when a machine will fail and mapping how failures could affect production or output
• Estimating how much fixing or preventing an issue will cost
• Making recommendations on whether it is worth fixing or preventing a problem

By including a sophisticated predictive maintenance solution in an end-to-end asset flow, APM players are trying to become the main partners for their customers’ digitalization journeys.

Our report lists 9 key components of APM:

1. Asset health monitoring
2. Maintenance optimization
3. Reliability analysis
4. Integrity management
5. Performance optimization
6. Failure prediction <- Predictive maintenance resides here 7. Digital asset twin 8. Sustainability management 9. Energy optimization

We assess in our report that improving the failure prediction module of APM solutions is currently one of the key initiatives of leading APM vendors.

Highlight 5: Successful standalone solutions vendors specialize in an industry or asset

Our research found that 30% of predictive maintenance vendors offer standalone, industry- or asset-specific solutions. By tailoring their efforts to specific niches in which they have acquired domain knowledge, they can discern the types of equipment and industries in which their solutions offer the most end-user benefits.

Snapshot:

Israel-based data science company ShiraTech Knowtion uses its equipment expertise in its offering of Predicto, an industrial IoT platform focused on industrial maintenance teams. The platform enables reading and processing of sensor data from production plants, ideally based on its own multisensing devices (iCOMOX). The company has developed specific offerings for motors, pumps, conveyors, and pipes. These asset-tailored offerings enable the company to scale.

6 considerations for predictive maintenance vendors

Six questions that predictive maintenance vendors should ask themselves based on insights in this article:

1. Market growth and strategy: Given the market’s growth to $5.5 billion and the projected increase to $14.3 billion by 2028, how can our company align its strategy to capitalize on this market expansion?
2. Accuracy improvement: Considering the current lower-than-50% accuracy of many predictive maintenance solutions, what innovative approaches or technologies can we adopt to enhance the accuracy of our predictions?
3. ROI communication: How can we better communicate the positive ROI of predictive maintenance to potential customers, especially those who are skeptical due to past experiences with inaccurate solutions?
4. Industry specialization: Given that the most successful vendors are specialized in specific industries, assets, or use cases, should we consider narrowing our focus, and if so, in which areas?
5. Data collection and integration: Are we effectively collecting the right kinds of data (including business and process data) and integrating it into the right IT systems for optimal predictive maintenance?
6. Software tool features: Do our software tools encompass the 6 common features identified in the report (data collection, analytics and model development, pre-trained models, status visualization, third-party integration, prescriptive actions), and are they competitive in the current market?

8 considerations for those looking to adopt or update predictive maintenance solutions

Eight questions that those looking to adopt or update predictive maintenance solutions should ask themselves based on insights in this article:

1. Solution type suitability: Which type of predictive maintenance solution (indirect failure prediction, anomaly detection, or RUL) best aligns with our specific maintenance needs and operational goals?
2. Integration with existing systems: How easily can predictive maintenance solutions integrate into our existing maintenance workflows and asset management systems?
3. Vendor specialization: Should we look for a vendor specialized in our industry, specific assets, or use cases, and how would that benefit us over a generalist provider?
4. Data collection and analysis: Do we have the necessary infrastructure for effective data collection and analysis to support a predictive maintenance system?
5. Accuracy and trustworthiness: How can we evaluate and ensure the accuracy of the predictive maintenance solution to build trust within our maintenance team?
6. Scalability and future growth: How scalable are the predictive maintenance solutions, and can they accommodate our future growth?
7. Software features and functionality: Do the software tools offered by vendors have all the key features we need, such as data collection, analytics, and third-party integration?
8. Market trends and innovation: Given the evolving nature of the predictive maintenance market, how can we stay informed about the latest innovations and ensure that our solution remains cutting-edge?

The post Predictive maintenance market: 5 highlights for 2024 and beyond appeared first on IoT Business News.

As the Internet of Things (IoT) continues to expand, interoperability within IoT ecosystems has emerged as a critical issue. With an ever-growing number of IoT devices and platforms, ensuring these systems can effectively communicate and work together is paramount. This article delves into the challenges of interoperability in IoT ecosystems and discusses strategies to overcome these hurdles, ensuring seamless integration and functionality.

Understanding Interoperability in IoT

IoT ecosystems are rapidly evolving, encompassing a wide array of devices from home appliances to industrial sensors. As of 2023, the number of connected IoT devices globally is in the billions, a number that is expected to grow exponentially. This growth, while promising, introduces complexity and challenges in maintaining interoperability among diverse systems.

Interoperability in IoT refers to the ability of different IoT systems and devices to communicate, exchange, and interpret shared data with one another, regardless of the manufacturer, model, or operating system. This is vital for creating efficient, scalable, and sustainable IoT ecosystems.

Challenges in Achieving Interoperability

• Diverse Hardware and Standards: IoT devices are produced by numerous manufacturers with different hardware configurations and standards, making interoperability a significant challenge.

• Varied Communication Protocols: IoT devices use a range of communication protocols (like Wi-Fi, Bluetooth, Zigbee, and others), which often lack uniformity, further complicating interoperability.

• Data Format and Semantic Differences: Even when devices can connect, differences in data formats and semantics can hinder effective communication and data exchange.

• Security Concerns: Ensuring secure data exchange between devices while maintaining interoperability is a complex challenge, given the varying security protocols and standards.

Strategies for Ensuring Interoperability

• Adopting Universal Standards and Protocols: Developing and adopting universal standards and protocols is crucial. This includes efforts by organizations like the IEEE, IETF, and ISO to create and promote widely accepted standards.

• Open Platforms and APIs: Encouraging the use of open platforms and Application Programming Interfaces (APIs) allows different devices and systems to communicate more seamlessly.

• Modular Design and Frameworks: Implementing modular designs in IoT devices can facilitate interoperability, as it allows for easier integration of components from different manufacturers.

• Common Data Models and Semantic Frameworks: Establishing common data models and semantic frameworks ensures that data exchanged between devices is understood consistently across different systems.

The Role of Industry Consortia

Industry consortia play a significant role in driving interoperability in IoT. Organizations like the Open Connectivity Foundation (OCF), the Industrial Internet Consortium (IIC), and the Zigbee Alliance work towards creating unified standards and certification programs for IoT devices and systems.

Government and Regulatory Bodies

Government and regulatory bodies are increasingly involved in setting guidelines and regulations to promote interoperability in IoT. This includes setting compliance standards for security and data privacy, as well as encouraging the adoption of universal standards.

The Importance of Testing and Certification

Testing and certification are crucial for ensuring interoperability. This involves rigorous testing of IoT devices and systems to ensure they can operate seamlessly across different ecosystems and comply with established standards.

Case Studies: Success Stories of Interoperability

Several industries have successfully implemented interoperable IoT ecosystems:

• Smart Home Technology: Companies like Apple, Google, and Amazon are working towards interoperable smart home ecosystems, allowing different smart home devices to communicate regardless of the brand.

• Healthcare: Interoperable IoT systems in healthcare have enabled better data sharing across various medical devices, improving patient care and operational efficiency.

• Manufacturing: In the manufacturing sector, interoperable IoT systems have streamlined production processes, allowing different machines and sensors to work in unison.

Future Trends and Developments

Looking ahead, the trend is towards increased standardization and interoperability in IoT. This includes the development of more sophisticated AI and machine learning algorithms to manage and facilitate interoperability across complex IoT ecosystems.

Conclusion

Interoperability remains a key challenge in the expanding world of IoT. However, through collaborative efforts, the adoption of universal standards, and the implementation of robust testing and certification processes, significant strides are being made. As we advance, the focus on interoperability will continue to grow, playing a critical role in the success and sustainability of IoT ecosystems.

The post Interoperability in IoT Ecosystems: Navigating Challenges and Strategies appeared first on IoT Business News.

Imagine you’re the director of the New York Philharmonic, planning a grand symphony orchestra event. With the finest instruments and the best musicians at your disposal, you’re set for an outstanding performance. However, anyone familiar with classical music knows that without a conductor to harmonize the instruments, manage the tempo, and guide the musicians through complex scores, even the most talented ensemble could descend into disarray.

Similarly, in the ever-evolving landscape of the Internet of Things (IoT), managed service platforms are not just facilitators; they are the linchpins that enable organizations to unlock the full potential of IoT solutions. Just like a conductor in an orchestra, a managed service IoT platform harmonizes the diverse components of IoT.

These platforms ensure that every ‘instrument’—from sensors on a factory floor to smart streetlights in a city—plays in perfect harmony. They ‘conduct’ the installation, manage the ‘tempo’ of data flow, and ensure that every ‘note’ of data is secure and precisely tuned. Without this guiding hand, the potential of IoT, akin to an orchestra without a conductor, would struggle to reach its full, melodious potential.

For this reason, managed service IoT platforms are increasingly becoming the backbone of connected device ecosystems. They offer a comprehensive suite of services that support organizations throughout the entire lifecycle of IoT deployment, transforming complexity into simplicity, the unreachable into the accessible, and the overwhelming into the effortlessly manageable. In a world where efficiency and connectivity reign supreme, these platforms are the vital links that seamlessly integrate our digital existence.

Global Connectivity and Installation Support

One of the primary advantages of managed service IoT platforms is their ability to offer global connectivity solutions seamlessly. With the proliferation of IoT devices worldwide, it’s crucial for organizations to maintain a reliable and secure connection across different networks. Managed service platforms typically have agreements with multiple network providers, ensuring resilient access across various regions. With a global SIM and connectivity management platform, companies can simplify the process of connecting devices anywhere in the world, which is particularly beneficial for organizations with international operations.

Installation support is another critical service provided by good managed platforms. Deploying IoT devices can be a complex task, especially when it involves a large number of devices across multiple locations. A worthwhile managed service provider can take the lead on installation, ensuring that devices are correctly set up and integrated into existing systems, which can significantly reduce the burden on internal IT teams.

Hardware Leasing

Another aspect where managed service IoT platforms can add value is through hardware leasing options for things like Satellite Tracking and Broadband Access. This approach allows organizations to avoid the high upfront costs associated with purchasing IoT devices outright. Instead, they can opt for a subscription-based model that includes the leasing of hardware, reducing capital expenditure and providing the flexibility to scale up or down as needed.

Device and Data Management

Effective device and data management are at the heart of any successful IoT strategy. Managed service platforms offer tools and services that allow organizations to monitor and control their IoT devices remotely. This includes updating firmware, managing device configurations, and troubleshooting issues without the need for on-site visits, which can be both time-consuming and costly.

Data management is equally important, as some IoT devices generate vast amounts of data that need to be collected, processed, and analyzed, while others just send a few crucial bytes. Managed service IoT platforms often come with built-in data management and analytics tools, enabling organizations to gain insights into their operations and make data-driven decisions, often saving them both money and time.

Addressing Market Challenges

Like all markets, the IoT market is not without its challenges. From the complexity of managing a diverse array of devices, to security concerns, and the need for specialized skills to handle IoT deployments. Managed service IoT platforms address these challenges by providing a unified solution that simplifies management, enhances security with end-to-end encryption protocols like LWM2M (Lightweight M2M), and offers access to expertise in IoT technology.
For instance, the implementation of the LWM2M protocol in a managed service platform would provide a secure solution by encrypting data from the device to the application. This standard protocol ensures that sensitive information remains protected, addressing one of the key concerns in IoT deployments.

Managed Services Are Pivotal to IoT

In the ever-evolving landscape of Internet of Things (IoT), managed service platforms are not just facilitators; they are the linchpins that enable organizations to unlock the full potential of IoT solutions. By offering a comprehensive suite of services, from global connectivity to intricate device and data management, these platforms are instrumental in overcoming the challenges inherent in IoT deployment.

When exploring managed IoT platforms, it’s crucial to consider those that are pioneering in the integration of protocols like LoRaWAN. Equally important is the recognition of technologies like LoRa Sensors for their unique ability to enable long-range data transmissions with amazing battery life. This capability is a game-changer in IoT, expanding the horizons of data communication and device interoperability.

LoRa’s significance in IoT is not just about its technical prowess but also about the doors it opens for innovative applications. From urban to rural, industrial to consumer spaces, the implications of long-range, low-power IoT solutions are vast and varied. This technology brings a new dimension to IoT, allowing for solutions that were previously inconceivable due to range and power limitations.

As we continue to navigate the complex world of IoT, it’s important to acknowledge the role of managed service platforms and technologies like LoRa in shaping the future of connectivity. They embody the spirit of innovation and progress, driving us towards smarter, more efficient, and more connected solutions. The journey of IoT is one of continuous exploration and growth, and these technologies are at the forefront, leading the way in transforming how we interact with the world around us.

In this dynamic environment, the true power of IoT lies in its ability to adapt, evolve, and integrate technologies that push the boundaries of what’s possible. Managed service IoT platforms and technologies like 5G and LoRa are pivotal in this journey, guiding us towards a future where the potential of IoT is fully realized.

The post Orchestrating the IoT World: The Indispensable Role of Managed Service Platforms appeared first on IoT Business News.

AI will not take all jobs nor end civilization. But it will help businesses make better decisions.

Artificial intelligence (AI) is everywhere. And stories are rampant about its promise and its threat. Will AI’s potential be realized in the year ahead?

SAS, the leader in AI and analytics, asked executives and experts across the company to predict trends and key business and technology developments in AI for 2024.

Below are some of the predictions they shared.

Generative AI will augment (not replace) a comprehensive AI strategy

“Generative AI technology does a lot of things, but it can’t do everything. In 2024, organizations will pivot from viewing generative AI as a stand-alone technology to integrating it as a complement to industry-specific AI strategies. In banking, simulated data for stress testing and scenario analysis will help predict risks and prevent losses. In health care, that means the generation of individualized treatment plans. In manufacturing, generative AI can simulate production to identify improvements in quality, reliability, maintenance, energy efficiency and yield.” – Bryan Harris, Chief Technology Officer, SAS
[Note: Earlier this year, SAS committed $1 billion to AI-powered industry solutions.]

AI will create jobs

“In 2023, there was a lot of worry about the jobs that AI might eliminate. The conversation in 2024 will focus instead on the jobs AI will create. An obvious example is prompt engineering, which links a model’s potential with its real-world application. AI helps workers at all skill levels and roles to be more effective and efficient. And while new AI technologies in 2024 and beyond may cause some short-term disruptions in the job market, they will spark many new jobs and new roles that will help drive economic growth.” – Udo Sglavo, Vice President of Advanced Analytics, SAS

AI will enhance responsible marketing

“As marketers we must consciously practice responsible marketing. Facets of this are awareness of the fallibility of AI and alertness to possible bias creeping in. While AI offers the promise of enhanced marketing and advertising programs, we know that biased data and models beget biased results. In SAS Marketing, we are implementing model cards that are like an ingredient list, but for AI. Whether you create or apply AI, you are responsible for its impact. That’s why all marketers, regardless of technical know-how, can review the model cards, validate that their algorithms are effective and fair, and adjust as needed.” – Jennifer Chase, Chief Marketing Officer, SAS

Financial firms will embrace AI amid a Dark Age of Fraud

“Even as consumers signal increased fraud vigilance, generative AI and deepfake technology are helping fraudsters hone their multitrillion-dollar craft. Phishing messages are more polished. Imitation websites look stunningly legitimate. A crook can clone a voice with a few seconds of audio using simple online tools. We are entering the Dark Age of Fraud, where banks and credit unions will scramble to make up for lost time in AI adoption – incentivized, no doubt, by regulatory shifts forcing financial firms to assume greater liability for soaring APP [authorized push payment] scams and other frauds.” – Stu Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions, SAS

Shadow AI will challenge CIOs

“CIOs have struggled with ‘shadow IT’ in the past and will now confront ‘shadow AI’ – solutions used by or developed within an organization without official sanction or monitoring by IT. Well-intentioned employees will continue to use generative AI tools to increase productivity. And CIOs will wrestle daily with how much to embrace these generative AI tools and what guardrails should be put in place to safeguard their organizations from associated risks.” – Jay Upchurch, Chief Information Officer, SAS

Multimodal AI and AI simulation will reach new frontiers

“The integration of text, images and audio into a single model is the next frontier of generative AI. Known as multimodal AI, it can process a diverse range of inputs simultaneously, enabling more context-aware applications for effective decision making. An example of this will be the generation of 3D objects, environments and spatial data. This will have applications in augmented reality [AR], virtual reality [VR], and the simulation of complex physical systems such as digital twins.” – Marinela Profi, AI/Generative AI Strategy Advisor, SAS

Digital-twin adoption will accelerate

“Technologies like AI and IoT [Internet of Things] analytics drive important sectors of the economy, including manufacturing, energy and government. Workers on the factory floor and in the executive suite use these technologies to transform huge volumes of data into better, faster decisions. In 2024, the adoption of AI and IoT analytics will accelerate through broader use of digital-twin technologies, which analyze real-time sensor and operational data and create duplicates of complex systems like factories, smart cities and energy grids. With digital twins, organizations can optimize operations, improve product quality, enhance safety, increase reliability and reduce emissions.” – Jason Mann, Vice President of IoT, SAS

Insurers will confront climate risk, aided by AI

“After decades of anticipation, climate change has transformed from speculative menace to genuine threat. Global insured losses from natural disasters surpassed $130 billion in 2022, and insurers worldwide are feeling the squeeze. US insurers, for example, are under scrutiny for raising premiums and withdrawing from hard-hit states like California and Florida, leaving tens of millions of consumers in the lurch. To survive this crisis, insurers will increasingly adopt AI to tap the potential of their immense data stores to shore up liquidity and be competitive. Beyond the gains they realize in dynamic premium pricing and risk assessment, AI will help them automate and enhance claims processing, fraud detection, customer service and more.” – Troy Haines, Senior Vice President of Risk Research and Quantitative Solutions, SAS

AI importance will grow in government

“The workforce implications of AI will start being felt in government. Governments have a hard time attracting and retaining AI talent since experts command such high salaries, however, they will aggressively recruit for expertise to support regulatory actions. And like enterprises, governments will also increasingly turn to AI and analytics to boost productivity, automate menial tasks and mitigate that talent shortage.” – Reggie Townsend, Vice President of the SAS Data Ethics Practice

Generative AI will bolster patient care

“To advance health and improve patient and member experiences, organizations will further develop generative AI-powered tools in 2024 for personalized medicine, such as the creation of patient-specific avatars for use in clinical trials and the generation of individualized treatment plans. Additionally, we will see the emergence of generative AI-based systems for clinical decision support, delivering real-time guidance to payers, providers and pharmaceutical organizations.” – Steve Kearney, Global Medical Director, SAS

Deliberate AI deployment will make or break insurers

“In 2024, one of the top 100 global insurers will go out of business as a consequence of deploying generative AI too quickly. Right now, insurers are rolling out autonomous systems at breakneck speed with no tailoring to their business models. They’re hoping that using AI to crunch through claims quickly will offset the last few years of poor business results. But after 2023’s layoffs, remaining staff will be spread too thin to enact the necessary oversight to deploy AI ethically and at scale. The myth of AI as a cure-all will trigger tens of thousands of faulty business decisions that will lead to a corporate collapse, which may irreparably damage consumer and regulator trust.” – Franklin Manchester, Global Insurance Strategic Advisor, SAS

Public health will get an AI boost from academia

“Public health is achieving technologic modernization at an unprecedented rate. Whether overdoses or flu surveillance, using data to anticipate public health interventions is essential. Forecasting and modeling are rapidly becoming the cornerstone of public health work, but government needs help. Enter academia. We will see an increase in academic researchers carrying out AI-driven modeling and forecasting on behalf of government. It is clear after COVID-19 that the protection of our population will require exceptional technology and collaboration.” – Dr. Meghan Schaeffer, National Public Health Advisor and Epidemiologist, SAS

The post AI reality vs. myth: Twelve predictions from SAS for 2024 appeared first on IoT Business News.

IoT Analytics released a new analysis, based on the ’Industrial IoT & Industry 4.0 Case Study Report 2023’.

Key insights:

• Digitalization has become essential for industrial companies worldwide, as IoT Analytics expects the industrial IoT market to reach $145 billion in 2023.
• The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves into 22 case studies, exploring their project objectives, technologies deployed, lessons learned, challenges, and outcomes.
• In this article, we share five learnings from the report: 1) Upgrading the ERP is often the first step in digital transformation, 2) cloud-native data storage and streaming are coming up, 3) first successful implementations of private 5G use cases, 4) digitalization becoming a prerequisite to achieving sustainability, and 5) the continued journey toward predictive maintenance.

Key quotes:

Rajini Nair, Analyst at IoT Analytics, remarks: “Upon analyzing the case studies outlined in the report, it becomes evident that digitalization is a key catalyst driving technological progress in industrial settings. This includes the enhancement of ERP systems and the migration of data to cloud platforms equipped with real-time streaming capabilities. Furthermore, digitalization proves essential in harmonizing companies with their sustainability objectives. Strategies such as the implementation of predictive maintenance algorithms and the adoption of private industrial 5G use cases are leveraged to improve operational efficiency. Looking forward, the technological landscape is transforming with the rise of AI, poised to shape the future of manufacturing.”

Digitalization has become crucial to manufacturers globally

Digitalization has become crucial for manufacturers in their respective competitive landscapes. IoT Analytics estimates the industrial IoT market to reach $145B in 2023, with a CAGR of 17.9% until 2030, as more and more companies undertake digital transformation initiatives.

For many, digitalization has already become a game changer:

• Carmaker Mercedes has achieved 25% greater efficiency in its S-class assembly after optimizing the value chain and introducing innovative technologies at its Germany-based Factory 56 facility.
• Energy giant TotalEnergies aims to generate $1.5 billion annually in savings with its digital solutions by 2025.
• Chemical company Covestro increased efficiency and reduced unnecessary downtime by shifting from calendar-based to condition-based maintenance.

These are just some of the benefits of the digital transformation initiatives our research uncovered as part of the 255-page Industrial IoT and Industry 4.0 Case Studies Report 2023. The report delves into 22 recent industrial digital transformation case studies, looking at initiatives related to digital transformation, data architecture, predictive maintenance, AI, and industrial 5G.

Benefits of case studies for digitalization journeys

Case studies by peers or companies in other industries are a great way to learn about digitalization, identify common challenges, develop a view of best practices, and understand how companies manage to scale.

The 22 case studies in our report offer readers a diverse set of manufacturing examples of current IIoT and Industry 4.0 projects, along with project objectives and takeaways from each. Our analysis of these takeaways yielded many trends in these companies’ digital transformation journeys, five of which we will delve into in this article (including selected highlights from the report):

1. ERP: An upgraded ERP is often the first step to digital transformation.
2. Cloud: Cloud-native data storage and streaming are increasingly accepted.
3. 5G: First manufacturers have successfully implemented private 5G use cases.
4. Sustainability: Digitalization is becoming a prerequisite to achieving sustainability objectives.
5. Maintenance: The journey toward predictive maintenance and remote monitoring continues.

Learning 1: An upgraded ERP is often the first step to digital transformation

Our analysis found that many manufacturers elect to prioritize upgrading their ERP systems to ensure their various data sources are connected before prioritizing other digital transformation initiatives. Since ERP systems are often the central nervous system of a business, prioritizing an updated ERP allows different departments to share and operate on the same data, reducing errors and improving efficiency.

Selected highlight: Celanese

In recent years, US-based chemical manufacturer Celanese has acquired several businesses or divisions from other companies. Celanese had been operating on a legacy SAP ERP system, and the acquired assets had different legacy ERP environments, making cross-section integration difficult. Celanese’s global CIO, Sameer Purao, did not want the company to invest in integrating the new acquisitions into older technology. Given this situation and Celanese’s adoption of a strategic, long-term approach to a scalable digital transformation plan, an ERP upgrade became necessary.

“Our previous ERP system had been a backbone, but it was close to 20 years old. Given its age, we didn’t want to invest in transforming until we upgraded that piece first. The acquisitions underscored that it wouldn’t make sense to invest in integrating them into older technology, so we opted to upgrade.” – Sameer Purao, senior vice president and global CIO, Celanese Corporation

In May 2023, Celanese announced that it had completed its upgrade to SAP S/4HANA. Further, since Celanese acquired DuPont’s Mobility & Materials (M&M) business just six months prior, it quickly cutover to upgrading M&M’s legacy ERP system as well, which is expected to be completed in the first half of 2024.

While there are other major aspects of Celanese’s digital transformation journey detailed in our report, this upgrade to its ERP provided a stronger backbone from which other digitalization solutions could be built. Another benefit of this upgrade is improved visibility and collaboration, enhancing transparency and teamwork and allowing for efficient data access across the enterprise.

Learning 2: Cloud-native data storage and streaming are increasingly accepted

The cloud market nearly doubled between 2020 and 2022, growing from $109 billion to $206 billion, based on our analysis of global cloud projects. While the COVID-19 pandemic certainly played a major role, it was not the only growth factor. Our analysis found that large-scale enterprise digitalization efforts and strong SaaS adoption also helped fuel this growth.

Cloud storage and data streaming allow companies to centralize and share their data with a smaller footprint than running their own on-premises servers, which comes with footprint and maintenance costs. Moving these services to the cloud also allows companies to scale without the need for significant capital investment in physical hardware.

Selected highlight: Michelin

In 2019, tire manufacturer Michelin started using Apache’s Kafka event streaming platform on-premises in its data centers to gain real-time insights and process data as continuous streams. However, as its operational footprint expanded, so did the resources it had to dedicate to maintaining the solution. By Q4 2019, Michelin’s IT department initiated its migration to the cloud, with Microsoft Azure as the cloud partner.

“One of the challenges with [streaming technology] Kafka was its operational complexity, especially as the footprint expanded across our organization. It’s a complex, distributed system, so we had to allocate a lot of our valuable technical resources and expertise to babysit it and keep it running.” – – Olivier Jauze, now CTO of Experiences Business Line, Michelin

By 2021, Michelin migrated its services to Confluent Cloud for Azure, a Kafka-based platform, to support its multi-cloud environment. Soon after, the company began exploring use case projects and has since migrated one of its most critical projects, online order management, to the cloud—replacing its on-premises orchestrator. By 2023, Michelin expanded its cloud-based event streaming architecture into several departments, including supply chain management, customer services, manufacturing, and R&D.

Through its adoption of cloud-native data storage and streaming, Michelin achieved the following benefits (among other things):

• Cost savings: Estimated 35% in cost savings in the cloud compared to on-premises operations
• Improved uptime: 99.99% uptime

Learning 3: First manufacturers have successfully implemented private 5G use cases

As 5G continues its public rollout globally, some manufacturers have successfully deployed private 5G networks to enable new use cases within their facilities. While faster speeds and lower latency may seem like key adoption drivers, our analysis found that improved reliability over Wi-Fi, enhanced cybersecurity, and the ability to access data locally are the core motivating factors.

Our analysis also found that during the public rollout of 5G, some companies did not simply dive into integrating 5G-specific technology. Instead, many integrated robust LTE solutions that were upgradable to 5G with relative ease (or so-called 4.9G solutions) once the technology evolved or became approved for industrial use.

Selected highlight: Airbus

To increase aircraft production and validation efficiency, European multinational aerospace corporation Airbus partnered with Ericsson, a Swedish multinational telecommunications company, in 2021 to implement private industrial 5G networks at 11 aircraft assembly manufacturing sites in Europe. The approach began with implementing 4G networks that either already had 5G capabilities or could seamlessly upgrade to 5G.

However, Airbus is not limiting this deployment to its European facilities. During a Q&A at the 5G Manufacturing Forum in November 2022, Hakim Achouri, the 5G and IoT solutions expert for digital aviation at Airbus, noted, “Airbus is going way beyond 11 networks at 11 sites, expanding beyond its core European manufacturing bases in France and Germany, to also deploy private 5G in Canada, China, Spain, the UK, and the US.”

With its implementation of private 5G networks at its production and assembly facilities, Airbus has realized the following benefits:

• Ability to implement advanced use cases: This includes site surveillance, efficient flight-to-ground data offloads, quality inspections, and the operation of automated guided vehicles (AGVs).
• Enhanced user experience: With increased speed, bandwidth, and reliability, employees at the production sites have access to more data, making operations smoother, more efficient, and more secure.
• Scalability through reusability: By developing a pattern in its strategy, Airbus was able to roll out private 4G/5G networks across its many sites with consistent quality and performance.

Learning 4: Digitalization is becoming a prerequisite to achieving sustainability objectives

We recently noted a trend of companies deploying digital twins to help realize their sustainability goals. But it is not simply digital twins assisting companies on this front—digitalization projects overall are helping companies monitor energy consumption, optimize resource usage, and reduce their environmental footprint in the manufacturing process.
Backing this awareness and trend toward sustainability are data points from our latest What CEOs Talked About report, where “sustainability” and related terms remained among the most discussed topics in boardrooms.

Selected highlight: TotalEnergies

French multinational energy and petroleum company TotalEnergies has publicly declared its ambition to achieve carbon neutrality by 2050. To meet this goal, the energy company has leveraged digital solutions to advance the implementation of sustainability measures on its offshore platforms.

For instance, TotalEnergies retrofitted their pipes with LoRaWAN-connected temperature sensors to detect gas leaks along their flare networks. As hydrocarbons are released, the temperature of the pipes significantly changes. When this change is detected, operators are alerted via emails for immediate action. This not only helps limit the release of hydrocarbons but also saves TotalEnergies money by reducing the loss of product.

Learning 5: The journey toward predictive maintenance and remote monitoring continues

According to our Predictive Maintenance and Asset Performance Market Report 2023–2028 (published in November 2023), the predictive maintenance market reached $5.5 billion in 2022. While the report notes several tailwinds supporting this interest and market growth, such as skill shortages and interest in reducing energy usage and CO2 emissions, costs are a major driver, as noted in our case studies report as well.

Equipment failure, especially during core operational hours, reduces productivity and adds repair expenses. To avoid these costs, companies often use preventative maintenance procedures, such as time-based inspections and repairs or condition criteria from sensors or physical measurements to trigger preventative intervention. However, intervening based on time can be inefficient since the equipment may not be in need of repair at that time, and data collection/monitoring requires personnel to conduct these tasks.

By implementing digital solutions, companies can remotely monitor the condition of critical equipment and establish conditions in which intervention is actually needed well before failure occurs.

Selected highlight: Battalion Oil Corp

US-based Battalion Oil Corp partnered with Novity, a US-based predictive maintenance solutions company, to pilot a predictive maintenance solution to detect valve leaks within their compressors and reduce unexpected compressor downtime. Initially, Battalion would sporadically measure valve cap temperatures using handheld devices to identify potential gradual leaks that could lead to a failure. While the checks were intended to be conducted daily, varying daily maintenance tasks and priorities often disrupted these important checks.

“Predictive automation is a game-changer for the oil and gas industry. By analyzing data in real-time and making accurate predictions about future events, drilling companies can optimize their operations to maximize efficiency, reduce costs, and improve safety. This technology has the potential to transform the way we do business and stay competitive in today’s market.” – John Smith, CEO of Oil and Gas Exploration Company

An initial step in the solution was to use a crank angle sensor and pressure transducers. However, physical crank angle sensors are usually the most difficult and expensive sensors to install, so the engineers developed a virtual crank angle sensor based on physics-based and data-driven methods using data from the pressure sensors.

After validating that the rotational position calculated by the virtual sensors matched the position provided by the physical sensors, engineers applied prognostic methods to the data from the virtual crank angle sensor and physical pressure sensors. The result was predicted gradual valve failures several weeks in advance—five to seven days on average before temperature checks indicated a gradual leak.

The digital transformation journey carries on

The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves further into the above-mentioned and 18 other case studies of ongoing digital transformation projections. While these companies and many others are advancing in their digital transformation journeys, there is still a long road ahead for many companies, some of which still rely on analog, pen-and-paper methods in their facilities. Even still, many companies are already experiencing real value, e.g., Mercedes’ achieving 25% greater efficiency and Battalion observing signs of gradual valve failures several weeks in advance.

Digitalization has become more than a nice-to-have for manufacturers today—it has become crucial for them in their respective competitive landscapes. The market reflects this assessment: according to our enterprise IoT market dashboard, the IIoT market size in 2023 is approximately $145 billion, with a forecasted CAGR of 17.9% between 2023 and 2030.

Looking ahead, AI continues to become a major theme in companies’ digital transformation initiatives. According to our continual series What CEOs Talked About, the topic and its related terms have already been of high and growing interest in boardrooms throughout 2023. We see a plethora of generative AI projects across the board, even in the industrial space (which we will report on soon). We will continue to monitor this space and highlight interesting case studies from adopters.

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IoT Business News: Can you explain how the transition from ‘scan it’ to ‘sense it’ technologies will impact global retailers and supply chains? How will these changes impact end-users/ consumers?

Steve Statler: For global retailers and their employees, the transition from ‘scan it’ to ‘sense it’ will save precious resources. The traditional ‘scanning’ method retailers have used is implementing handheld scanners. This takes up employee time, a scarce resource that could be better spent elsewhere. Additionally, manual scanning is inherently inaccurate given its reliance on workers – if workers are not performing perfectly, which is typically the case in the real world, these results break down – and upon fixed infrastructure, which limits information to one moment or location. By using ambient IoT, radios don’t require expensive people or expensive readers. Ambient sensors are already pervasive and visibility is comprehensive across a number of buildings and delivery vehicles, meaning it has a low barrier of entry for global retailers.

What role will the ambient IoT play in this transition?

For the transition to a ‘sense it’ culture, the ambient IoT is essential. The technology builds on the learnings and automations used in 1st generation UHF RFID, adding cloud intelligence and utilizing commodity radios that are already pervasive. The pervasive nature of the UHF RFID, in combination with the ever emerging standards, will allow for a smooth transition.

How can businesses successfully transition their business from manual inventory scanning to sensor technology?

For businesses to successfully transition their business, it will require adoption of battery free Bluetooth tags, readers, and cloud applications that are designed and built for a serialized, digital product passport. A benefit of the transition is that there is hardly any transition labor for employees, as they do not need to be retrained for scanning.

How does ambient IoT improve upon traditional methods of supply chain management?

Ambient IoT improves upon traditional methods of supply chain management in a number of ways. The technology enables a transition to real-time visibility that is end to end across all spaces, including items in transit. This alone is an improvement given that traditional ‘scanning’ methods of supply chain management lack the opportunity to provide real time information. Ambient IoT allows for much higher accuracy. Simultaneously, ambient IoT allows for sensing of additional conditions, such as humidity levels.

What are the larger implications for business owners when implementing ambient IoT?

For business owners, improvements in visibility from ambient IoT allow for optimized business models. The technology allows for lower labor costs, given the lack of employees scanning and less capital tied up in inventory. Additionally, the technology allows for better intelligence and insights, meaning less out-of-stocks, better service levels, and higher sales. Finally, the product can allow for smaller store footprints, as it limits the necessity to order high stock of items.

In addition to these labor and inventory benefits, businesses that have already implemented the technology can capitalize upon the opportunity to transition to product as a service as well. Finally, for all companies, the tech allows for a better ability to comply with upcoming regulations that require visibility across supply chains for customers.

Are there any other use cases for ambient IoT within supply chain management?

As we transition from supply chains to demand chains, the ambient IoT will show, in real time, demand signals from stores or even homes. As sustainability becomes top of mind for many companies, the tech allows for circular product usage, meaning businesses can reuse and resell products in the aftermarket. With better tracking and a source of authentication and provenance for resellers, resellers can sell second hand products for higher costs with proof of validity, and producers can participate in the revenue from resale.

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A new report from IoT Analytics highlights eight notable trends helping to advance and promote digital twins.

Four of these trends are discussed in detail in this article. These trends are shaping the future of the digital twin market and influencing investment priorities for companies across various industries.

Key insights:

• According to our Digital Twin Market Report 2023–2027, the digital twin market is expanding, with a projected CAGR of 30% between 2023 and 2027.
• 29% of global manufacturing companies have either fully or partially implemented their digital twin strategies. Further, job posts related to digital twins have increased by 11% compared to October 2021, while openings for other tech topics have declined in the same timeframe.
• The report notes eight notable trends helping to advance and promote digital twins, four of which are discussed in this article.

Digital twin market snapshot

Data from our Digital Twin Market Report 2023–2027 indicates that 29% of global manufacturing companies have either fully or partially implemented their digital twin strategies, marking a noticeable increase from 20% in 2020. Moreover, the proportion of these companies not contemplating the implementation of digital twins appears to have reduced to 9% in 2023, down from 33.6% in 2020.

Supporting this trend, an analysis of job postings on SimplyHired suggests a growing demand for digital twin expertise. Postings mentioning “digital twin” increased by 11% in October 2023 compared to just two years prior, while openings for most other tech topics declined in the same timeframe. Notably, among the 60 tech-related skill sets we tracked, digital twin-related skills experienced one of the most significant growths.

Based on these observations and additional insights from our report, our current projection estimates that the global digital twin market could grow at a CAGR of approximately 30% between 2023 and 2027.

Against the backdrop of this growing market, the report notes eight trends that help promote the advancement and adoption of digital twins. Here, we will focus on four:

1. Digital twins deployed to meet sustainability goals
2. Digital twins employed as virtual sensors in complex conditions
3. Partnerships between cloud hyperscalers and OT and simulation specialists
4. Initiatives promoting interoperability of digital twins across multifaceted systems

Before we dive into these trends, and to provide context, it is helpful to understand how IoT Analytics defines digital twins and the digital twin market.

Defining digital twins and the digital twin market

In our March 2023 Decoding Digital Twins article, we define digital twins as a virtual model replicating the behavior of an existing or a potential real-world asset, system, or multiple systems. This definition captures a macro concept of digital twins, but our report and previous article present a three-dimensional, cuboid model to help classify digital twins so apple-to-apple comparisons can be made. Each axis of the cuboid represents one dimension of a digital twin:

1. Life cycle phase: The X-axis represents the six life cycle phases a digital twin is used for, from design to decommissioning.
2. Hierarchical levels: The Y-axis represents a digital twin’s five hierarchical levels, from information to multi-system.
3. Use/purpose of implementation: The Z-axis represents the seven most common uses for digital twins, such as simulation and prediction.

Based on this model, there are 210 potential combinations (5 x 6 x 7 = 210); however, our research indicates that many digital twin initiatives are tailored to multiple combinations.

In terms of the digital twin market, our market model only considers software spending for digital twins, which we break into two scopes:

• Broad digital twin market – includes the revenue for all software solutions that provide capabilities that are used for digital twins, such as solutions that integrate data sources into a digital twin and are capable of simulation, visualization, and predictions
• Narrow digital twin market – only considers spending for digital twin-specific software, such as software intended to model the digital twin data

Though hardware and services related to digital twins can bolster the capabilities of digital twin software, we only view them as supporting services for the software, and we do not consider them in our digital twin market model. That said, this ability to bolster capabilities means they can aid digital twin market growth, as shown in some of the trends below.

Digital twin market trends

We will now look at four trends helping digital twin market growth.

Trend 1: Digital twins deployed to meet sustainability goals

Sustainability was discussed in approximately 21% of recent CEO earnings calls and has remained a consistent topic throughout 2023. This pairs with our report’s analysis that the pursuit of sustainability goals is a tailwind macro factor for the digital twin market.

To achieve their sustainability goals, many companies are exploring digital twins. Due to their ability to simulate real-world conditions and deliver real-time information, organizations can optimize resource usage, reducing carbon emissions and improving supply and transportation networks.

According to Capgemini Research Institute’s 2022 digital twins report, 57% of organizations agreed that one of the key drivers for their digital twin investments was improving their sustainability, and 51% agreed that digital twins would help achieve their organization’s environmental sustainability goals.

In our report, a former VP at an industrial automation vendor shared, “Digital solutions provide the visibility, analysis, and insight needed to address the challenges inherent in sustainability goals. A digital twin strategy as part of an overall digitalization plan can be a crucial capability for asset-intensive industries and needs to encompass the entire asset lifecycle, process, and value chain from design and operations through maintenance and strategic business planning.”

Example: Aden, a Chinese integrated facility service provider, created a digital twin for one of its commercial centers in Chengdu, China. The digital twin was designed to help facility managers inspect, maintain, and repair building assets. 3D simulations assist facility managers in visualizing, predicting, and optimizing energy consumption, and the expected benefits include lower annual energy consumption, water usage, and waste.

Trend 2: Digital twins employed as virtual sensors in complex conditions

Virtual sensors approximate data that otherwise cannot be obtained via physical sensors, often due to physical sensors being impractical, costly, or hazardous to employ. Companies are now building digital twins to model hardware and calculate data based on other conditions. This not only allows the collection of data from complex equipment but also enables operators to track equipment performance and predict maintenance and downtimes.

Example: In large motor applications, such as room-sized motors that pump high volumes of gas, oil, or other chemicals, operators need to monitor the motors’ temperatures—especially if the motors are high-powered and repeatedly started. If a motor is too hot during a restart, the components could cause serious damage. Unfortunately, using a direct, physical sensor within a motor is often impractical, and operators must work from an assumptive time it takes for the motor to cool (with an additional safety buffer time). This means motors can be down longer than necessary, impacting efficiency and revenue.

To address this issue, Siemens developed a prototype virtual sensor based on a digital twin. It offers a simulation of how a physical sensor would operate if it were possible to install it within a motor. Using AR headsets, operators can see a simulation of the motor and its interior with a demonstrator superimposed over it, and they can see the motor’s temperature.

Trend 3: Partnerships forged in the clouds: Hyperscalers team up with OT and simulation specialists

In the last few years, cloud hyperscalers like AWS and Microsoft Azure have introduced digital twin platforms, e.g., AWS IoT TwinMaker and Azure Digital Twins, that allow for interconnecting various data sources and building digital twin topology. However, these companies realize they cannot deliver end-to-end digital twin solutions by themselves and are thus partnering with OT and simulation companies to add capabilities to their networks.

Hyperscalers and OT companies

OT companies provide industrial data management capabilities and connections to millions of local physical assets. By partnering with hyperscalers, OT companies can reach broader audiences looking for digital transformation solutions by offering typical cloud benefits like storage and computation power.

Example: In 2021, Siemens and AWS announced an expansion of their partnership, with digital twin technology being an area of focus. The companies aimed to accelerate Siemens Xcelerator adoption and democratize new digital twin solutions using AWS IoT TwinMaker, a service intended to make creating digital twins that incorporate multiple data sources faster and easier.

Hyperscalers and simulation companies

Simulation companies bring specialized expertise in creating accurate, high-fidelity models of physical entities, be it machinery, buildings, or entire ecosystems. By partnering with a hyperscaler, simulation companies can harness these cloud services’ data storage and computational power to create more robust, responsive, and accurate data twins that can be scaled and integrated seamlessly into broader IT ecosystems.

Example: In 2023, Ansys and Microsoft announced a partnership to help customers envision digital twins on a large scale. In cooperation with Tata Consultancy Services, Ansys and Microsoft integrated Ansys’s Twin Builder’s physics-based simulation capabilities with Azure Digital Twin and IoT data to allow Twin Builder users to run scenarios and obtain predictions on how their systems will behave.

Trend 4: Initiatives promoting interoperability of digital twins across systems from different vendors

Agreement on integration standards among the various digital twin technology providers is crucial for building cross-system digital twins. With standards, manufacturers can offer services that can be applied to unique situations with other digital twin technology and software.

Recognizing this, countries and industry organizations have taken several steps to address this need:

Plattform Industrie 4.0 and CESMII

Plattform Industrie 4.0 and the Clean Energy Smart Manufacturing Innovation Institute (CESMII) partnered to establish standards for manufacturers in Germany and the United States to better enable smart, sustainable competition. The partnership addressed similar challenges related to Industry 4.0 and Smart Manufacturing.

DTC digital twin interoperability framework

The Digital Twin Consortium (DTC), a collaborative partnership of industry, academia, and government experts, released the Digital Twin System Interoperability Framework to “unify a nascent ecosystem of high-value, multi-vendor services that can seamlessly ‘plug into’ a multi-dimensional, interoperable system of systems.”

DTC and IDTA collaboration

DTC and the Industrial Digital Twin Association (IDTA) announced a liaison agreement to collaborate on standardization requirements, enabling interoperability through discussions, aligning work in horizontal domains, and collaborating on open-source projects, contributions, and reference implementations.

DTC and OPC Foundation collaboration

DTC and the OPC Foundation announced a liaison agreement to “accelerate the development and adoption of digital twin-enabling technologies,” promoting interoperability standards and processes to advance the use of digital twins in manufacturing across multiple industries.

Considerations for digital twin vendors

3 questions digital twin vendors should ask themselves based on research findings discussed in this article:

1. Sustainability goals: Given the emphasis on sustainability and the role of digital twins in achieving it, is my company’s digital twin solution well-equipped to support sustainability objectives? Are there new features we need to develop?
2. Interoperability: Is our digital twin solution currently designed to be interoperable with other systems and technologies, especially those from different vendors?
3. Product development: How aligned is our product development roadmap with the emerging trends highlighted in the report, especially around end-user and technological advancements?

Considerations for adopters

3 questions adopters should ask themselves based on research findings discussed in this article:

1. Infrastructure readiness: Do we have the necessary infrastructure and resources in place to support the deployment and effective use of digital twins? If not, what investments are required?
2. Data integration: How compatible are our current data sources and systems with digital twin technologies, especially in terms of sensor data (both physical and virtual/soft)?
3. Interoperability concerns: As we use multiple software solutions across our operations, how important is interoperability between digital twin technologies and our existing systems?

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Since the founding of our company, SAM has welcomed efforts by government agencies and regulators worldwide to raise consumer awareness about cybersecurity in the IoT space. These efforts benefit both consumers and the network operators connecting them to the digital world. Consumers benefit by being better informed about an IoT product’s security attributes at the “point of sale” and operators benefit as this increased awareness amongst consumers will make it easier to develop and sell new network-based security services.

The latest development comes from the United States, where the White House has introduced the “Cyber Trust Mark” program. This program aims to certify IoT devices bearing the label, ensuring they meet essential security attributes safeguarding consumers’ networks and device data. While voluntary, this initiative, led by the Federal Communications Commission, is set to begin implementation in 2024. This is part of an initiative that includes a collaboration between the White House and the National Institute of Standards and Technology (NIST) to establish cybersecurity standards tailored to routers.

These moves will have a positive impact on the IoT ecosystem on a variety of levels. Yet, while product labels will increase consumer awareness and education, they cannot address the ongoing evolution and fragmentation of IoT devices. Thousands seemingly hit the market each year, making “constant” security unattainable. Even a seemingly secure device could falter over time without proper software updates, which in reality, the average consumer does not do.

This fact is part of a trend that has led to a situation where most home and small business devices and networks lack adequate protection. This vulnerability arises due to various reasons, including the widespread use of consumer electronics devices that have become connected IoT devices through home routers. While some vulnerabilities may only be an inconvenience for some users, other can open the door to malicious activities. One of the most pressing challenges in the realm of IoT is the sluggish discovery-to-patching process by firmware vendors, leaving users exposed indefinitely. This issue highlights a critical gap in home security, where the timely resolution of IoT vulnerabilities should be a requirement, not a “luxury.”

However, for consumer electronics in general, it takes time to create a fix, to test it in the field and then to distribute it. And for IoT devices, it’s a different matter altogether, as numerous devices have minimal security and no ongoing security patch program. Or the devices are no longer on the market at all. This condition creates a significant window of opportunity for hackers who are well aware of these vulnerabilities and often have ample time to exploit them before the vendors issue a remedy, leaving end users vulnerable to attacks. Even when the patch is ready for deployment, there is still the question of how it will be deployed onto the users’ devices. Some devices can be updated via the corresponding app on the smartphone. Others, however, need to be updated manually – a lengthy and quite complicated process for even those who are tech savvy.

Katherine Gronberg, Head of Government Services at NightDragon, who works frequently with NIST and the White House on matters relating to IoT security, has commented: “With the explosion of IoT devices available from a wide variety source, consumers have until now not had any help in deciding what to buy or even to be mindful of security. The Cyber Trust Mark will allow consumers to identify products that have been designed and manufactured according to secure development guidelines and that offer some basic security features, most of which will likely not require any actions by the device user. While this program doesn’t apply to IoT devices that are already in use today, it will create a more informed customer and may make other parties in the ecosystem such as retailers or ISPs more conscious of the problem and might motivate them to take action.”

One action that the industry has seen recently is a renewed focus on routers, as seen in a recent security advisory issued by the US NSA, in which one of its recommendations was for consumers to exchange ISP-issued routers for ones they would purchase themselves. And there is another router-focused technique that more and more ISPs are using to help their customers with IoT network security, namely the “hot patching” measure, which uses a router-based software agent to provide protection for the router itself and every device connected to it.

Hot patching is designed as a “one stop” protection program in which an ISP would download an agent to a router to provide constant real-time monitoring and alerts. Hot patching is based on what is known as “deep packet inspection,” or DPI, which is a well-known and long-standing technique wherein the payload of packets traversing a data network is inspected and analyzed. The result empowers consumers with comprehensive router and device security, eliminating vulnerability monitoring and patching complexities.

While security labeling undoubtedly enhances consumer awareness and overall IoT security, the quest for constant security calls for a gateway-based solution. Such a solution can act as the ultimate backstop to industry and government initiatives, securing IoT devices and the connecting network.

Therefore, we believe the “Cyber Trust Mark” program will certainly be a great benefit for the consumer or “end user” and the increased awareness about IoT security it will raise gives ISPs an excellent opportunity to play a more proactive role that will be welcomed by their customers and which will increase IoT network security in meaningful ways.

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The realms of Quantum Computing and the Internet of Things (IoT) represent two of the most cutting-edge technologies in the modern era. Quantum Computing, with its potential to process complex data at unprecedented speeds, stands at the forefront of a computational revolution. Meanwhile, IoT, which encompasses a network of interconnected devices, is redefining how we interact with our environment. The intersection of these technologies promises to bring about transformative changes across various sectors.

This article offers an overview of Quantum Computing and IoT, their individual characteristics, and the potential impact of their convergence. It balances technical details with considerations of future prospects and ethical implications, providing a well-rounded perspective on these rapidly evolving technologies.

Quantum Computing

At the heart of Quantum Computing are qubits, units of quantum information that, unlike classical bits, can exist simultaneously in multiple states thanks to quantum superposition. This allows quantum computers to perform many calculations at once, dramatically increasing their processing power. Entanglement, another quantum phenomenon, enables qubits separated by large distances to be interconnected, providing a new paradigm for information processing.

Recent advancements in Quantum Computing have been groundbreaking. Companies and research institutions have developed quantum processors with increasing qubit counts, paving the way for more complex and practical applications. However, challenges such as error rates and quantum decoherence, where qubits lose their quantum state, remain significant hurdles.

IoT (Internet of Things)

IoT technology connects everyday devices to the internet, enabling them to send and receive data. This network extends from common household items to sophisticated industrial tools. The integration of IoT with advanced technologies like AI and edge computing is enhancing its capabilities, leading to smarter and more responsive systems.

However, IoT faces its own set of challenges. Security is a major concern, as the increase in connected devices creates more vulnerabilities. Additionally, managing the vast amounts of data generated and ensuring scalability pose significant challenges.

Intersection of Quantum Computing and IoT

Quantum Computing can significantly enhance IoT capabilities, particularly in areas requiring complex computations like optimization and advanced encryption. For instance, quantum algorithms can process vast IoT data sets more efficiently, enabling more effective decision-making.

In the realm of smart cities, Quantum IoT can lead to more efficient urban planning and management. In healthcare, it can enable quicker analysis of patient data, leading to faster and more accurate diagnoses. Logistics and supply chain management can also benefit from optimized routes and inventory management through quantum-enhanced IoT solutions.

Challenges and Ethical Considerations

While the potential is immense, the combination of Quantum Computing and IoT raises significant ethical and security concerns. The power of quantum computers could render current encryption methods obsolete, posing a threat to data privacy. Addressing these concerns is crucial as we advance in these fields.

Conclusion

The integration of Quantum Computing and IoT holds the promise of a more efficient, connected, and intelligent world. While challenges remain, ongoing research and development in these fields are likely to lead to innovative solutions that can overcome current limitations. The future of Quantum Computing and IoT is not just about technological advancement but also about responsibly harnessing these technologies for the betterment of society.

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In the ever-evolving landscape of technology, the Internet of Things (IoT) and Digital Twin technology stand out as pivotal innovations. IoT, with its network of interconnected devices, and Digital Twins, which are virtual replicas of physical systems, are revolutionizing industries from manufacturing to urban planning.

This article presents a comprehensive exploration of IoT and Digital Twin technology, detailing their individual characteristics, the benefits of their integration, and the challenges faced. It offers insights into current trends and future directions, providing a well-rounded perspective on these transformative technologies.

The Convergence of IoT and Digital Twin Technology

IoT’s network of sensors and devices captures real-time data from the physical world. This data is crucial for creating Digital Twins, which are dynamic virtual models of physical objects or systems. By integrating IoT data, Digital Twins can simulate real-world conditions, predict outcomes, and optimize processes.

Understanding IoT

IoT involves a network of physical objects (‘things’) embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet. These devices range from ordinary household items to sophisticated industrial tools.

1. IoT in Industry: In industrial settings, IoT devices monitor and optimize manufacturing processes, improve supply chain logistics, and enhance product lifecycle management.

2. Consumer IoT: In the consumer segment, IoT encompasses smart home devices, wearable health monitors, and connected vehicles.

Digital Twin Technology Explained

A Digital Twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. It is used for simulation, analysis, and control.

1. Application in Manufacturing: In manufacturing, Digital Twins are used to create and test prototypes, predict equipment failure, and optimize production lines.

2. Urban Planning and Infrastructure: Digital Twins simulate entire cities or infrastructure systems, aiding in urban planning and management.

The Synergy of IoT and Digital Twins

1. Real-Time Data and Analysis: IoT feeds real-time data into Digital Twins, allowing for accurate simulations and analyses. This integration helps in predictive maintenance, reducing downtime in industrial settings.

2. Enhanced Decision-Making: By leveraging IoT data, Digital Twins enable better decision-making. They provide insights into system performance, potential failures, and maintenance needs.

3. Customization and Innovation: The combination allows for customization of products and services, driving innovation in various sectors like healthcare, automotive, and aerospace.

Benefits of IoT and Digital Twin Integration

1. Operational Efficiency: This integration enhances operational efficiency by enabling real-time monitoring and predictive maintenance.

2. Cost Reduction: It reduces costs by identifying potential issues before they become critical, minimizing downtime.

3. Sustainability: It promotes sustainability by optimizing resource usage and reducing waste.

Challenges and Solutions

1. Data Security and Privacy: The vast amount of data generated poses security and privacy risks. Implementing robust cybersecurity measures is essential.

2. Interoperability and Standardization: Ensuring interoperability among diverse IoT devices and systems is challenging. Developing universal standards and protocols is key.

3. Complexity and Scalability: Managing the complexity and ensuring the scalability of these systems require continuous innovation and investment.

Future Prospects and Emerging Trends

1. Advancements in AI and Machine Learning: Integrating AI with IoT and Digital Twins will enable more advanced predictive analytics and automation.

2. 5G Integration: The rollout of 5G will enhance IoT capabilities, offering faster, more reliable connections for Digital Twins.

3. Sustainable Development: There’s an increasing focus on using IoT and Digital Twins for sustainable development, particularly in smart city projects and environmental monitoring.

Conclusion

The integration of IoT and Digital Twin technology is a formidable force driving innovation and efficiency across multiple industries. While challenges exist, the potential benefits are immense. As these technologies continue to evolve, their combined impact on industry, urban development, and sustainability will be profound, marking a new era of digital transformation and innovation.

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The Internet of Things (IoT) represents a transformative shift in the way we interact with technology. As physical devices around us become increasingly connected, they offer new levels of efficiency, automation, and convenience. However, this rapid advancement and ubiquity of IoT devices also raise significant regulatory challenges. This article explores the evolving regulatory landscape for IoT, addressing the need for standards, privacy concerns, security risks, international coordination, and the path forward.

Understanding IoT’s Expansion and the Need for Regulation

The IoT ecosystem encompasses a broad range of devices, from smart home appliances and wearables to industrial sensors and smart city technologies. According to Gartner, the number of connected devices will reach over 25 billion by 2025. This expansion is not just quantitative but also qualitative, as IoT technology becomes more complex and integral to various aspects of life and business.

Regulation is crucial in this context to ensure these devices are safe, secure, and respectful of user privacy. However, the unique characteristics of IoT – including its diversity, the volume of data it generates, and its cross-industry applications – pose significant regulatory challenges.

Data Privacy and Protection in IoT

Data privacy is a paramount concern in IoT. These devices often collect sensitive personal information, which can include location data, health metrics, and even personal habits. Ensuring the privacy and security of this data is crucial.

The European Union’s General Data Protection Regulation (GDPR) sets a precedent for data privacy, including provisions that affect IoT. It mandates strict data handling procedures and grants individuals rights over their data. Similarly, the California Consumer Privacy Act (CCPA) in the U.S. provides consumers with rights over their personal information collected by businesses.

However, these regulations often face challenges in enforcement and applicability, particularly with devices that cross international borders. The diverse nature of IoT devices also means that a one-size-fits-all approach to data privacy may not be feasible.

Security Concerns and Standards

IoT security is another critical area of regulatory focus. The interconnectedness of IoT devices creates a broader attack surface for cyber threats. The Mirai botnet attack in 2016, which utilized unsecured IoT devices to launch large-scale distributed denial-of-service (DDoS) attacks, highlighted the potential consequences of inadequate IoT security.

Regulatory efforts in IoT security include the development of standards and guidelines. For instance, the National Institute of Standards and Technology (NIST) in the U.S. has published a series of documents offering guidance on IoT cybersecurity. The UK government has also introduced a code of practice for consumer IoT security and is working on legislation to enforce basic security requirements for IoT devices.

International Coordination and Compliance Challenges

The global nature of IoT poses significant challenges for regulatory compliance. IoT devices often cross international borders, and data collected by these devices can be stored and processed in different countries. This scenario necessitates a coordinated international regulatory approach.

Efforts in this direction include the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) working on international standards for IoT. These global standards aim to provide a common framework that can be adopted by different countries, fostering interoperability and easing compliance challenges.

Consumer Protection and Transparency

With IoT devices becoming a staple in consumer electronics, there’s a growing need for regulations that protect consumers. This includes ensuring that IoT devices are safe, reliable, and do not engage in unfair or deceptive practices.

Transparency is also crucial. Consumers need to be informed about what data their devices are collecting and how it’s being used. The U.S. Federal Trade Commission (FTC) has been active in enforcing transparency and has brought cases against companies that fail to adequately disclose their data practices.

The Road Ahead: Adaptive and Inclusive Regulation

As IoT continues to evolve, so too must its regulatory framework. This requires a balance between fostering innovation and protecting public interests. Adaptive regulation that can evolve with technology is key, as is the inclusion of various stakeholders in the regulatory process. This includes not just governments and industry, but also consumer groups, academia, and civil society.

Engaging in ongoing dialogue and partnership can help address the dynamic challenges IoT presents. It is also important to foster public awareness and education about IoT, empowering consumers to make informed decisions and advocate for their interests.

Conclusion

The regulatory landscape for IoT is complex and multifaceted, reflecting the diverse and rapidly evolving nature of the technology itself. Effective regulation requires a nuanced approach that addresses privacy, security, international coordination, and consumer protection. As IoT devices become more ingrained in our daily lives, the importance of robust, flexible, and forward-looking regulation cannot be overstated. The future of IoT is not just about technological innovation but also about creating a regulatory environment that supports sustainable and responsible growth.

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The advent of the Internet of Things (IoT) has revolutionized how we interact with technology, integrating the physical and digital worlds in unprecedented ways. However, the proliferation of IoT devices also brings challenges, particularly in managing their lifecycle effectively. IoT Device Lifecycle Management (DLM) is crucial for the efficient and secure operation of these devices. This article delves into the various stages of IoT DLM, the challenges involved, and the strategies for effective management.

Understanding IoT Device Lifecycle Management

IoT DLM refers to the processes and methodologies used to manage an IoT device from its inception to retirement. It encompasses the entire spectrum of an IoT device’s existence, including design, development, deployment, operation, maintenance, and eventual decommissioning. Effective DLM is vital for ensuring device security, functionality, and overall network health.

1. Design and Development Stage

The lifecycle of an IoT device begins with its design and development. This stage involves defining the device’s purpose, functionality, and the environment in which it will operate. Key considerations include:

• Security by Design: Implementing security measures at the design phase is critical. This includes hardware and software security features, data encryption, and secure communication protocols.
• Scalability and Compatibility: The device should be designed to be scalable and compatible with different platforms and technologies.
• Energy Efficiency: Many IoT devices are deployed in locations where power sources are limited, making energy efficiency a crucial design factor.

2. Manufacturing and Deployment

Once designed and developed, the next stage involves manufacturing and deploying these devices. This phase must ensure that the devices are built according to the specified design and that they are deployed correctly in their intended environment.

• Quality Assurance: Rigorous testing for quality and compliance with standards is essential.
• Deployment Strategy: The deployment should be planned to minimize disruptions and ensure seamless integration with existing systems.

3. Operation and Maintenance

This is the longest phase in the lifecycle of an IoT device. Key activities include:

• Monitoring and Management: Continuous monitoring for performance and security is vital. IoT devices generate vast amounts of data, and their performance must be consistently managed.
• Software Updates and Patch Management: Regular software updates and patches are essential to address security vulnerabilities and enhance functionality.
• Remote Troubleshooting and Support: Given the often remote and distributed nature of IoT devices, remote troubleshooting capabilities are crucial.

4. Data Management and Analysis

IoT devices collect and transmit data, necessitating effective data management strategies.

• Data Storage and Analysis: Efficient storage solutions and advanced analytics capabilities are required to derive meaningful insights from the data collected.
• Data Privacy and Compliance: Adhering to data protection regulations and ensuring user privacy is paramount.

5. Decommissioning and End-of-Life Management

Eventually, IoT devices reach their end of life, whether due to technological obsolescence, wear and tear, or other factors. This stage involves:

• Safe Decommissioning: Ensuring that devices are decommissioned safely, without posing risks to the environment or data security.
• Data Sanitization: Properly erasing stored data to prevent unauthorized access or data breaches.
• Recycling and Disposal: Adhering to environmental standards for recycling and disposing of electronic waste.

Challenges in IoT Device Lifecycle Management

Managing the lifecycle of IoT devices presents various challenges:

• Scalability: As IoT networks grow, managing an increasing number of devices becomes complex.
• Diverse Device Ecosystem: IoT encompasses a wide range of devices with different functionalities, requiring diverse management strategies.
• Security Risks: IoT devices are often targeted by cyberattacks, making security a continuous concern.
• Regulatory Compliance: Compliance with various regional and industry-specific regulations can be challenging.

Strategies for Effective IoT Device Lifecycle Management

To address these challenges, several strategies can be employed:

• Implementing Standardized Protocols: Adopting industry-standard protocols can help in managing devices efficiently.
• Automated Tools and Platforms: Leveraging automation for device management can significantly reduce the complexity and improve efficiency.
• Regular Security Audits: Conducting regular security audits and assessments can help in identifying and mitigating risks.
• Training and Awareness: Ensuring that staff are trained and aware of best practices in IoT DLM is crucial for its success.

The Future of IoT Device Lifecycle Management

Looking ahead, IoT DLM is set to become more complex and crucial. The integration of AI and machine learning can provide more intelligent and automated management solutions. The adoption of edge computing can also enhance the efficiency of IoT operations. Moreover, as IoT continues to evolve, there will be a greater emphasis on sustainable practices in device lifecycle management, focusing on minimizing environmental impact.

Conclusion

Effective IoT Device Lifecycle Management is critical in ensuring the efficient, secure, and sustainable operation of IoT devices. As IoT continues to grow and permeate various sectors, the challenges in managing these devices will

also increase. However, with the right strategies, tools, and awareness, these challenges can be addressed, leading to more robust, efficient, and secure IoT ecosystems. The future of IoT DLM lies in its ability to adapt to evolving technologies and requirements, ensuring that IoT devices continue to be a driving force in the connected world.

The post IoT Device Lifecycle Management: Ensuring Efficiency and Security in a Connected World appeared first on IoT Business News.

In the landscape of modern technology, the Internet of Things (IoT) stands out as a revolutionary paradigm, embodying a network of interconnected devices that communicate and exchange data seamlessly. This burgeoning web of devices, ranging from smart thermostats to autonomous vehicles, has the potential to reshape our daily lives, enhance efficiency, and open new avenues for innovation. However, the swift advancement and integration of IoT into various sectors also raise profound ethical questions that warrant careful consideration.

Navigating the Complexities: A Deep Dive into the Ethical Implications of IoT Proliferation

Privacy Concerns

The very essence of IoT is to collect, analyze, and transfer data across devices, which inherently raises significant privacy concerns. Smart devices in homes, offices, and public spaces can track individual movements, habits, and even predict future behavior. This granular level of data collection poses a threat to individual privacy. It is essential to question who owns this data, how it is used, and what measures are in place to protect it from misuse.

Companies and policymakers must ensure that individuals retain autonomy over their personal information. This includes transparent policies on data collection, the option to opt-out, and robust security measures to prevent unauthorized access. The European Union’s General Data Protection Regulation (GDPR) is an example of a legislative framework aimed at protecting user privacy, which could serve as a model for other regions.

Security Risks

The interconnected nature of IoT devices means that they are often part of a network, and if one device is compromised, it could potentially lead to a chain reaction affecting the entire system. IoT devices have already been used to launch massive Distributed Denial of Service (DDoS) attacks, and the potential for more sophisticated cyber threats looms large.

Ensuring the security of IoT devices is not just a technical challenge but an ethical imperative. Manufacturers are morally obligated to implement stringent security measures before releasing products into the market. This includes regular updates, secure software by design, and prompt responses to vulnerabilities.

Autonomy and Human Agency

IoT devices are increasingly capable of making decisions without human intervention, which raises concerns about human agency. For instance, smart home systems can regulate temperature and lighting, and autonomous vehicles can navigate roads with minimal or no human input. While this automation can offer convenience and efficiency, it also raises questions about the degree of control humans should relinquish to machines.

The ethical dilemma revolves around finding the balance between harnessing the benefits of automation and maintaining human decision-making power. There should be clear boundaries on the autonomy of IoT devices, and humans should have the ultimate say in critical decisions, especially those with moral ramifications.

Inequality and Accessibility

The proliferation of IoT has the potential to widen the socio-economic divide. Access to the benefits of IoT technology is often contingent on economic status, with the affluent having more opportunities to integrate IoT into their lives. This digital divide could lead to a society where there is an inequality of convenience, efficiency, and even health outcomes, as IoT continues to expand into areas like healthcare and city planning.

To address this ethical concern, it is vital to advocate for inclusive technology policies that ensure IoT devices are accessible and affordable. Governments and organizations can play a role in subsidizing costs or providing IoT solutions in public services to bridge the gap.

Environmental Impact

The production, operation, and disposal of billions of IoT devices have significant environmental implications. The demand for rare earth elements and the energy consumption of IoT devices contribute to ecological degradation. Additionally, electronic waste is a growing concern, as many IoT devices have short lifespans and are not designed to be recyclable.

Ethically, there is a responsibility to consider the environmental footprint of IoT. This includes designing products with longer lifespans, energy-efficient operations, and sustainable materials. Furthermore, recycling programs for IoT devices should be standard practice, and consumers should be educated about their environmental responsibilities.

Conclusion

The Internet of Things heralds a new era of technological integration, but with it comes a complex web of ethical considerations that must be addressed proactively. Privacy, security, autonomy, inequality, and environmental impact are just some of the ethical challenges that the IoT presents. As we move forward, it is imperative for stakeholders, including technologists, policymakers, and the public, to engage in open dialogue and develop a framework that prioritizes ethical considerations in the evolution of IoT.

The conversations about the ethics of IoT are as essential as the technology itself. They will shape the norms, laws, and regulations that guide the development and deployment of IoT technologies. Only with a concerted and collaborative effort can we ensure that the IoT serves as a tool for positive change, enhancing lives while respecting individual rights and ecological boundaries. As we advance into this connected future, our moral compass must navigate the course as much as the innovative spirit that drives the IoT forward.

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“SpaceX’s move towards standards-based IoT solutions in the direct-to-device market reflects the industry’s shifting landscape from proprietary solutions to standardisation.”

SpaceX made its first-ever acquisition in August 2021 when it purchased Swarm Technologies, a start-up known for its early adoption of proprietary ultra-narrowband satellite IoT solutions; a move that signalled SpaceX’s intention to expand its capabilities and diversify its offerings to cater to a broader range of customers. However, Swarm’s announcement in July 2023 that it would cease selling new devices has raised eyebrows. Swarm’s decision appears to be directly linked to its parent company’s intention to shift the focus of its IoT strategy towards the direct-to-device/cell (D2D/C) market.

Why is this worth noting? In other verticals – specifically broadband access, enterprise, mobility, and government – that are using the Starlink offering, SpaceX is offering proprietary solutions, which has enabled it to scale rapidly. However, it would appear that integrating with telcos in the IoT market requires a different approach for achieving scale and success.

The move is not surprising to NSR, as this shift in direction towards standards-based solutions from proprietary ones aligns with the trend that was outlined in our recently released report, M2M and IoT via satellite, 14th edition report, that the number of in-service IoT units using 5G and standard-based protocols (for example, LoRa) is expected to grow rapidly in the next 10 years, reaching 39.3 million by 2032, out of a total market of 57.7 million devices (Figure 1).

The satellite direct-to-device ecosystem is taking shape … gradually

3GPP Release 17’s inclusion of non-terrestrial networks opens the door for direct-to-cell development, allowing IoT devices to communicate with satellite networks directly. Chipset manufacturers are working to integrate standards; for example, Mediatek has demonstrated its 3GPP NTN technology, and Qualcomm has launched new 3GPP modems. In March 2023, the FCC unveiled a proposed satellite direct-to-device regulatory framework, which will facilitate partnerships and collaboration between satellite operators and telecoms operators (telcos), as well as establish ground rules for market players. All these advances will promote the development and growth of the D2D ecosystem.

To seize the opportunities presented by this shift towards standardization, SpaceX has been forming partnerships with terrestrial network operators worldwide. Some recent examples include its partnership with T-Mobile to broaden coverage in the USA, and Optus for D2C coverage across Australia. These future integrations have primarily been marketed as opportunities for consumers to request emergency assistance and send text messages back and forth, but such partnerships are also ideal for narrowband IoT applications; these applications have a clear short-term value proposition for enterprises that crave 100% visibility on mobile assets.

One exciting prospect that could arise from SpaceX’s D2C vision is its potential to connect a wide array of devices and allow roaming to satellite networks, especially those that are mobile, moving in and out of terrestrial network coverage, like passenger vehicles. Elon Musk is known for his ambitious goals and SpaceX’s D2D initiatives could pave the way for connecting Tesla cars. Musk has previously noted that Tesla vehicles will not be line-fit to connect to the Starlink service, this was in the context of high-speed flat panel antenna connectivity – a much more expensive service to integrate into vehicles. Musk has since commented that SpaceX’s narrowband D2D initiative is much more suited to Tesla vehicles.

However, it is important to note that these partnerships will take time to actually implement. Bigger satellites are needed for this service, which in turn will need to be delivered by SpaceX’s larger Starship launch vehicle – the same one that was destroyed after a failed test flight in April this year. Consequently, SpaceX has not yet set a date for the satellite-to-cell service to be trialled, but it is likely to occur some time in 2024. In the meantime, other proprietary services are already moving in.

Proprietary solutions still have their place in the IoT ecosystem

From a technical perspective, there is no definitive conclusion as to which protocol strategy is better – using proprietary systems, 3GPP standards, or other standards-based systems such as LoRa. All have their advantages and disadvantages.

Proprietary protocols, whether deployed on small satellite constellations, or using leased mobile satellite service (MSS) capacity, do provide some advantages over standards-based protocols according to operators, which are mainly related to efficiencies and costs. Satellites can use highly efficient proprietary waveforms to handle billions of sensors on a single footprint, and therefore offer better economies of scale than current 3GPP standards.

This is in part due to the overhead in satellite-to-cellular and cellular-to-satellite hand-off on 5G 3GPP systems, which needs to be considered carefully, otherwise the data overhead could overwhelm networks. While 3GPP Release 17 improves this significantly, more needs to be done to make this a more seamless process. Some satellite operators are therefore already going beyond Release 17 in order to improve efficiency, and it is likely that such improvements will be incorporated into future releases.

For end users, deploying proprietary systems makes most sense when there is no terrestrial connectivity available (or it is just not needed), because the greater spectrum efficiency can potentially reduce the price down to single-digit dollars per year in some use cases.

For standards-based IoT solutions though, operators will need to strike a balance between backward compatibility and performance and regulatory certainty. Collaborative network planning and integration are also essential to ensure seamless integration of networks. If stakeholders come together and co-operate in ecosystems based on standards, this could maximise synergies between terrestrial and non-terrestrial networks. If this occurs, and more roaming agreements come into place, a high-growth scenario for the broader satellite IoT market could occur, resulting in almost 140 million in-service units in 2032 (Figure 2).

Other uncertainties will challenge business models including the revenue split between satellite operators, MNOs, IoT service providers, and other value-chain members. Any regulations and policies will also introduce elements of uncertainty.

Bottom line

SpaceX’s move towards standards-based IoT solutions in the direct-to-device market reflects the industry’s shifting landscape from proprietary solutions to standardization. This strategic move should position SpaceX to take advantage of the rapidly growing direct-to-device IoT market of the future while aligning with industry standards and trends. SpaceX should be well-positioned to play a pivotal role in the direct-to-device market by forming partnerships, leveraging standards and capitalizing on decreasing device prices.

Although a lot of uncertainties remain in the D2D market, going by the open standards route would simplify telco integration and increase market penetration. At the moment, proprietary solutions have the edge; but 5G standards will begin to outpace growth and market penetration by 2028 and this will continue in the long term. This market development is still 4 years away, but SpaceX is making a bet today that open standards will win over proprietary systems.

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As billions of sensors, smart devices, and machines generate a constant stream of data, harnessing the power of this data deluge poses formidable challenges, particularly when it comes to data analytics within IoT systems.

In this article, we delve into the five key challenges that organizations face when implementing or utilizing data analytics within their IoT ecosystems. Managing the sheer volume and variety of data, addressing real-time processing demands, ensuring interoperability and scalability, navigating the complexities of long-term maintenance — each hurdle presents unique obstacles.

To complement our exploration of these challenges, we offer practical tips and strategies to overcome them. Let’s start.

Challenge 1: Data Volume and Variety

IoT devices generate an enormous amount of data in various formats and from diverse sources, and this influx of data can overwhelm storage and processing capabilities. Moreover, IoT data comes in structured and unstructured forms, requiring sophisticated data integration and transformation processes to get accurate outcomes. Failing to handle such a wide range of data types efficiently, and in a way that aligns with analytical goals, can result in information overload and hinder the ability to derive meaningful insights from IoT data.

Addressing this challenge o requires a strategic approach. Here are several recommendations that can be helpful:

Data Preprocessing and Cleansing:

• Invest in robust data preprocessing pipelines to clean, filter, and standardize IoT data.
• Utilize data cleansing techniques, such as data imputation and noise reduction, to improve data quality and consistency.
• Implement data compression and aggregation techniques to reduce the volume of data while retaining essential information.

Data Storage and Management:

• Employ scalable and flexible data storage solutions, such as distributed databases or cloud-based storage, to accommodate the growing volume of IoT data.
• Use data partitioning and indexing to optimize data retrieval and analysis.
• Implement data lifecycle management strategies to archive or delete obsolete data, reducing storage costs and improving system performance.

Advanced Analytics and Machine Learning:

• Leverage advanced analytics techniques, such as machine learning algorithms and predictive modeling, to extract meaningful insights from IoT data.
• Implement edge analytics, which allows data processing and analysis to occur closer to the data source, reducing the need for transmitting large volumes of data to a centralized server.
• Explore data reduction methods, like dimensionality reduction or feature selection, to focus on the most relevant data attributes and reduce data variety.

By adopting these strategies, organizations can better manage the challenges of data volume and variety in IoT analytics, enabling them to derive valuable insights from their IoT deployments while optimizing resource utilization.

Challenge 2: Real-time data processing

Real-time data processing in IoT systems is challenging due to several factors. First, IoT devices generate continuous streams of data at high velocities, necessitating rapid processing to provide timely insights. Second, ensuring low-latency processing is demanding, as data must be analyzed and acted upon swiftly, often within milliseconds or seconds. Third, real-time analytics require significant computational power, making it essential to have the right infrastructure. Finally, handling real-time data can strain network bandwidth and storage resources, and failures or delays in processing can lead to critical consequences in applications like autonomous vehicles or industrial automation, making reliability a paramount concern.

Addressing the challenge of real-time data processing in IoT systems is vital for timely decision-making and responsiveness. Here is a set of tips to cover that:

Streamlining Data Pipelines:

• Implement edge computing to process data in close proximity to its source. This reduces latency by analyzing data locally, only transmitting important insights to central servers. Edge devices can pre-process, filter, and aggregate data before it reaches the cloud or data center, minimizing the computational load on central servers.
• Utilize stream processing frameworks like Apache Kafka, Apache Flink, or AWS Kinesis. These platforms enable real-time data ingestion, processing, and analytics, facilitating the handling of data streams with low latency.

Efficient Data Storage and Retrieval:

• In-Memory Databases: Deploy in-memory databases like Redis or Apache Cassandra to store frequently accessed data. In-memory storage significantly reduces data retrieval times compared to traditional disk-based databases.
• Data Indexing: Implement efficient indexing mechanisms to quickly locate and retrieve specific data points. This ensures that real-time analytics can access the required data swiftly.

Scalability and Load Balancing:

• Use auto-scaling techniques to dynamically allocate resources based on workload demand. This ensures that your system can handle increased data loads during peak times and scale down during periods of lower activity.
• Employ load balancing solutions to distribute incoming data and processing tasks evenly across multiple servers or instances. Load balancing optimizes resource utilization and prevents overloading individual components.

By implementing these tips, IoT systems can enhance their real-time data processing capabilities, enabling quicker insights and more responsive actions, which are especially critical in applications like smart cities, healthcare monitoring, and industrial automation.

Challenge 3: Interoperability of Data Flows

Interoperability is a challenge in IoT data analytics because IoT ecosystems involve a diverse array of devices, protocols, and standards from various manufacturers. These disparate components may not naturally communicate or work seamlessly together, hindering data aggregation and analysis. Without effective interoperability, IoT systems may struggle to harmonize data from different devices, hindering the quality of data analytics. Here are some tips to overcome this challenge:

Open Standards and Protocols. Prioritize IoT devices and systems that adhere to widely accepted open standards and communication protocols such as MQTT, CoAP, or OPC UA. These standards facilitate interoperability by ensuring devices can communicate with one another regardless of their manufacturer.

Middleware and API Layers. Implement middleware solutions or API layers that serve as intermediaries between heterogeneous devices and data analytics solutions. These layers can translate data formats, protocols, and interfaces, ensuring data compatibility and uniformity.

IoT Platforms and Ecosystems. Choose IoT platforms that offer built-in support for diverse devices and protocols, simplifying integration and analytics. Platforms like AWS IoT, Azure IoT, or Google Cloud IoT provide tools and services to bridge interoperability gaps.

These tips can help organizations to streamline data integration and ensure more coherent and accurate analytical outcomes within the IoT ecosystem.

Challenge 4: Scalability of Data Infrastructure

IoT ecosystems often grow rapidly, involving an increasing number of devices and data sources. As the volume of data and the complexity of analytics processes expand, traditional infrastructure and software may struggle to keep up. Failure to address scalability can result in system bottlenecks, decreased performance, and inefficient data analysis.

Scalability challenges necessitate the use of modular architectures, distributed computing, and cloud-based solutions to accommodate the ever-growing data and analytical requirements of IoT systems. Below you can see these strategies explained.

Distributed Computing. Utilize distributed computing frameworks such as Apache Hadoop or Apache Spark to process and analyze large datasets across multiple nodes or clusters. This allows the system to scale horizontally by adding more computational resources as needed.

Cloud Services. Leverage cloud-based IoT platforms and analytics services like AWS IoT Analytics, Azure IoT Hub, or Google Cloud IoT to benefit from the inherent scalability and flexibility of cloud infrastructure. Cloud providers can automatically allocate resources based on demand, ensuring scalability without the need for extensive manual management.

Containerization and Microservices. When IoT software development, adopt containerization technologies like Docker and Kubernetes to containerize analytics applications and services. This modular approach enables easy scaling of individual components, making it simpler to add or remove instances to match changing workloads.

Equipped with these strategies, organizations can ensure that their systems can accommodate expanding data sources and computational requirements while maintaining performance and reliability.

Challenge 5: Long-term Maintenance

IoT systems often have extended lifecycles. Over time, hardware and software components may become obsolete, requiring upgrades or replacements. Additionally, data analytics algorithms and models may need continuous optimization to remain relevant and accurate as data patterns evolve. The challenge lies in sustaining the functionality, security, and performance of IoT data analytics systems amid technological advancements and changing requirements, which demands ongoing resources and expertise.

Here are three tips to tackle long-term maintenance issues:

Comprehensive Documentation and Knowledge Transfer:

• Maintain detailed documentation of the system architecture, hardware components, software configurations, and data flows. This documentation should be regularly updated to reflect changes and additions.
• Implement knowledge transfer mechanisms to ensure that the expertise required for system maintenance can be passed on to new team members or external contractors. This can involve creating manuals, conducting training sessions, and establishing clear roles and responsibilities.

Regular Updates and Patch Management:

• Establish a robust update and patch management process for both hardware and software components. Regularly check for and apply security patches, firmware updates, and software upgrades to mitigate vulnerabilities and ensure compatibility with evolving technology standards.

Scalable and Future-Proof Design:

• Design the IoT analytics system with scalability and flexibility in mind. Ensure that it can accommodate future technological advancements and changing requirements without significant disruptions.
• Implement forward-compatible data models and analytics algorithms that can adapt to evolving data patterns and business needs, reducing the need for frequent reconfiguration.

By following these tips, organizations can contribute in ensuring the longevity and continued functionality of their IoT data analytics systems while minimizing risks associated with system obsolescence and degradation.

Closing Remarks

As organizations continue to harness the power of IoT-generated data, understanding the challenges becomes essential for leveraging the full potential of interconnected devices. By addressing data volume and variety, embracing real-time processing, ensuring interoperability, planning for scalability, and committing to long-term maintenance, companies can navigate the complexities of IoT data analytics with confidence, which empowers businesses to make data-driven decisions and create a smarter, more connected future.

The post 5 Challenges of Data Analytics within IoT Systems and Tips to Solve Them appeared first on IoT Business News.

With the inflation and slowdown in macro-economic growth, the Bluetooth device market is estimated to decrease by 6.3% on YoY to 8.07 billion units in 2022, as a result of the significant decrease in the market for host devices such as smartphones, PCs, TVs… and the saturation of Bluetooth audio application market.

Despite the weak consumer demand, BLE (Bluetooth Low Energy) market has been growing in 2022 and 2023 thanks to the expansion in industrial automation, home/building/retail automation, healthcare, automotive and so on. Bluetooth has been expanding new application and use cases along with the introduction of new standards and features.

Bluetooth Device Market Forecast by Device Type

The Bluetooth device market is estimated to shrink slightly to 8.01 billion units in 2023 due to weak consumer demand and inventory correction. However, Bluetooth device market is estimated to rebound in 2024 after the inventory digestion, and improved consumer spending. Bluetooth market will grow by mid to low single digit after 2024, will surpass 10 billion units by 2028.

Dividing Bluetooth application into host type devices such as smartphones, PCs, and TVs, and peripheral/accessory type devices, the market for host-type devices has matured and will decline significantly in 2022-2023. The peripheral/accessory type devices market grows in 2023 onwards, driving the growth of the Bluetooth market. Peripherals and accessories are estimated to account for 65% of the total Bluetooth market in 2021, growing to just under 73% in 2028. Bluetooth adoption will expand in various devices such as HID (Human Interface Device), remote controller, proximity tag/ asset tracking, home/building/retail automation, smart lighting, automotive accessory, mobile accessory, healthcare, and so on.

Single Mode Bluetooth Low Energy Device Market Forecast

In 2021, the BLE chip market expanded strongly by 44% YoY to 1.57 billion units. 2021 growth was partly contributed by the inventory buildup in user companies and channel. Strong demand continued by the first half 2022, but the demand decline and inventory adjustment began in the second half of 2022. 2022 market growth is estimated to remain at 9.4% YoY. The consumer, PC and mobile application market remains weak, but industrial and automotive applications will grow. Overall market growth is estimated at 4% in 2023.

After 2024, in addition to the economic recovery and inventory normalization, BLE device market is expected to show strong momentum due to the expansion of new applications such as audio, automotive digital key and TPMS(Tire Pressure Monitoring System), commercial tag, smart lighting, access control (door lock), electric shelf label, smart metering, and EV charging pile. Some of the application market will grow along with the new features such as Periodic Advertisement with Response (for electric shelf label), Channel Sounding (secure access), LE audio/Auracast (for hearing aid and public speaker). Bluetooth adoption in Matter standard also support Bluetooth penetration in smart home market.

Though BLE has been used in broad range of IoT devices, the main volume application of BLE chip are as follows in descending order: 1.remote controller, 2.tag/beacon (proximity, asset tracking), 3.wearable (smart watch/ activity tracker), 4.HID (mouse/keyboard/digital pen), 5.home/building automation (lighting, door lock/access control, remote switch, sensor…), 6.healthcare device.

In 2022-2023, while the volume of BLE shipments for tag/beacon (proximity/tracking), home/building automation, home/building automation, healthcare, and automotive accessories will increase. BLE IC shipment for wearable decreases significantly as BLE has been replaced by Bluetooth Audio SoC, with the emerge of voice enabled smart watch market in India, China and other developing countries. Remote controllers and HIDs market volume remains flat in relation to the sluggish market for PCs, tablets, TVs, and set-top boxes.

Bluetooth Low Energy IC Market Share

There are over 50 BLE IC suppliers and market share is quite fragmented. However, Nordic Semiconductor keeps top spot since 2014. Nordic establishes leading position in BLE IC market by growing developer community with mature SDK, reference design, low power RF design and flexibility in memory.

In 2022, Nordic, Realtek, Telink, Renesas, and PHYPlus are recognized as top 5 supplier in shipment unit, followed by SiLabs (SiliconLabs), NXP, STMicroelectronics, Qorvo, Infineon, and TI (Texas Instruments). Nordic, PHYPlus and NXP gained the market share in 2022, while Telink, Renesas and Realtek dropped in market share.

The main supplier differs by end market and application. The table below shows the main BLE IC suppliers by application.

Major BLE SoC Supplier by Application

Chinese low cost BLE IC suppliers takes major share in activity tracker, remote controller, HID, mobile accessory, and PC accessory. On the other hand, Western IC suppliers are relatively strong in the tag/beacon (industrial/commercial asset tracking), healthcare, and automotive.

Energy Harvesting and Ambient IoT are attracting attention in the fields of tag/beacon, tracking and proximity. Energy Harvesting and ultra low power BLE IC suppliers are Atmosic, InPlay, OnSemi, WilioT, EM Microelectronics, and Renesas.

For automotive, NXP and TI are the popular BLE IC supplier. In addition, major automotive IC suppliers such as Infineon and Renesas aims to enter the automotive BLE IC market. Moreover, there is a demand for domestic Bluetooth IC suppliers in the Chinese market, and Beken, Senasic and others are developing Bluetooth ICs for automotive.

The post TSR Market Update: Bluetooth Low Energy Market appeared first on IoT Business News.

IoT Analytics, a leading provider of market insights and strategic business intelligence for the Internet of Things (IoT), AI, Cloud, Edge, and Industry 4.0, today released the results of the quarterly company earnings call analysis.

This analysis is based on a comprehensive dataset of Q3 2023 earnings calls from 4,000+ leading US-listed firms. The findings from Q3 2023 show that three pivotal themes are currently trending in CEO discussions: 1. AI and Generative AI, 2. AI chips, and 3. sustainability. These influential topics have captivated boardrooms worldwide and are shaping the future investment priorities for companies across various industries.

KEY INSIGHTS:

• According to the latest “What CEOs talked about” report, three themes gained noticeable traction in earnings calls in Q3 2023: 1) AI and most of its related application topics, 2) AI chips, and 3) sustainability.
• ChatGPT is losing traction as the AI keyword of choice, and economic worries and uncertainty appear to be easing.

KEY QUOTES:

• Knud Lasse Lueth, CEO at IoT Analytics, comments: “I’m encouraged to see that as concerns of a potential recession fade, the emphasis on sustainability remains robust. AI, particularly generative AI, is emerging as the dominant theme of 2023. It’s evident that generative AI isn’t just another passing trend like blockchain or the metaverse. CEOs must take decisive action now to be at the forefront of this technological revolution.”
• Philipp Wegner, Principal Analyst at IoT Analytics, adds that “The surge in discussions around AI strategy and AI chips in Q3 2023 is a clear indicator that businesses are moving beyond the ‘what’ and ‘why’ of AI to the ‘how.’ CEOs are now grappling with the logistical challenges of integrating AI into their existing infrastructures, and that’s where the real transformation begins.”

The big picture

In Q3 2023, topics related to economic worries remained prominent in boardroom discussions globally but continued to decline quarter-over-quarter (QoQ). Mentions of the most talked about topic, inflation, dropped 5% QoQ to 47% of calls. Supply chain worries saw the smallest drop, 2% QoQ, while recession experienced the largest drop of 38% QoQ, now appearing in 11% of earnings calls.

Key quote on the macro environment: “Our base case today assumes a mild recession.” – Mark Mason, CFO, Citigroup, July 14, 2023

Key rising themes in Q3

AI : In Q3 2023,the broader topic of AI rose fairly steeply to 29% (+37% QoQ), and its subfield generative AI continued to rise in importance to 10% (+56% QoQ). During their quarterly earnings calls, CEOs appeared to be separating AI and many of its subfield topics from discussions about ChatGPT, which declined this QoQ (as discussed below in “Declining themes”).

Our analysis also shows a marked incline in the mentions of AI strategy and AI infrastructure, rising 128% and 103% QoQ, respectively.

Key CEO quote on generative AI: “Generative AI is at the forefront of customer conversations. However, enterprises are also realizing that they cannot have an AI strategy without a data strategy to base it on.” – Frank Slootman – CEO, Snowflake, August 23, 2023

AI chips : Mentions of GPUs greatly outpaced mentions of CPUs, with the former climbing 102% QoQ to 2.8% in Q3 2023 earnings calls and the latter climbing 47% QoQ to 1.7%. Coinciding with this trend is GPU chipmaker Nvidia climbing 146% in mentions to 1.9% compared to Intel only climbing to 1.2% (+48% QoQ) and AMD to 0.8% (+11% QoQ) in reference to AI chips.

“We’ll actually take the Nvidia hardware as fast as Nvidia will deliver it to us. Tremendous, tremendous respect for Jensen and Nvidia. They’ve done an incredible job. And frankly, I don’t know if they could deliver us enough GPUs.” – Elon Musk – CEO, Tesla, July 19, 2023

Sustainability, energy transition, and renewable energy : Mentions of sustainability, energy transition,and renewable energy slightly rebounded to 21.4% (+9.2% QoQ), 5.3% (+15.4% QoQ), and 5.2% (+10.9% QoQ), respectively. These topics are lower than their peaks in Q1 2022; however, since Q3 2022, each topic has remained generally consistent in its percentage of earnings call mentions.

“We don’t talk a lot about grid … but increasingly here in the U.S., people appreciate how critical grid modernization will be to the energy transition.” – Larry Culp – CEO, General Electric, August 28, 2023

Declining themes in Q3

ChatGPT: As mentioned, ChatGPT experienced a decline in its number of mentions in the earnings reports, dropping 34% QoQ to 2.5%. Last quarter, we saw a transition from CEOs specifically discussing ChatGPT to them discussing enterprise-wide applications of generative AI. That transition still appears to be occurring, as the other AI topics saw significant rises in mentions:

• LLM rose 98% QoQ to approximately 2%.
• Computer vision rose 60% QoQ to 1%.
• Chatbot rose 58% to 1%.

Remote work: The topic of remote work dropped the most of the key topics we tracked, falling 48% QoQ to 0.5% of earnings calls. This decline comes amid new concerns of a COVID-19 infection surge due to a newly detected SARS-CoV-2 variant. Early studies show that antibodies from vaccinations and past infections may enable immune systems to detect and combat the variant sufficiently, which could be easing boardroom concerns of a significant surge (of note, mentions of Corona dropped 8% QoQ to 0.9% of earnings calls).

This decline in discussions around remote work also comes amid increasing so-called return-to-office mandates, with 90% of US companies expected to require employees to work in person at least a few days a week by the end of 2023.

Deep-Dives

#1 AI

In the first quarter of this year, ChatGPT was the hot new (AI) topic for CEOs. In Q2, generative AI shared the stage with ChatGPT as AI proponents discussed other generative LLMs. Now, in Q3, ChatGPT is no longer the hot topic, but rather, generative AI, AI strategy, AI infrastructure, and AI Chips—with companies realizing that ChatGPT is just one tool of many in the new AI portfolio. However, AI is an evolving topic, and OpenAI continues to conduct research and development into ChatGPT, so there is no reason to suspect ChatGPT will completely fade from discussions in the near term.

AI use cases like coding and chatbots are starting to climb

AI use cases like coding and chatbots are also starting to climb, but they have not yet reached the same level as the aforementioned topics. These use cases will likely roll out in the next 6–12 months, so we assess these (and other) topics to grow in Q4 and into 2024.

Of the AI umbrella (not counting AI chips, which we dive into more below), generative AI was the most talked about by CEOs. LLMs, AI infrastructure, and AI strategy saw significant rises in discussions, though they only appear within 1%–2% of the earnings reports overall.

Technology and communication services sectors lead in AI discussions

The technology and communication services sectors lead most AI and AI subfield discussions. 67.6% of technology sector earnings calls mentioned the general topic of AI, while 50.8% of the communications services sector earnings calls discussed it. Generative AI was also fairly high for both sectors, with it mentioned in 33.9% of technology sector earnings calls and 24.2% in communications services sector calls. Of note, the industrial sector showed the third highest interest in generative AI, with 6% of their earnings calls mentioning the topic.

Coinciding with the topic of AI and subfields is a rise in AI strategy and infrastructure discussions. Though hovering between 0.5% and 1% of earnings calls, they rose 128% and 103%, respectively. This could be a sign that as companies look to AI to increase productivity and assess future investments, they are also considering AI implementation strategies.

Key CEO quotes on AI and its subfields

“Generative AI really [instantiated] AI in software, which means developers play a bigger role rather than data scientists. And that’s where you really see the business impact, and I think that impact will be large over the next three to five years…” – Dev Ittycheria – CEO, MongoDB, September 01, 2023

“The application of AI has both horizontal and vertical components. Horizontal is building out the AI infrastructure.” – Anirudh Devgan – CEO, Cadence, July 24, 2023

#2 AI Chips

Also coinciding with the rise in AI discussions are GPUs, most noteworthy Nvidia, the leading GPU vendor in the market. While general discussions of GPUs rose approximately 102% QoQ to 2.8% of earnings calls, Nvidia rose 146% QoQ to 1.9%. On the chipset front, CPUs only saw a 47% climb QoQ to 1.7% of earnings calls. Meanwhile, on the vendor front, Intel saw a decent climb in discussions (~48% QoQ), and AMD a subtle climb (~10% QoQ).

GPUs are becoming the core of choice for AI applications largely due to their architectural advantages over CPUs. While CPUs are optimized for task switching and fast sequential execution with only a few, large cores, GPUs often have many more, smaller cores designed for parallel processing. Deep learning involves performing many mathematical operations, including floating-point arithmetic (often used in rendering graphics) simultaneously, making GPUs a natural fit.

GPUs also offer better cost efficiency for scaling. Though more expensive than CPUs, the time savings they offer for training large language models can more than compensate for that cost. Given that the race is on for AI market dominance, time can actually be money in this case.

Key CEO quotes on AI chips

“I think you can look at 2023 as really a year of planning for AI, because as I said, there’s tons and tons of GPUs being purchased.” – Jayshree Ullal – CEO, Arista Networks, July 31, 2023

#3 Sustainability

Since their peaks in Q1 2022, the rate of discussions of sustainability, emissions, energy transition, and renewable energy in earnings calls have remained generally consistent each quarter, with each topic generally ticking up and down QoQ. This consistency signals a continued awareness of climate impact considerations in the sectors without it being a growing discussion topic in general.

That said, our analysis showed a general trend of CEOs emphasizing real-world projects for energy transition and renewable energy. It is worth highlighting that this emphasis comes amid a year (even quarter) of record temperatures. The US National Oceanic and Atmospheric Administration (NOAA) found that July 2023 had the warmest monthly sea surface temperature of any month and declared August 2023 the warmest August since NOAA started keeping global climate records 174 years ago.

Basic materials, utilities sectors and EMEA companies lead discussions on sustainability

The basic materials and utilities sectors led the discussions on sustainability—45.7% and 40.9% of their respective earnings calls—with utilities taking the general lead in discussions of emissions, climate, batteries, and renewable energy (including solar power). Of note, the energy sector had the second-highest mentions of emissions (39.3%) and the fourth-highest mentions of sustainability (30.8%).

By region, our analysis showed that European, Middle Eastern, and African (EMEA) companies talked about sustainability the most in Q3 2023, with mentions rising approximately 17% QoQ to 38.5%. Meanwhile, Asia-Pacific (APAC) companies continued to increase their number of mentions over North American companies—the former reaching 28.1% in Q3 2023 while the latter remained stagnant around 17%.

Key CEO quotes on sustainability, energy transition, and renewable energy

“The main areas of investment will be in generative AI, next-generation social infrastructure, and renewable energy.” – Junichi Miyakawa – CEO, SoftBank, August 06, 2023

“The downturn in the global economy is not—across the board—even, and consequently, there [is] still a lot of capital being moved […] And it holds for the energy transition, which is almost a given that we have to move forward one way or another.” – Tom Erixon – CEO, Alfa Laval Corporate AB, July 20, 2023

What it means for CEOs

5 things CEOs should ask themselves based on findings in this report:

1. Generative AI and data strategy: How is our company planning to leverage generative AI? Are we strategizing how to deal with this technology breakthrough and do we have a solid data strategy to ensure its effective application? Are we willing to play the “waiting game” and become a smart follower as other companies charge ahead with their generative AI plans.
2. AI chips: With GPUs seemingly becoming the hardware of choice for AI processes, how are we positioned in terms of hardware infrastructure? Do we have access (directly or through strategic partners) to the compute capacity we may need?
3. Macroeconomic concerns: With inflation, supply chain worries, and recession discussions seemingly on the decline, are we prepared for a potentially better than anticipated 2024?
4. Remote work and in-person mandates: With some companies calling employees back to the office and others continuing to operate remotely or flexibly hybrid, what are we communicating to our teams?
5. Sustainability: With an increasing focus on sustainability, what is our strategy to achieve net-zero? Are we really doing enough and have we baked in unforeseen challenges into our calculations, such as high electricity demand through the upcoming AI wave?

What it means for those serving CEOs

There is an opportunity for employees, service providers and other stakeholders to help CEOs excel at the topics they care about. Here are 5 questions those people serving CEOs could ask themselves based on findings in this report:

1. Generative AI implementation: How are we helping the CEO prioritize and implement generative AI projects? Are we helping in aligning the teams behind the opportunity and help secure the infrastructure that may be needed?
2. Making the most of generative AI: Beyond ChatGPT and obvious use cases such as chatbots or coding, what other generative AI technologies are on the horizon that might be relevant to our operations? How can we ensure that our CEO is briefed on these potential opportunities?
3. Economic pulse: While macroeconomic concerns like inflation and recession are declining in discussions, how are we ensuring that our CEO gets regular, concise, and clear updates on these topics and their potential impacts on our operations?
4. Work model adaptation: How can we help the CEO formulate a remote/hybrid work policy that ensures productivity, ongoing learning, employee happiness and safety for the employees?
5. Sustainability initiatives: Do the company’s current projects and investments reflect the increasing importance of sustainability and renewable energy? How can we help the CEO better communicate these initiatives to shareholders and the public?

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