Winning in IoT: How the enterprise IoT market is evolving

 According to our January 2023 IoT market update, IoT enterprise spending reached $201 billion in 2022, up from below $100 billion in 2018. For comparison, this represents roughly 5% of the global IT market in 2022. While we had to lower the outlook for the forecast period of 2023–2027 in our January 2023 update, the market is still expected to grow by +19% annually and reach $483 billion in spending by 2027.

IoT technology in 2023 adds value to organizations in all verticals, from manufacturing (e.g., factories) to retail (e.g., warehouses) and transport (e.g., cars). The IoT market has moved past headlines such as the infamous “3/4 of all IoT projects fail” (Cisco 2017).

87% of all IoT projects meet or exceed expectations

In 2023, 87% of all IoT projects met or exceeded expectations, based on a 2023 survey of 300 IoT decision-makers that will be published in an upcoming IoT Analytics adoption report. Some companies have connected millions of connected IoT devices (e.g., Walmart, Tesla, and Hapag-Lloyd) and are looking to expand with more sophisticated software tools. Despite several key challenges remaining related to interoperability, skills and know-how, and chipset supply, companies do not question if they should do IoT but rather how it will be scaling from here.

IoT technology maturity framework

To understand how the IoT tech stack is changing and where the growth opportunities in IoT are going forward, one needs to consider a typical technology-focused maturity curve an IoT adopter goes through:

Stage 1: Enabling the asset

In the first stage, whether it is a smart washing machine, a heavy asset in a factory, or a ship at sea, companies need to invest in sensors and local controllers/gateways to be able to process IoT data. Despite a renewed edge computing investment cycle that is seeing many companies invest into more powerful and flexible hardware, many companies at this point have passed the first hurdle of ensuring they have basic IoT data to work with. We expect the spending for IoT hardware/devices to be the lowest growth category at 14% until 2027.

Stage 2: Establishing connectivity

In the second stage, enterprise end users establish and simplify the connectivity to their IoT hardware. While some technologies used have been around for decades (e.g., certain field buses), companies in recent years have invested heavily into higher bandwidth connectivity (like ethernet), wireless connections (e.g., 4G/5G and LPWAN), and more modern and lightweight protocols (e.g., OPC-UA and MQTT). Spending on connectivity is expected to grow by 18% until 2027.

Stage 3: Creating the software backbone

Data normalization and analysis are key to the third stage of IoT maturity. Companies invest in the software backbone that allows them to access various IoT data sources and build valuable services, e.g., using cloud storage and platform services, centralized data lakes, containerization, and modern databases. Many companies are currently in a major investment phase in this part of the maturity curve. That is why we expected spending related to IoT Platforms and middleware to grow 30% and 34% for Infrastructure as a Service (IaaS) until 2027.

Stage 4: Building value adding IoT applications

In the fourth stage of IoT maturity, IoT end users build cloud native or edge-based applications that make use of IoT data at scale. The ability to connect to any asset (stage 1) in a standardized fashion (stage 2) and having those data easily accessible (stage 3) enables a number of IoT use cases. Some of the early innovators (e.g., several automotive OEMs) have reached this stage and are building all kinds of internal (e.g., for their factories) and external (e.g., for their cars) IoT applications. We expect more companies to reach this stage of maturity in the coming years, which is why we expect the spending on IoT applications to exhibit a CAGR of 29% until 2027.

Stage 5: AIoT = infusing AI into IoT

Enabling business with AI is the fifth stage of IoT maturity. This is where companies explore ways to augment existing applications and build new applications by embedding AI. Machine vision and predictive maintenance are two of the most common AI-enabled IoT use cases today. Recent breakthroughs in generative AI may add a new dimension and are a driver for companies to rapidly adopt AI further.

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The pros and cons of AI and IoT

 IoT has revolutionised how we interact with technology and the world. It has created a network of interconnected devices that share data and insights, making our lives more efficient and convenient. As a result, IoT has become an integral part of our daily routines, ingrained in logistics networks, supply chains, smart cities, and much more.

While IoT has already significantly impacted our lives, integrating AI into IoT systems is the likely next step in its evolution, with its potential to help IoT systems become more efficient and effective. But is the autonomy and instant decision making, in what is essential a black box a cause for concern?

Let’s explore both sides of the coin, starting with the positive aspects of AI in IoT.

Benefits of AI

There are many ways in which AI has the potential to revolutionise IoT. Firstly, AI can process and analyse vast amounts of data generated by IoT devices more efficiently and effectively than traditional methods. Using machine learning algorithms, AI can identify patterns, derive insights, and make predictions based on the data collected from IoT-connected devices. This enables organisations to extract valuable information and act proactively.

Taking this a step further, AI could take over that decision-making process and implement new strategies or approaches based on changing data, conditions, and reactions without human intervention. This can increase efficiency, reduce human error, and improve productivity across various applications such as smart homes, industrial automation, transportation, and healthcare.

These better decisions will positively impact energy usage in IoT systems. Furthermore, by analysing data from sensors and devices, AI can identify energy consumption patterns and optimise efficiency. For example, in a smart building, AI can automatically analyse occupancy data to adjust heating, cooling, and lighting systems, resulting in energy savings. Meanwhile, by improving predictive maintenance of IoT-connected devices, AI can reduce downtime, optimise performance, improving overall equipment reliability.

A final promising opportunity for AI and IoT is in Edge Computing, which has been a topic of interest in IoT for some time now. Since AI can be deployed at the edge, it is possible that real-time decision-making could be enabled, and the need for constant data transmission to the cloud could be reduced. This would improve latency, bandwidth usage, and privacy while enhancing the overall efficiency of IoT deployments.

Challenges of AI

While AI brings numerous potential benefits to B2B IoT applications, some concerns and challenges must be addressed. First and foremost, data privacy and security, due to the amount of sensitive data being collected and processed by a technology which has full autonomy and yet is completely hidden from sight of humans.

Companies must ensure adequate measures to protect data from unauthorised access, breaches, and misuse. In addition, there is a clear need to improve the transparency of the decision-making processes of AI, including the ability to take back control and/or reverse decisions, so not to lose control over the system.

The reliability and accuracy of AI algorithms in B2B IoT applications are of utmost importance. Incorrect or unreliable AI predictions can have significant consequences, especially in critical healthcare, transportation, and manufacturing applications. Therefore, ensuring the accuracy and robustness of AI models, along with rigorous testing and validation, is essential to maintain trust and confidence in B2B IoT systems.

Of course, integrating AI with existing IoT systems can be complex and challenging. For example, B2B organisations may already have established IoT infrastructure, and integrating AI capabilities into these systems requires careful planning and implementation. In addition, compatibility, scalability, and interoperability issues may arise when integrating AI algorithms into various IoT devices and platforms.

There are also a couple of elephants in the room regarding AI implementation. Firstly, there is a concern about the shortage of AI experts and data scientists who can develop, deploy, and maintain AI systems effectively. As a new technology, organisations must invest in training programs and provide resources for upskilling employees to bridge this skill gap.

The second is regulation. There are numerous calls from governments, enterprises, and even the godfathers of AI to get regulation in place rapidly. As a result, any AI implemented into IoT applications today may well be subject to legal challenges tomorrow. Compliance will be essential, but right now, it will take a great deal of future-gazing to anticipate the likely regulations that come to pass.

Conclusion

Over-reliance on AI could lead to situations where humans lose control or understanding of underlying processes. This can result in unintended consequences, such as systems behaving unexpectedly or failing entirely.

As such, any AI implementation in IoT systems must be designed with human oversight and intervention to ensure that humans retain control over the technology. Additionally, it is essential to have fail-safe mechanisms in place to prevent such unintended consequences.

Overall, there are clear opportunities in integrating AI into IoT systems, I believe we must approach with caution, and take steps to bring AI into the world of IoT in a considered manner.

While in the past moving fast with new technologies has been a strong move to make, moving fast with a self-learning, autonomous technology carries greater risk, and is worthy of a little more caution.

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Everything Is Connected: Five IoT Trends Moving Forward

 Each year, people’s lives become increasingly connected. Digital assistants, smart homes and the like were only possible in the realm of science fiction not too long ago. Consumers are starting to see the Internet of Things (IoT) expanding into more and more aspects of their daily lives. It is fair to say that the IoT has become ubiquitous to the point where people no longer question the addition of connectivity and smart features to devices.

From an enterprise perspective, IoT sprawl has created unique challenges and opportunities which companies will have to either overcome or embrace. As workers increasingly rely on connected devices and technology makes its way into more unexpected places, companies have to balance the wants of the consumer and employees against the needs of the shareholder. To that end, here are five enterprise IoT predictions for the year ahead.

While Environmental Social Governance (ESG) has been a political flashpoint recently, stewardship of the planet isn’t going to disappear from the boardroom radar in the near future. Companies will begin to take a hard look at their ESG initiatives and realize that the first step toward managing an effective ESG program is understanding the nuances of what makes up their total carbon footprint. To do this, they will need data. Not just data on their own operations, but data on the operations of their suppliers and vendors.

The IoT also provides valuable data to these companies in their efforts to minimize their overall impact on the environment. The next trend in ESG will be leveraging data from the IoT to refine products and processes to have a positive effect on ESG initiatives. IoT devices can provide a constant positive feedback loop where, for instance, when leveraged with machine learning (ML), predict and implement the most efficient control of a device. This need for connectivity amongst IoT devices brings us to the next two major predictions for 2023.

Connecting to a satellite is no longer constrained to specialty or luxury devices. Anytime that there is a need for a connection from a stationary device, satellites may provide easy connectivity while simultaneously shifting some of the load to different frequency bands. While the promise of nationwide mobile technology is a promise, there are still remote areas where satellite communications are vital.

Smart cities are poised to be one of the largest adopters of this shift to satellite communications, particularly with new Satellite networks being launched with support for standard 3GPP cellular connectivity, making them a cell tower from the sky. Building smart city IoT around satellite communications eliminates the need for either costly hard-wired IoT networks or increased local cellular congestion. There will also be a major shift on the cellular side of communications.

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Why IoT users should be asking more from their network

It’s well documented that the Internet of Things (IoT) is central to the convergence of the digital and physical worlds. But while the potential is considerable, realising it has proved a challenge.

By Michael Karlsen, CEO @ Onomondo

It’s well documented that the Internet of Things (IoT) is central to the convergence of the digital and physical worlds. But while the potential is considerable, realising it has proved a challenge.

Many barriers to the adoption and scaling of projects exist today, but the major challenge has been the lack of interoperability across the ecosystem, and more specifically the siloed and fragmented nature of the connectivity pillar.

Traditional connectivity has served a purpose

By its very nature, IoT requires interoperability between its three constituent parts – hardware, connectivity, and cloud – but current technology stacks across these areas have remained fragmented.

Connectivity is the defining element of IoT, enabling seamless communication and data exchange between devices and unlocking crucial business insights. But traditional network connectivity was not purpose-built for IoT. These networks were designed for traditional voice and data communication, and thus present significant obstacles.

Firstly, this infrastructure often lacks the necessary protocols, bandwidth, or scalability to efficiently handle a large influx of geographically dispersed IoT device connections and data traffic. Further, users often have to source their own data plans to power IoT projects, where they can find themselves locked into a single provider. This inhibits the ability to explore alternative options, which can have serious consequences for global IoT deployments.

Additionally, operator lock-in prevents adjusting the quality and quantity of coverage to fit the needs of the project. For example, shipping companies often need to track cargo of differing value. For non-precious cargo, intermittent updates would suffice, but for highly valuable cargo, the company needs regular updates on the precise location of the shipment.

This example requires an agile connectivity solution, which recognises that no one IoT project is ever the same. They differ in needs and configurations, and requirements often change over time, meaning they need to be able to move between operators freely and seek the best deals on the market.

Yet, the status quo means that they will likely have to adopt and switch between a patchwork of IoT partnerships for different use cases, which becomes difficult to manage and expensive.

Connectivity is changing

Legacy forms of connectivity have been useful, but the downsides have triggered a new era of innovation, transforming connectivity from a siloed barrier at the middle of the ecosystem, to one that can be used to scale projects in an affordable, agile way.

One such example is represented by embedded connectivity. That is, rather than an afterthought, connectivity can be built into a device at a manufacturing level.

At a simple level, the role of a SIM card is to give devices a connection to send and receive data, the primary source of value offered by IoT. But today many users are charged for how many devices they have connected, regardless of how they are being used – that’s an expensive way to run things.

Not only does embedding connectivity through solutions like SoftSIM at the manufacturing level give more control at an operational level, but it can also translate to greater commercial control, such as only charging users for the data they actually need. Further, removing physical SIMs from devices can reduce idle power consumption by over 90%. This is simply not possible through traditional forms of connectivity. 

Asking more from your network

Only recently has doubling device lifetime, halving battery consumption, and cutting costs from innovating within the network itself gone from fantasy to reality. Our industry is beginning to ask whether we can expect more from our network, because doing so remains the central way IoT can deliver on its potential.

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IoT Devices Targeted by Malware, Giving Hackers Remote Control

 Researchers have discovered a new Mirai variant, IZ1H9, a malware that attacks small IoT devices, and enables remote operation by hackers to carry out large-scale network attacks.

The malware was discovered by Unit 42, the threat research team from Palo Alto Networks. First discovered in 2018, IZ1H9 targets devices using the Linux network, which is primarily used by IoT devices and has grown increasingly active in recent years.

Unit 42 researchers published in May identifying a string of IZ1H9 attacks since November 2021, all from the same source.

“IoT devices have always been a lucrative target for threat actors, and remote code execution attacks continue to be the most common and most concerning threats affecting IoT devices and Linux servers,” the researchers said. “Exposed vulnerable devices could lead to serious threats.

“The vulnerabilities used by this threat are less complex, but this does not decrease their impact, since they could still lead to remote code execution. Once the attacker gains control of a vulnerable device, they can include the newly compromised devices in their botnet.”

To combat the threat, Unit 42 recommended running regular updates when possible to monitor the presence of threats.

Mirai malware identifies and targets unsecured smart devices, taking control of them to create a network of remote-controlled bots that can launch collective cyberattacks. Mirai typically attacks consumer devices which are then used to conduct distributed denial of service (DDoS) attacks.

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Industrial security firm Dragos came under attack

IoT news of the week for May 

Dragos, which provides security for industrial and operational technology, reported a cybersecurity event this week in which a known cybergang attempted to breach its internal network and its cybersecurity platform. They didn’t make it. But the attackers did breach the company’s SharePoint servers and contract management platform. 

They were trying to get into Dragos’ network in order to encrypt its devices and presumably hold them for ransom. This is not great, but it’s better than some nation-state actor trying to infiltrate Dragos as a precursor to industrial sabotage. 

The hackers got access to personal information from a salesperson who had recently been hired but hadn’t yet started, then used that information to impersonate the salesperson through an employee onboarding process. 

I get exhausted just trying to think about securing information and devices in today’s world. 

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