From industrial plants to smart cities, the Internet of Things (IoT) market is expanding with a compound annual growth rate projected to reach from 10% to over 20%, with IoT services expected to see the highest growth. The Internet of Things seems to be the way of the future, and it might be a good time to implement a new IoT project. With that in mind, what IoT trends should we watch out for in 2026?
IoT connectivity—LoRa networks in industrial IoT solutions—an alternative to cable and cellular IoT connections
The first challenge faced by a company that wants to follow the latest IoT trends is building a network of IoT devices that collect data and send it to a central system for analysis. This is particularly tricky on brownfield deployments, where building a traditional communications network from scratch can be prohibitively complex.
So how does one transfer data from all these IoT devices and smart sensors and other IoT components? The low latency communication protocols designed to work with smart home devices and the smart devices in our pockets are not necessarily ideal for IoT devices used in edge computing or other IoT systems. Wi-Fi has a short range and can easily be blocked by concrete walls, water tanks, or banks of metal machinery. Laying cables to connect and power all IoT devices can be expensive, if not downright impossible, in brownfield deployments. Cellular IoT connections would seem like a good idea, but they are expensive and can also be unstable underground or in complex industrial spaces.
This is where LoRaWAN comes in. While this technology only allows for transfers of 50 kilobits per second, it can operate over distances of a few kilometers in urban areas and over ten kilometers in rural settings. It is also effective at penetrating dense building materials and physical obstacles, allowing industrial IoT networks to convert basements or heavy concrete buildings with complex interiors into smart buildings. What is more, depending on the frequency of data transfers (counted in minutes rather than milliseconds), batteries in these devices can last from five to ten years on a single charge. LoRaWAN IoT devices also sport data security measures in the form of mandatory AES-128 cryptographic security solutions, helping protect IoT devices from tampering and sensitive data from data breaches. Given that they do not need either logic or power connections, nor easy access for maintenance, they can be installed in virtually any environment, and when handled correctly, the smart infrastructure can be cheaper and quicker to set up than traditional solutions. In settings where real-time tracking is not a necessity and small packets of data are sent every ten minutes or so, these benefits are well worth the trade-offs, as demonstrated by numerous real-world examples of LoRaWAN setups.
Latest IoT trends—managing energy consumption with connected IoT devices
Speaking of low energy demands, one of the most beneficial uses of networks of IoT devices and thus one of the key IoT trends that’s worth looking at is energy consumption management. Despite the drop in average electricity prices in the EU in 2025, the cost of electricity in the region is still, on average, higher than in the USA or China. Although emerging trends indicate that with lower gas imports, deployment of renewables, and the increasing flexibility and integration of smart grids, the EU is on its way to achieve lower electricity prices than the USA, the situation is still dire enough for, for example, the German government to introduce industrial electricity price regulation for the coming years. Given the significant regional differences in energy pricing, increasing operational efficiency in this area is definitely going to be a valuable investment in 2026.
Thus, among the trends in IoT to look out for in 2026, we can expect an increase in the use of IoT technology, such as IoT sensors, and data analytics to reduce energy consumption in industrial settings. Valuable insights in modern IoT systems come from, among other factors, the granularity of data provided by IoT solutions. Each connected device can have its energy consumption measured on an individual level and with high frequency, revealing any deviations from the norm. This information from interconnected devices can be used to control which devices are not working at peak efficiency or are at risk of breaking down, as well as which can be occasionally turned off to limit energy consumption during times of peak demand. Combined, this data can be used for predictive analytics to adjust demand and capacity to fluctuating electricity tariffs and mitigate problems such as reactive power losses.
One way of using IoT data analytics in this capacity is through solutions like Nexem EMS (Energy Management System). This is the core subsystem of the Nexem IoT ecosystem and is responsible for monitoring energy usage, including not just electricity but also other media, compressed air, and industrial gases, as well as analyzing data on energy usage and quality. It can alert users when limits are about to be reached or exceeded, helping to prevent penalties and ensuring energy stability, allowing for energy cost optimization and the production of reports aligned with ISO 50001 and ESG. In short, by combining industrial data acquisition with advanced analytics, this system provides production managers with actionable insights allowing for smarter energy management.
Emerging trends—artificial intelligence in manufacturing processes—predictive analytics for energy prices
When it comes to energy management, one the trends in IoT technology to look out for in 2026 is the growing use of AI. Unlike the often-overhyped use cases involving LLMs, specialized machine learning algorithms can offer genuine benefits by predicting electricity supply and price swings. This can be hugely beneficial because in some markets, electricity costs can vary greatly from day to day or even hour to hour, depending on demand and availability, especially from renewable sources. The weather can also have a tremendous influence (such as when water levels in the Poland’s Vistula River drop in summer droughts, limiting cooling capacity in power plants while everyone’s AC is running at full power). This is compounded by local pricing solutions—for instance, in the UK, the price of electricity is decided by the most expensive supplier so, while cheap renewables produce most of the energy, as soon as a natural gas power plant kicks in, electricity jumps in price to levels dictated by the volatile global market for fossil fuels.
The ability to react to these changes is another challenge that can be addressed by IoT solutions such as Nexen Predict, which uses AI models to offer accurate short-term energy price forecasts. Machine learning algorithms are used to analyze numerous parameters, including market data (energy prices, power demand, renewable energy generation) and weather information (temperature, precipitation, cloud cover, wind speed) to predict expected rises and falls in energy prices up to a week in advance, allowing production to be planned for the most cost-effective time windows. With smart production scheduling, energy-intensive processes can be aligned with periods of lower energy costs, while strategic procurement supports purchasing decisions and contract negotiations. By capturing overall trends and daily fluctuations, the system allows companies to better plan operations and take advantage of favorable cost periods to support smarter production and purchasing decisions.
Read more on Industrial IoT:
Industrial data acquisition for energy management—how we do it at Fabrity
Internet of Things (IoT) security: A challenge for 2026
The LoRaWAN technology for industrial settings: Four practical use cases for 2026
Where range matters: LoRaWAN for smart cities
Predictive maintenance in manufacturing industry
Industrial IoT solutions—5 practical examples
Data acquisition: the backbone of Industry 4.0 in 2025
Edge AI technology: driving Industry 4.0 in 2025
What is a smart factory of the future and how do you create one?
Industrial IoT communication protocols: a comprehensive guide to modern connectivity
8 practical applications of AI in manufacturing
IoT development in 2026—predictive maintenance with connected IoT devices
Another application of AI in combination with connected devices that stands out among IoT trends for the near future is predictive maintenance, which offers a way of balancing the two opposing aims of maintenance: to have parts work as long as possible and to avoid unexpected breakdowns. Traditionally, this was achieved by following a strict maintenance schedule based on historical data analysis—and a healthy dose of guesswork and luck. Predictive maintenance replaces this with real-time processing of data from connected devices in IoT systems. IoT devices such as vibration and temperature sensors can be attached to machinery and monitor its state. Instead of relying solely on visible symptoms or operator experience, the system (such as Nexen Predict mentioned above) analyzes data from IoT sensors over time to detect subtle patterns and long-term trends that indicate gradual wear and declining performance. These changes are often nearly impossible for humans to notice, as they usually develop slowly. In other words, the IoT sensors alert the system of any parts or machines showing signs of upcoming malfunctions. Maintenance work can then be scheduled for a convenient moment, before the machine breaks but also when the repairs will be the least disruptive.
Emerging trends in IoT—computer vision use cases
One final application of artificial intelligence combined with IoT technology that looks to be an emerging IoT development in 2026 is the application of computer vision. This technology has made great strides in recent decades and, combined with AI learning algorithms and IoT technology, as well as edge computing (in some cases), it can be very useful in industrial settings. Here are two examples of how computer vision can be used in industrial settings, based on the capabilities offered by Nexen Vision.
Let’s start with the first industrial application of computer vision and IoT—workplace safety. Cameras in specific areas can detect whether employees are wearing PPE, such as hardhats, masks, goggles, etc., and alert the supervisor if violations are found. This reduces the need for human oversight and removes the element of chance from the process. Similarly, IoT devices equipped with facial recognition capabilities can recognize when people wander into areas they should not have access to, be that for safety or security reasons. Normally, this would require putting a human or a complex access control system at every door, but with computer vision and IoT devices that collect data, security can be reactive, while removing the inconsistency inherent in human monitoring. The issue of privacy should be addressed here—with facial recognition in use, local data processing or even edge computing might be a better solution than cloud computing, to avoid constantly sending workers’ images to an external entity where they can be at risk of data breaches and privacy violations.
The other application of computer vision in the digital transformation of manufacturing is quality control. There is a limit to the number of products a human controller can check for defects, especially in fast-paced mechanized production lines, where traffic flow is high and fast. Furthermore, vigilance studies show that the accuracy of human monitors declines after 20 to 30 minutes due to fatigue. In contrast, computers do not get fatigued and can perform real-time data processing much faster than humans (at least in narrow applications). Combine IoT with computer vision and it is possible to inspect every item coming off the assembly line. If the possible defects are complex and/or varied, machine learning can be used to develop fault recognition algorithms based on historical data.
Emerging trends in IoT cybersecurity
With the growing potential of IoT solutions in the industrial environment, and a growing number of connected industrial IoT technology networks, comes enhanced efficiency but also certain risks, particularly when it comes to data security. Smart home devices often come with no security measures or even hardcoded vulnerabilities; edge devices can be physically tampered with; patient data can be intercepted on its way to the cloud platform; IoT devices can serve as an access point to the broader network for malicious actors. How can you remain secure in the Internet of Things in 2026? The risk of penetration of IoT networks can be mitigated by the use of edge computing (and, by extension, edge AI) rather than cloud computing, but this is only viable in some cases. More mundane solutions might actually be more important: Make sure to update firmware on smart devices regularly and avoid purchasing IoT devices with weak security or add extra security measures on IoT network hubs. Asset tracking is also important: Make sure that all IoT devices are visible to security systems. Protect edge computing devices from physical access and monitor them for signs of tampering. Keep as much data within private IoT networks as possible and encrypt it before uploading to the cloud: Controlling data traffic flow is key to IoT security.
What is also bound to affect the IoT landscape in terms of security is the final implementation of the NIS2 directive, which most countries are expected to achieve in 2026. The requirements of the directive are specific. Organizations must implement network segmentation, separating operational technology networks from enterprise IT and Internet access. Access controls must enforce least-privilege principles, with multifactor authentication for remote access and privileged accounts. Encryption is mandatory for data in transit and at rest. Continuous monitoring must detect and log security events. Incident reporting follows strict timelines. Early warning notifications must reach national authorities within 24 hours of detection of a significant incident. Detailed incident reports are due within 72 hours, documenting the scope, impact, and initial response. Final reports submitted within one month must include root cause analysis and remediation measures. Current estimates of the costs of implementing the directive across the IoT industry trend toward significant, but so do the costs of dealing with data breaches, including ransomware attacks. Industry organizations implementing new systems should evaluate security capabilities as a primary selection criterion, not an afterthought.
IoT trends for 2026
From smart cities to edge computing (or even edge AI), the IoT market offers many opportunities. In 2026, we expect the major IoT trends and challenges to include: IoT security, applications of IoT sensors with computer vision capabilities, LoRa networks helping transfer data between evermore connected devices with high operational efficiency,combining AI models with IoT devices for predictive maintenance and optimizing energy usage in smart factories. These trends in IoT technology are not just an interesting phenomenon to watch but a huge opportunity for the manufacturing industry.
