At the core of Smart Cities lies the Internet of Things (IoT), enabling real-time visibility across infrastructure, mobility networks, and public services. By connecting physical assets to digital platforms, cities can optimize operations, reduce costs, and support more responsive governance. However, the implementation of Smart Cities raises complex technical, organizational, and economic questions that extend far beyond sensor deployment.

Key Takeaways

• Smart Cities rely on IoT infrastructure to collect, process, and act on real-time urban data.
• Key domains include mobility, energy management, public safety, and environmental monitoring.
• Multiple connectivity technologies coexist, from LPWAN to 5G and fiber backbones.
• Data integration and interoperability remain major technical and organizational challenges.
• Long-term success depends on scalable architectures, governance models, and sustainable business cases.

What is a Smart City?

Smart Cities refer to urban environments that leverage digital technologies—particularly IoT—to monitor, manage, and optimize infrastructure, mobility systems, and public services in real time. This involves embedding sensors, connectivity, and data platforms into physical assets such as roads, buildings, utilities, and transportation systems.

In the broader IoT ecosystem, Smart Cities represent one of the most complex and large-scale deployments, combining heterogeneous devices, multiple communication layers, and diverse stakeholders. Unlike isolated industrial IoT systems, Smart Cities require cross-domain integration, spanning public infrastructure, private services, and citizen-facing applications.

The objective is not solely technological. Smart Cities aim to improve urban efficiency, reduce environmental impact, and enhance service delivery, while balancing economic viability and regulatory constraints.

How Smart Cities work

The architecture of Smart Cities typically follows a multi-layered model, combining edge devices, communication networks, data platforms, and application layers.

At the device layer, sensors and actuators are deployed across urban assets. These include traffic sensors, environmental monitors, smart meters, surveillance systems, and connected infrastructure components. Devices collect data such as traffic flow, air quality, energy consumption, or occupancy levels.

Connectivity forms the backbone of Smart Cities infrastructure. Depending on the use case, cities deploy a mix of technologies, including low-power wide-area networks (LPWAN), cellular IoT (LTE-M, NB-IoT), Wi-Fi, and increasingly 5G. Each technology addresses different requirements in terms of bandwidth, latency, coverage, and energy consumption.

Data is transmitted to centralized or distributed platforms, often hosted in cloud or edge environments. Edge computing is increasingly used to process data closer to its source, reducing latency and bandwidth usage—particularly critical for applications such as traffic control or public safety.

At the platform layer, IoT platforms aggregate, normalize, and analyze data from multiple sources. This enables interoperability across systems and supports analytics, visualization, and automation. Application layers then translate these insights into operational decisions, such as adjusting traffic signals, managing energy distribution, or optimizing waste collection routes.

Key technologies and standards

The technological foundation of Smart Cities is diverse, reflecting the wide range of use cases and operational requirements.

• Connectivity technologies: LPWAN (LoRaWAN, Sigfox), cellular IoT (NB-IoT, LTE-M), 5G, Wi-Fi, and fiber backhaul networks.
• Edge computing: Distributed processing nodes that enable low-latency decision-making at the network edge.
• IoT platforms: Middleware solutions that manage device connectivity, data ingestion, and analytics.
• Data standards and interoperability frameworks: Protocols such as MQTT, CoAP, and REST APIs for device communication and integration.
• Digital twins: Virtual representations of urban systems used for simulation and predictive analysis.
• Security frameworks: Identity management, encryption, and secure device provisioning to protect urban infrastructure.

Standardization remains an ongoing challenge. While some frameworks exist, Smart Cities deployments often involve legacy systems and proprietary technologies, requiring integration layers and custom development.

Main IoT use cases

Smart Cities encompass a broad set of applications, each addressing specific urban challenges.

• Smart mobility: Traffic management systems use real-time data to optimize signal timing, reduce congestion, and improve public transport efficiency. Connected parking solutions guide drivers to available spaces, reducing emissions and travel time.
• Energy management: Smart grids and connected meters enable dynamic energy distribution, demand response, and integration of renewable sources.
• Environmental monitoring: Sensors track air quality, noise levels, and weather conditions, supporting regulatory compliance and public health initiatives.
• Waste management: IoT-enabled bins monitor fill levels and optimize collection routes, reducing operational costs and emissions.
• Public safety: Surveillance systems, connected lighting, and emergency response platforms improve situational awareness and response times.
• Smart buildings: Connected systems manage heating, ventilation, lighting, and occupancy to improve energy efficiency and user comfort.

These use cases are often interconnected. For example, mobility data can inform environmental strategies, while energy consumption patterns influence urban planning decisions.

Benefits and limitations

The deployment of Smart Cities offers several operational and societal benefits, but also introduces technical and organizational constraints.

Benefits include:

• Improved operational efficiency through data-driven decision-making.
• Reduced environmental impact via optimized resource usage.
• Enhanced citizen services and user experience.
• Greater visibility across infrastructure and urban systems.

Limitations and challenges include:

• Interoperability: Integrating heterogeneous systems remains complex.
• Scalability: Managing millions of connected devices requires robust architectures.
• Security risks: Urban infrastructure becomes a target for cyber threats.
• Data governance: Ownership, privacy, and regulatory compliance are critical concerns.
• Economic viability: Many projects struggle to demonstrate clear return on investment.

Trade-offs are inherent in system design. For example, low-power networks offer extended battery life but limited bandwidth, while high-performance networks increase cost and energy consumption.

Market landscape and ecosystem

The Smart Cities ecosystem involves a wide range of stakeholders, each contributing to different layers of the value chain.

• Device manufacturers: Provide sensors, gateways, and embedded systems.
• Connectivity providers: Telecom operators and LPWAN network providers deliver communication infrastructure.
• Platform vendors: Offer IoT platforms for device management, data analytics, and application development.
• System integrators: Design and deploy end-to-end solutions, often combining multiple technologies.
• Public sector authorities: Define requirements, manage infrastructure, and ensure regulatory compliance.

Collaboration between public and private sectors is essential. Many Smart Cities initiatives rely on public-private partnerships, where investment, risk, and operational responsibilities are shared.

The market remains fragmented, with varying levels of maturity across regions. Some cities adopt comprehensive strategies, while others implement isolated use cases without full integration.

Future outlook

The evolution of Smart Cities is closely tied to advances in connectivity, data processing, and artificial intelligence.

5G and future 6G networks are expected to support more demanding applications, including autonomous mobility and real-time urban control systems. Edge AI will enable faster decision-making directly at the device level, reducing reliance on centralized platforms.

Digital twins are likely to become more prominent, allowing cities to simulate scenarios, predict outcomes, and optimize planning. At the same time, regulatory frameworks around data privacy and cybersecurity will continue to shape deployment strategies.

Long-term success will depend on the ability to move from pilot projects to scalable, integrated systems. This requires not only technological maturity but also governance models that align stakeholders and ensure sustainable funding.

Frequently Asked Questions

What defines a Smart City?
A Smart City uses digital technologies, particularly IoT, to monitor and manage urban infrastructure and services in real time.

Which technologies are most important for Smart Cities?
Key technologies include IoT sensors, LPWAN and cellular connectivity, edge computing, cloud platforms, and data analytics tools.

How do Smart Cities improve mobility?
They use real-time data to optimize traffic flow, improve public transport efficiency, and enable services such as smart parking.

What are the main challenges in Smart Cities deployment?
Challenges include interoperability, scalability, cybersecurity, data governance, and securing long-term funding.

Are Smart Cities only about large metropolitan areas?
No, smaller cities and municipalities can also deploy Smart Cities solutions, often focusing on specific use cases such as energy or mobility.

How does IoT enable Smart Cities?
IoT connects physical assets to digital systems, enabling real-time data collection, analysis, and automated decision-making.
Source: https://iotbusinessnews.com/

Сообщение Smart Cities and IoT: Infrastructure, Mobility and Urban Services появились сначала на SV-Consulting.

The big change with SGP.32 is that it enables remote management of eSIMs in an IoT context, enabling frictionless switching of SIM profiles. This is not to suggest that SGP.32 should be used to enable continuous switching between operators or that it somehow cuts operators out of the equation. It’s not desirable for devices, especially power-constrained ones, to change SIM profiles frequently and operator relationships are still required to enabled connectivity so excessive switching remains counter-productive.

Over-simplification in the market has led to some confusion being created over what SGP.32 can achieve with some hype suggesting that it abstracts away all the complexity of IoT connectivity. The reality is that complexity still remains but SGP.32 provides a way for simpler connectivity to be achieved, utilising eSIMs and supporting technologies such as SGP.32-enabled operating systems, the eSIM IoT Remote Manager (eIM), the embedded IoT profile assistant (IPAe) and the familiar subscription manager data preparation plus (SM-DP+) server from the consumer specification.

The new SGP.32 Buyers Guide has been created by Beecham Research and Wireless Logic to address queries IoT organisations have regarding the specification and highlight when it should be used. Importantly, the Guide also reveals when not to use SGP.32, detailing nine use case scenarios alongside reasons not use the specification. In addition, the Guide features an RFI checklist and answers 53 important questions to be considered in relation to SGP.32.

Intrinsically scalable cellular IoT

“We’re saying that SGP.32 provides a higher level of overall control than has been available before,” explains Robin Duke-Woolley, the Guide author and the chief executive of Beecham Research. “The specification has opened up a management capability to not just remotely provision [connectivity] but also to remotely make changes. SGP.32 moves IoT from the plastic SIM card to a completely different situation with a scalable management solution. We’ve moved cellular IoT from being difficult to scale to something that is intrinsically scalable.”

Approximately four million IoT devices have been shipped with Wireless Logic connectivity and the company manages the ongoing needs of these in addition having deployed around two million SIM profiles over the air to devices which it did not originally provide services to. Those devices are largely utilised to serve remote workforces, lone workers and others in markets across the globe, including challenging connectivity markets such as Brazil, where permanent roaming is not allowed.

“We’ve largely solved the shortcomings of SGP.02 and SGP.22 but we do welcome the SGP.32 specification and, over time, we expect to consolidate around SGP.32,” says Iain Davidson, the head of product marketing at Wireless Logic. “As with the previous specifications, our approach is fully end-to-end from the sourcing of eSIM profiles to the complete lifecycle management of connected devices.”

Hyperscale eSIM orchestration

Davidson sees the need for solutions providers here because of the continuing complexities of managing carrier relationships across the world and the need to enable hyperscale connectivity. “The vast majority of enterprises won’t have the expertise or the appetite to develop our capabilities for themselves,” he confirms. “There will be large or super-large companies who will invest in their own infrastructure and own carrier relationships to have access to their own profiles, but they will need a partner to orchestrate that.”

The Guide also looks ahead to the upcoming SGP.42 specification which will include in-factory profile provisioning (IFPP). This will aid global devices which can ship from the factory with eSIM connectivity embedded regardless of their deployment location, adding further flexibility and simplicity to IoT connectivity.

“That’s all part of the puzzle of managing things at scale remotely,” confirms Duke-Woolley. “With SGP.32 it’s the ability to enable remote management at scale that is of great benefit. It makes the difference in moving cellular IoT forward and without SGP.32 cellular IoT would have found it hard to scale further. It’s an essential part of the cellular IoT marketplace.”

That scalable simplicity will be a critical enabler for large-scale international deployments. “We’ll have an eSIM orchestration function to handle fleet and eSIM profile stock management that enterprises can access via an API,” adds Davidson. “That allows operational profiles to be managed if a device is redeployed or repositioned or if there are changes in network roaming, fees or performance. Drawing on more than ten years of experience we can support customers down to the module AT command level and everything above that. Customers don’t need to get involved in the technical complexities.”

Source: https://www.iot-now.com/

Сообщение Beecham Research and Wireless Logic demystify SGP.32 in new Buyers Guide появились сначала на SV-Consulting.

In sum – what to know:

Hybrid approach – T-Mobile’s new enterprise 5G offerings merge public 5G coverage, based on the only 5G-Advanced network in the US, with local private 5G and edge capabilities.

Market context – while the model is familiar in China, T-Mobile is pioneering the approach in the US, aiming to attract enterprise and industrial verticals with a simpler private 5G system.

Challenges ahead – analysts note the technical potential is strong, but US operators face organizational hurdles; execution, reliability, and enterprise trust will determine whether it delivers.

T-Mobile’s move in the US to offer hybrid private 5G services to enterprises on its 5G-Advanced network should be considered important as a model for how western operators might use their 5G infrastructure to serve enterprises, and also as a signal that T-Mobile will seek to repeat its trick in the consumer space, where it has closed ground on Verizon and AT&T (and leads the live contest), in the much tougher enterprise domain. But that’s just my take. 

What do the experts think? Well, their assessments range from rather doubtful (“an interesting experiment”) to measured applause (“precise and timely”) to kind-of impressed (“ahead of the game”). But let’s recap, because T-Mobile’s announcement last month followed on a trio of well-telegraphed moves around a developing service narrative in the US enterprise market, including its launch of public network slicing and a new ‘cyber defense centre’. 

The first is a commercial proposition, and the second looks as much like a vote-pulling strategic play to inspire confidence, suddenly, that T-Mobile can be trusted with mission-critical enterprise comms. As well, with less PR fanfare, the firm quit the CBRS market in the US – or, at least, withdrew official support for third-party MOCN-based neutral-host solutions. Clearly, and logically, these events are connected, and there is method to its madness.

Its introduction, late October, of a pseudo-virtual private 5G offer called Edge Control – where the control plane is attached to its public core network, and the user plane (UPF) is distributed in, or near, enterprise campuses – is the company’s fourth shape-shifting exercise in a matter of weeks. And suddenly, T-Mobile, the day-glo upstart brand on the US telco scene, forever playing catch-up in the serious-minded enterprise space, looks like a contender, at last.

It does on paper, anyway. T-Mobile claims to have “America’s only 5G Advanced network”, as an inter-generational upgrade on 5G Standalone (SA), still being deployed by its rivals. Its hybrid-private 5G proposition uses slicing to prioritise traffic and local cell sites to somehow direct connectivity across “smaller hops” to slash latency versus just public or public-sliced 5G, and to reduce costs versus all-private 5G products, as offered by the rest of the market.

In parallel, it has launched a management (“visibility and control”) enterprise platform, called T-Platform – “because managing your network shouldn’t be a scavenger hunt”. T-Platform is presented as a “5G portfolio platform” to cover the gamut of enterprise device and connectivity management, including for IoT fleet devices inside and outside of enterprise premises. T-Platform provides “unified management” of its business services, it said.

But how big or important is all of this? Because the model is borrowed from (or properly-modelled in) China, which is home to 55,000-odd operator-provided hybrid-private 5G networks, and commonly used in markets where spectrum has not been made directly to enterprises to own and manage their own ‘standalone’ 5G systems – such as India, where the carriers control everything, the market is stymied, and the subject is fraught with intrigue. But it is new 

“There is nothing proprietary in what T-Mobile is doing; it is all in accordance with 3GPP standards. The Chinese market used this approach since standalone private wireless networks were not permitted,” says Dave Bolan, research director at Dell’Oro Group, as a matter of order. Which is very plain, of course; but T-Mobile is the first to do it in the old ‘west’ – at least, with such a bright infrastructure palette and such a grand geographic canvas. 

And so its significance, even just regional, might be noted. Bolan says: “The timing is good with 5G-A enabling more capabilities and RedCap [also] reducing the cost of IoT 5G SA connectivity.” He presents a juxtaposition, here, about 5G development: network capability on one side, hardware optimisation on the other – where reduced-capability 5G (5G RedCap) enables cheaper cut-down versions of full-fat 5G devices, even as carrier networks are more powerful. 

It provides helpful context. Joe Madden, founder and chief analyst at Mobile Experts, reflects: “T-Mobile is ahead of the game – as it seems clear that all operators will be moving into a combination of network slicing and private 5G. It is useful to have the right solutions from an architectural point of view, early in the game.” He goes on to suggest the firm is making “some smart moves” on the grounds the broad enterprise market is fragmented, and needs options.

He says: “Some verticals – gaming, automotive, public safety – will align with the slicing approach; others are focused more on control of data. Manufacturing, say, overwhelmingly prefers to have a dedicated private 5G radio on-premise, together with edge computing to control the flow of data. T-Mobile is offering both network slicing and a local UPF-based private 5G network, as two opposite approaches to capture distinctly different customer groups.”

He adds: “Big companies have kicked off the private LTE/5G market, because they can hire teams of engineers and implement complex systems. Over time, private 5G will get simpler and easier to consume for smaller companies. But they will [also] be more aligned with slicing solutions – so we’re forecasting a trend for slicing in some markets as early-adopter giants like John Deere and Bosch are eclipsed by large numbers of mom-and-pop companies.”

T-Mobile might be offering two approaches in the US, but there is a third – a non-hybrid private-private 5G system, completely separate from the public network. And actually, most big manufacturing firms outside China, which have tended to adopt private-5G proper until now, want this third option – which T-Mobile is not offering, or seen to be offering (in the same way as Verizon, notably, which is standing up on-prem private 5G in international markets). 

Dean Bubley, founder at Disruptive Analysis, says: “It is an interesting experiment, maybe more suitable for some verticals – maybe retail, say, or others that are multi-site, and want a cookie-cutter approach, and don’t need full private 5G at every location. It’s all very well having local-breakout for the data plane, but no major industrial site is going to want to risk a complete shutdown if there’s an outage, or a fibre cut in the backhaul link.”

It is rhetorical, but he asks: “What are the SLAs and insurance policies if downtime costs thousands per minute?” And zooms-out, and suggests not to over-play its importance. “It is a part of the puzzle. T-Mobile is trying to reclaim the ‘slicing’ story for private 5G. [But] I think most operators don’t really care – they’re starting to view private 5G as something that belongs to their units, which can be deployed as a separate project, especially for major sites.”

He raises the idea, briefly, of “semi-private RedCap over wide areas” – which is likely a piece of this particular T-Mobile puzzle, anyway, whether with its private or sliced 5G products. Meanwhile, Leo Gergs, principal analyst at ABI Research, picks up on the architectural aspect of T-Mobile’s enterprise strategy – namely, its merger of nationwide public 5G coverage with local private or edge capabilities, as a shift from standalone private models.

“T-Mobile’s entry into private 5G is a precise and timely move into a space that has been dormant for years. The enterprise edge was largely forgotten until the rise of AI inference revived demand for localized compute and secure connectivity. [The hybrid combination of public and private infrastructure] addresses the new need for distributed AI processing without the integration and spectrum friction that crippled earlier private network efforts.”

He goes on: “Hybrid networks hold enormous promise: they deliver the flexibility and scale enterprises want with the control they need. Yet, no operator has managed to make deployment simple enough to spark genuine enterprise interest. But it comes down to execution: how well T-Mobile can translate telco jargon into tangible business outcomes. If it can do that, it might finally prove that operators can play a meaningful role in the enterprise edge.”

High stakes, then. “If not, this may be the last real chance for carriers to claim any slice of the enterprise connectivity market,” he says. Even higher, perhaps.  

Madden also raises some doubt, echoing Bubley’s earlier comment about organisational structures, and just the headache of complex problem solving and systems integration in the enterprise market. “The other operators will also develop localized private networks and network slicing for each vertical. [But] progress is frustratingly slow because the US operators are all organized around selling ‘smartphone plans’, not selling enterprise services. 

“The biggest challenge is not technical, it’s an organizational challenge for them to break out of their rigid organizational structure and offer more nimble solutions to the business community.”

Source: https://www.rcrwireless.com/

Сообщение ‘Ahead of the game’? Has T-Mobile just stolen a march in the US enterprise 5G market? появились сначала на SV-Consulting.

In sum – what to know:

Boardroom talk – once limited to ad-hoc pilots, AI is now embedded in manufacturing strategy, governance, and performance frameworks.

Practical value – automated optical inspection is the top industrial AI use case, delivering big ROIs for manufacturers like Renault and Georgia-Pacific. 

New foundations – vendors such as Siemens are building domain-specific gen AI models, while early agentic AI systems hint at self-optimising factories.

Note: this article is the third in a short three-part series about the new tech foundations of Industry 4.0: industrial 5G (private wireless), industrial IoT, and industrial AI – linked but distinct technologies that form the connective, sensory, and cognitive layers of modern industry, driving digitalisation in hard-nosed industrial environments. Here is part three, based on figures from IoT Analytics, with some market-sizing about the state of the industrial AI market. Part one (5G) and part two (IoT) are available here and here. 

Here is a quick run-through of an excellent (400-page) paper on the state of industrial AI by Germany analyst house IoT Analytics; it goes like this… The global industrial AI market was worth $43.6 billion in 2024, with compound growth (CAGR) pegged at 23 percent per annum through 2030 – when it is expected to be worth $153.9 billion. The new growth is because of the buzz about generative AI. But two things: industrial AI spending only represents 0.1 percent of corporate industrial revenue, but most manufacturing firms now have a CEO-driven AI strategy. 

The average US manufacturer made $30.5 million in 2024, estimates IoT Analytics. All of them together spent over $10 billion on industrial AI in 2024. This translates to an average of roughly $40,000 per manufacturer, it says – which is about 0.1 percent of average revenue, three percent of average R&D spending ($1.56 million), and seven percent of average IT spending ($610,000). Larger companies spend more on AI than smaller companies. A significant portion of industrial AI spending is allocated to consulting and system integration services. 

The top-earning AI services vendor is Ireland-based Accenture, which announced a $3 billion three-year investment in late 2023, and claimed 2,000-odd generative AI projects in 2024/2025 (fiscal; ending August 31). Other major suppliers – “in a fragmented services market” – are India-based Infosys and UK-based Deloitte. On the flipside, the top industrial AI user is Japanese auto manufacturer Toyota, which invested 1.7 trillion yen ($10.6 billion) during (its fiscal) 2025, including on front-line ML models, OT know-how digitisation, and analytics for safety and productivity.

When IoT Analytics polled execs at big manufacturers in 2021, AI was hardly on the radar, rarely appearing in more than “ad-hoc exploratory projects”. It is different today: most leading manufacturers have dedicated AI strategies, which are “vision-driven, supported by governance frameworks, performance targets, and integration with broader business objectives”, says IoT analytics. “This marks a significant cultural and structural shift, elevating AI from a peripheral technology investment to a top-of-mind discussion point for CEOs during earnings calls,” it notes.

But generative AI, so hyped-up, is way down the list; camera AI cases for quality inspections are way ahead in Industry 4.0. IoT Analytics states: “Of the 48 industrial AI use cases [we have] analyzed… automated optical inspection [is]s the leading one with a share of approximately 11 percent. For comparison, all the gen AI cases combined currently account for less than five percent of the market – with coding being the largest at one percent.” This is likely because the ROI proof (“nine-digits in savings and value gained”) is clearer with other AI cases.

IoT Analytics says: “While the financial community grapples with [the] AI bubble, and some outlets report 95 percent failure rates for enterprise AI pilots, many industrial AI projects have already proven their value through measurable cost savings, uptime improvements, and quality gains. In 2023, IoT Analytics noted that machine vision had the highest ROI and quickest amortization time of all Industry 4.0 technologies at that time, with AI-assisted flaw detection and process/operations optimization as the top rising machine vision applications at the time.” 

It references a couple of case studies: car maker Renault SA saved €270 million on its energy and maintenance bills back in 2023 with predictive (AI/IoT) maintenance; pulp and paper company Georgia-Pacific claims to have saved (“annual value capture”) hundreds of millions through its AI projects – likely, mostly IoT analytics, but also via a generative AI document generation tool called ChatGP. And while generative AI represents only a scrap of industrial AI activity, the interest is palpable, and the projects are growing; IoT Analytics has a repository of 530 of them, it says.

These are being used for issue resolution (35 percent of projects), inquiry handling (34 percent), and post-sale support (19 percent). Marketing ( content-creation; 17 percent) and IT (development and coding; 15 percent) are also popular disciplines. It says: “In the manufacturing sector, issue resolution and coding support have become particularly important. Applications like these have helped gen AI… to become a leading industrial AI development.” Generative AI will comprise a quarter of industrial AI projects by 2030, from six percent in 2024.

IoT Analytics says: “Common use cases for gen AI in industry include operations and service support (documentation querying and troubleshooting) and code generation for OT and embedded assets. But it is also increasingly used across the entire manufacturing value chain, including in R&D (product discovery), design (generative design), engineering (gathering requirements), and field service (guided maintenance). At this point, manufacturing rollouts have largely been driven by industrial software vendors in the form of copilots in industrial software.”

It cites copilot integrations from Siemens, Rockwell Automation, and ABB.

There is an issue with industrial foundation models (LLMs), however. “Some manufacturers who have tried to build assistants and copilots with LLMs from the likes of OpenAI, Google, or Anthropic have seen limited understanding in industrial environments. Since many of the valuable industrial data points that are required to train an LLM do not reside on the public internet, some industrial tech vendors have started to build purpose-built industrial foundation models (IFMs) that aim to “speak the language of engineering,” and are trained on domain-specific data.

It cites examples from Siemens (Industrial Foundation Model), Google (Gemini Robotics), Nvidia Isaac GR00T N1), and others. 

Beyond generative AI, talk about industrialised agentic AI is early. “While many industrial software vendors began prominently featuring the term agentic AI in their messaging in 2025, deployment is still in its infancy,” says IoT Analytics, with a review of its findings at Hannover Messe 2025, where most showcases demonstrated “only basic orchestration capabilities” – except for Accenture, whose ‘engineering orchestrator’, for modifying engineering designs using natural language, was the one bright spot (the “one promising showcase”.)

It says: “The agentic engineering chatbot serves as a control layer on top of existing tools, interpreting user prompts and executing design changes across multiple tools – like Siemens NX, Siemens Polarion, Altair Hypermesh, and Altair HyperView.” As well, it suggests dynamic AI agents will replace static rules in industrial manufacturing execution system (MES) setups, as offered by the likes Portugal-based MES vendor Critical Manufacturing – to “adapt, learn, and optimize production in real time”. More to come for sure, and fast.

Source: https://www.rcrwireless.com/

Сообщение Three sides of Industry 4.0 – industrial AI in numbers (part 3) появились сначала на SV-Consulting.

In sum – what to know:

Faster adoption – large-scale IoT deployments (10,000+ devices) are expected to more than triple next year, with most enterprises anticipating ROI within 12 months.

Parallel tech – industrial AI and 5G are driving IoT momentum; most enterprises see AI as key to IoT success, and over half plan to use private 5G in the next two years.

Industrial scope – from manufacturing and logistics to healthcare and energy, IoT is becoming the essential data engine for Industry 4.0 – if it wasn’t already.

Note: this article is the second in a short three-part series about the new tech foundations of Industry 4.0: industrial 5G (private wireless), industrial IoT, and industrial AI – linked but distinct technologies that form the connective, sensory, and cognitive layers of modern industry, driving digitalisation in hard-nosed industrial environments. Here is part two, based on figures from Verizon Business, with some market-sizing about the state of the IoT market. Part one (5G) and part three (AI) are available here and here. 

RCR Wireless always said this about ‘the internet of things’ – that the terminology is useful as a concept, but kind of hard-to-pin-down as a technology. Because really, in the end, these are lots and lots of internets-of-things, and really just private networks of them, whether they are standalone sensor devices or embedded sensor modules, integral to much bigger and more-complex things – like robots or cars. And if they are ever connected to the internet in the cloud, their data is never supposed to be shared outside of strict corporate boundaries. 

So yeah, brilliant concept, completely useful, but impossible to talk about in isolation – of industrial networks (5G, say), industrial analytics (AI). But IoT is “moving to centre stage”, says Verizon Business. 

The firm has a new report about IoT, based on a survey of 500 enterprises, which covers it all, and says how closely these technologies are related – IoT data on 5G networks to AI software for corporate ends. “IoT a smart bet, AI a major accelerator,” it concludes, finding “soaring enthusiasm” for IoT and “multiple emerging technologies acting as catalysts”. Verizon Business writes: “5G, private networks, and eSIM are cited as cornerstones of enterprise IoT infrastructure. RedCap, slicing, and satellite are priorities for many.”

So what do the stats say? Firstly, they say that big enterprises like IoT. Deployments of over 10,000 devices will more than triple (!!!) next year, apparently – among respondents, if their plans pan out. They also say that IoT works in ROI-terms, and is driving more investments in it. More than half cite efficiency and productivity gains; almost everyone (98 percent) expects “real benefits” within two years. “Most anticipate returns in less than 12 months,” says Verizon Business. I mean, if these numbers are reflective, has there ever been such unbridled enthusiasm for IoT?

Maybe it’s because everyone loves AI – and because AI makes private IoT smart. Eight-four percent of enterprises think AI is key for IoT, and 70 percent have accelerated their IoT deployments accordingly; 82 percent have combined AI with camera (just an IoT-thing) data for real-time decision-making. Most (87 percent) say systems integration support (“from a mobile network operator”) is “important or critical” for IoT, and half (52 percent) intend to use private 4G/5G networks for their IoT projects in the next 12-24 months – a 24 percent jump “over current usage”.

Some other interesting stats: three quarters (74 percent) of companies plan to adopt reduce-capability 5G (5G RedCap); the same percentage expect to integrate satellites into their IoT roadmaps; more (78 percent) see network slicing as essential to customizing IoT performance; and eSIM adoption is surging (up 240 percent on the company’s own ThingSpace platform). A notable proportion (43 percent) say cybersecurity is their biggest IoT hangup, likely also accelerated by AI. There are some choice pull-out stats by vertical discipline below.

Daniel Lawson, senior vice president of global solutions and IoT at Verizon Business, said: “IoT is a data powerhouse. An enterprise’s connected machines, IT systems, and overall operations produce an enormous amount of data – location, condition, anomaly, threat, operational progress and continuity, and so on. IoT is how that data gets collected and parsed to provide businesses with insights about how things are going and how they can be improved. This survey tells us that IoT is highly valuable and scalable, when implemented correctly, and is about to get a lot more interesting and dynamic with AI and other emerging technologies.”

Source: https://www.rcrwireless.com/

Сообщение Three sides of Industry 4.0 – industrial IoT in numbers (part 2) появились сначала на SV-Consulting.

Private 5G continues to outpace the broader telecoms market, with enterprise deployments expanding and industrial use cases taking shape. New data from Dell’Oro Group shows sustained almost-double-digit growth and a gradual shift in focus – from enterprise connectivity experiments to production-grade industrial applications.

In sum – what to know:

Outlier-outrunner – revenue from private 4G/5G systems grew 40% in 2024 and is tracking 20% higher again in 2025, compared with flat growth for public RAN.

Industrial momentum – adoption is accelerating beyond China and into critical industrial environments – notably manufacturing, logistics, mining, and energy.

Concentrated market – the top five RAN vendors are Huawei, Nokia, Ericsson, Samsung, and ZTE; integration and scale requirements favour established suppliers.

Note: this article is the first in a short three-part series about the new tech foundations of Industry 4.0: industrial 5G (private wireless), industrial IoT, and industrial AI – linked but distinct technologies that form the connective, sensory, and cognitive layers of modern industry, driving digitalisation in hard-nosed industrial environments. Here is part one, based on figures from Dell’Oro Group, with some market-sizing about the state of the private 5G market. Part two (IoT) and part three (AI) are available here and here. 

Global revenue from the sale of private 4G and private 5G radio access network (RAN) systems to enterprises grew by nearly 40 percent last year (2024), according to analyst Dell’Oro Group. That momentum has “extended” through the first half of 2025, it said, and revenue growth will be about 20 percent up on 2024 (on 40 percent growth on 2023) by the end of the year. Private wireless continues to shine as a bright spot in the telecom market,” said the firm.

Private RAN sales are outrunning “flattish” sales in traditional public cellular networks, operated by big operator groups, mostly for consumers. Private 4G/5G now represents a “mid-single-digit share” of total RAN sales, said Dell’Oro Group. Adoption is “accelerating beyond China”, it said, and “toward industrial applications”. The temptation, here, is to translate that private 4G/5G is being engaged more closely in operational technology (OT) in Industry 4.0. 

The sense is that private cellular has found its métier in industrial environments – in manufacturing, logistics, oil and gas, mining – where it has been mostly deployed, initially, for worker safety and camera analytics, adjacent to production systems. But Dell’Oro Group does not conclude, actually, that private 4G/5G is now being deployed into precise critical operations; it reckons, instead, that the market has ‘pivoted’ from traditional enterprise environments” 

It stated: “The market remains highly concentrated. The same five leading public RAN suppliers continue to dominate the private RAN landscape. This overlap reflects the market’s pivot away from traditional enterprise environments toward industrial applications, where scale, performance, and integration requirements favour established vendors.” Huawei, Nokia, Ericsson, Samsung, and ZTE (listed in that order) are the top suppliers, it said.

Growth in the first half was driven by both local-area (campus) and wide-area deployments; the latter led the growth, it said. China was the largest market by revenue, but growth outside of China is accelerating at a faster pace. The top suppliers by total revenue this year are Huawei, Nokia, and Ericsson; outside of China, The top three are Nokia, Ericsson, and Samsung. Huawei leads for wide-area deployments in the period, Nokia for local-area deployments.

Dell’Oro Group described the market as a “still-untapped, high-growth opportunity”. Total private 4G/5G RAN revenues have “accelerated rapidly” in the first half of 2025, it said, going from a “low single-digit share in 2022” to a “mid-single-digit share” of total RAN revenues today. Growth is “tracking closely” with the trajectory of enterprise Wi-Fi adoption during its first five years. It will account for five-to-10 percent of total RAN by 2029; public RAN is expected to decline at a two percent CAGR over the same period.

It stated: “The high-level message has not changed – private wireless is a massive opportunity. Still, it will take some time for enterprises to embrace private cellular technologies.” Dell’Oro Group has a report all about it.

Source: https://www.rcrwireless.com/

Сообщение Three sides of Industry 4.0 – industrial 5G in numbers (part 1) появились сначала на SV-Consulting.

In sum – what to know:

Global telecom market rebounds 4% – After two years of decline, revenues rose in 1H25 thanks to inventory stabilization, currency tailwinds, and easier y-o-y comparisons.

Recovery led by EMEA, NA – Stronger demand in these regions offset weakness in Asia-Pacific, particularly in optical and core network segments.

Huawei strengthens position – The vendor’s market share has grown about three points since U.S. restrictions began, sustaining growth where it remains active.

After two consecutive years of declining investment, the global telecom equipment market showed signs of recovery in the first half of 2025 after two consecutive years of declining investment, according to preliminary data from Dell’Oro Group.

The report indicates that aggregate worldwide revenues across six major segments — broadband access, microwave and optical transport, mobile core network (MCN), radio access network (RAN), and service provider router and switch — grew by 4% year-over-year in the first half of the year.

Dell’Oro noted that the rebound was chiefly fueled by a combination of easier year-over-year comparisons, stabilized inventories, and favorable currency movements. The recovery was broad-based across all telecom programs, with optical transport, mobile core, and routing leading the gains.

“The broader market was boosted by favorable developments in EMEA and North America, which helped to offset more challenging conditions in the APAC region,” Stefan Pongratz, vice president at Dell’Oro Group, told RCR Wireless News.

Market conditions outside China were particularly strong, with revenues climbing 8% year over year during the first half of 2025. While overall global supplier rankings remained largely unchanged, revenue shares shifted modestly. Huawei continued to strengthen its position, while Ericsson and Nokia saw slight declines compared with 2024.

“Regarding Huawei’s share gains, we estimate that its overall revenue share has increased by roughly three percentage points since efforts by the US government to curtail the company’s growth began. While there is no question that its TAM has contracted, Huawei has delivered remarkable results in the markets where it remains active,” Pongratz added.

Following the stronger-than-expected first half, Dell’Oro revised its short-term outlook upward. The firm now expects total telecom equipment revenues across the six tracked segments to grow between 2% and 3% in 2025, compared with a flat forecast in its previous update.

Global telecom capital expenditures stabilized in the second quarter of 2025 after two years of reductions, according to a previous report from Dell’Oro Group.

The Dell’Oro report notes that while Q2 showed stabilization, total first-half 2025 capex was still down year-over-year due to steep Q1 declines. Overall, the investment environment is expected to remain challenging for both capex and telecom equipment revenues, it added.

By 2029, capex-to-revenue ratios are projected to approach 15%, about three points lower than the 2022 peak. Wireless capital intensity is forecast to reach 12–13% by 2029, down five to six points from the 5G rollout highs.

Source: https://www.rcrwireless.com/

Сообщение Telecom equipment market rebounds: Dell’Oro появились сначала на SV-Consulting.

The company has also opened an office in the country after securing a license to use S-band spectrum for non-terrestrial 5G IoT services from its constellation of 10 small satellites, founder and CEO Omar Qaise told SpaceNews.

The Australian approval follows the launch of OQ Technology’s LEO services in “countries such as Saudi Arabia, Rwanda, Nigeria and many more,” Qaise said via email.

Qaise pointed to soaring demand for space-based connectivity in the oil and gas, agriculture and mining sectors in particular, where terrestrial coverage is often limited, alongside rising needs from government and logistics customers.

He said OQ Technology delivers services through a mix of its own licensed spectrum and more than 20 roaming agreements with terrestrial telcos worldwide. 

Dutch telco KPN is the latest telco to announce an agreement with OQ Technology, adding the venture to its roster of space-based partners to enable IoT coverage across land and sea.

Enterprise customers include Aramco, Saudi Arabia’s state-owned oil and gas giant.Earlier this year, OQ Technology secured funds from a European Union-backed accelerator to help enable direct-to-device (D2D) connectivity from space to mass-market smartphones. 

Qaise said the operator aims to launch a D2D text messaging service by the end of 2026, joining a market currently dominated by U.S.-based players such as SpaceX.

He said “being a European LEO operator with NTN IoT and D2D capability and standardized spectrum, [OQ Technology] can be the European sovereign LEO D2D operator [unlike] other players.”

OQ Technology ultimately plans to operate a constellation of 82 satellites in LEO to provide global 5G IoT and D2D services at scale.

Source: https://spacenews.com/

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The research was conducted by a team led by Northeastern University with the participation of IMDEA Networks, the Technical University of Berlin, the University of Porto, the University of Oslo, the Polytechnic University of Turin, the Technical University of Denmark, and Hewlett Packard Labs. Over the course of a year, experts studied the performance of mobile networks in eight major cities.

The geography of the study covered the world’s largest technology centers. The list included Berlin, Turin, Oslo, Porto, and Madrid in Europe, as well as Vancouver, Boston, and the San Francisco Bay Area in North America. This selection of cities helped to obtain a representative picture of 5G deployment in developed countries.

The methodology combined a broad survey of volunteers with technical measurements of millimeter wave usage. This approach made it possible to obtain both general statistics and detailed technical indicators of the performance of next-generation networks.

The results contradict the marketing promises of telecom operators. Researchers found significant differences in service quality depending on the specific operator and geographic location. Some networks showed excellent 5G uplink speeds, while others were almost indistinguishable from Long Term Evolution (LTE) technology.

The most surprising results were those related to signal latency. Despite the widespread deployment of 5G infrastructure in large cities, it has not been possible to achieve a consistent reduction in latency compared to fourth-generation networks. In many cases, the new standard does not offer any obvious advantages in terms of system response speed.

The reasons for this situation lie not so much in the technological limitations of 5G as in the decisions of the operators themselves. The key influencing factors were the choice of frequency band, the density of base station deployment, and the use of cloud and edge infrastructure. These parameters vary significantly between different companies and regions.

The results of the study are of practical importance for users and application developers. For many delay-sensitive services, the transition to 5G does not guarantee automatic performance improvement. Decisions regarding the use of mission-critical applications should be based on actual measurements, not just claims about the novelty of the technology.

These data also raise questions about the future development of mobile communications. Researchers caution against premature transition to sixth-generation (6G) technology. There is a danger of wasted capital investment and unfulfilled public expectations due to irrational resource allocation.

The authors of the study emphasize the need to focus on the real user experience before introducing the next generation of mobile communications. Policy and investment in 6G should be based on transparent and reproducible results, rather than optimistic industry forecasts.

Summarizing the results of their work, the researchers conclude that 5G networks in the regions studied are conditionally mature. Although the infrastructure has been deployed, consistently high performance remains out of reach for most operators.

Translated with DeepL.com

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Heritage Petroleum, the largest oil producer in the Caribbean, used Cogent DataHub software in a recent project to modernize their SCADA systems and integrate new IoT-enabled well-monitoring devices. The project integrated HiberHilo sensor data equipment with AVEVA InTouch, Historian, and Web Client over LoRaWAN networking.

The project team at TOSL Automation and Technology Limited (TATL) collaborated closely with AVEVA partner Incentro S.A. for software support and Soati for system integration expertise. Using DataHub software, they performed a system-wide upgrade instead of rip-and-replace, which dramatically cut hardware costs and saved months of time.

“This project was unique,” said Aneesa Mohammed, ICE Engineer at TATL. “It wasn’t just plug and play. The data coming in from the gateways was in different JSON formats for several different types of sensors and devices. The DataHub software had to take this information, process it, and send it to InTouch in a way that would make sense. The Skkynet support team helped us build out custom scripts for JSON conversions.”

In addition to seamless MQTT connectivity, the TATL team used the DataHub MQTT Smart Broker to enable sophisticated redundancy switchovers based on MQTT quality of service.

“Heritage was looking for modernized dashboards, increased availability, user friendliness, stability, and security. The MQTT connections improved analytics, alarm management, integration with third party applications, and the ability to scale and add more devices. All of this was achieved by using DataHub software,” said Mohammed.

Source: https://www.ien.eu/

Сообщение Integrating SCADA and IoT появились сначала на SV-Consulting.