5 Trends in IoT Confirming its Maturity in 2024

In my experience, the world of IoT is shifting towards a more mature and mainstream technology. Almost all new physical devices have a means of connecting and sharing their data with a central system. The data from these devices is processed for purposes such as condition monitoring, intelligent operations through actuators, and predictive behavior using algorithms and artificial intelligence.

Based on our projects and market developments, I see the following trends in 2024 confirming that IoT is becoming more mature:

1.      Security: With the rise of standards, security is not just a checkbox for device manufacturers. Security by design has become the standard practice, and the European Cybersecurity directive for digital devices requires manufacturers to ensure security throughout the lifecycle. This includes the requirement for a secure initial setup, active identification of known vulnerabilities, and distribution of updates and patches to keep devices secure. The prevailing operating mode of any IoT data processing party is to be extremely paranoid towards every connected device. IoT architecture embraces security by design, implementing principles such as least privilege, minimizing the attack surface, open design for easy audit, and producing evidence in terms of logs and audit trails. Security is a serious topic for any system, especially for IoT systems that interact directly with the physical world. Compromised security can have serious consequences and harm in the real world.

2.      Asset Intelligence and Decentralized Decision Making: Things are becoming increasingly more intelligent. Embedding logic, algorithms, and artificial intelligence decentralizes decision-making to the embedded software or plant controllers. The number of these decisions increases in frequency and complexity of logic. The logic goes beyond simple on/off commands towards more advanced optimization settings. These algorithms help the system achieve a desired outcome based on the preferred operating mode of the owner of the device, machine, or plant. This can be cost savings, increased worker security, increased customer satisfaction, sustainability, or a combination of these. I see a growing number of parameters that form the basis of this decision, adding to the increasing complexity. Being able to explain the decisions is essential in this model for real-time intelligent operations. Decisions have a direct impact on the real world, and in case of a less desirable outcome, it is crucial to be able to explain it. The following requirements are vital for decentralized decision-making powered by artificial intelligence: traceability and audit trail, clear labeling of the version of the algorithm and training data, management of operational boundaries of an automated decision-making process, clear instructions for what the decentralized asset can do in case of loss of connection, and an emergency key to suspend “optimized operations” and safeguard minimum operational processes.

3.      Energy Management and Sustainability: The increasing prices of energy, economic and political uncertainty, and climate action have accelerated the use of digital technology for energy management and sustainability. More and more legislation is being implemented based on the European Green Deal, requiring organizations to actively steer on reducing their climate impact by greenhouse gas emissions. Metering and controlling energy assets are crucial to increase the production of more sustainable energy and optimize energy usage in a more optimal way to maximize the usage of sustainable energy. This includes the transition from fossil fuel to (sustainable) electricity, creating challenges of congestion and the demand for more flex capacity. An increasing number of digital meters and actuators are added to the energy network, creating a real-time smart grid.

4.      Monitoring of IoT Run: The increasing importance of IoT systems and data supporting intelligent operations requires a serious approach to the run of an IoT system. These systems have evolved from a monthly report of the status to a real-time decision system. This requires a completely different level of operations and monitoring. Monitoring systems have become more active, signaling not only programmed alerts but also anticipating the abundance or absence of devices and data. They also include active monitoring of sensible data coming from the metering sensors. For example, an outside temperature sensor used to optimize the indoor climate system. If this sensor suddenly jumps from a regular measurement of 10 Celsius in March to minus 50 Celsius, it is still a valid temperature value. However, it is unlikely that the temperature in Amsterdam would suddenly drop by 60 degrees in a couple of days, considering the sea climate, and this value could lead to drastic energy spills in the climate system. Sensing anomalies in the incoming data are also part of the monitoring system. IoT operations have evolved from a best-effort system to a mission-critical system, embracing all possibilities for robust, fast recovery to operations and failover of the data feeds. Since an intermittence of this service could have serious financial or even physical consequences.

5.      Multi-Source Platform: IoT systems are becoming more reliant on multiple parameters to make decisions, and the IoT platform needs to ingest data from multiple sources and domains. Especially for larger organizations, this can be challenging. Mixing data from multiple OT systems with planning data, customer preferences, supply chain, and third-party sources such as weather and traffic can be difficult. Data management is vital in this structure, defining uniform usage of the data and the possibility to use, exchange, and compare with each other. Besides processing IoT telemetry data, the engineers of this platform also need to be able to work with data coming from back-end systems from SAP, IBM, Oracle, or other ERP planning and maintenance systems. The focus area of IoT shifts from sensor and connectivity towards integrating a vast amount of data sources and bringing them together in one platform.

In conclusion, the next phase for IoT shows that this technology is growing in maturity. The regular requirements, architecture, patterns, and operational processes are similar to those of other complicated, high-availability systems. This means our IoT baby has grown up to a mature IoT system, collaborating on an equal level with other mature systems.