Internet of Things
Evolution of IoT devices E
By Nick Wood, sales and marketing director, InsightSIP
arly IoT devices were typically based on quite simple architectures. They would incorporate a simple microprocessor, a sometimes- separate radio chip (often Bluetooth Low Energy), and some sensors; their only function would be to gather some data and transmit it onwards to other systems. Since then, IoT devices have developed enormously in terms of performance, features and sophistication. This article looks at these trends and what the future holds.
Early IoT
First wave IoT devices were often doing little more than replacing a cable, or enabling functions such as configuration and setup to be done on a tablet or phone. There was a lot of value gained by linking systems that had previously been isolated, or by the simplicity of replacing a wired architecture with a wireless one. The IoT devices did little more than read data in, and transmit it onwards somewhere else. The focus, particularly for Bluetooth Low Energy devices, was minimising power consumption to allow extended lifetimes when powered by a battery.
Onboard microprocessors Quite early on, chipsets with integrated radios and microprocessors became the norm. However, there is a night and day difference between the processors in early devices and those of the latest generation devices. Typically based on ARM cores, early devices would have the most basic M0 core running at perhaps 16MHz. The state-of-the-art devices today have M33 core devices running at up to 320MHz – a 20x speed increase in little over a decade. This has been achieved without increasing power consumption, and indeed reducing it in some cases.
The most advanced devices offer multicore processors, typically independent network and application processors, which allow real time response to inputs without interfering with over-the-air communications. Often this has allowed end device manufacturers to dispense with additional microprocessors even for more sophisticated applications.
16 March 2026
Security “built in” not “added on” Security in early IoT devices was typically weak, with encryption of over-the-air traffic being about the limit of what was offered. This is no longer acceptable, as IoT systems become ever more connected and integrated into mission critical applications. Secure microprocessors, incorporating cores such as ARM Trustzone, and secure key storage for end-to-end authentication and encryption are becoming standard. The European Cyber Resilience Act is making much of this obligatory, but even in other regions, the reputational risk of insecure systems is driving an increased focus on cyber security.
The majority of IoT systems will incorporate over-the-air update capabilities to fix security flaws as well as provide feature updates, just as phones, tablets and PCs already do. However, these require strong authentication and encryption processes to be helpful, otherwise they could cause more harm
Components in Electronics
than good. In addition to secure software, physical tamper resistance is also likely to become standard in the future.
Rich peripheral sets
A further development has been the range of peripheral connections supported by modern devices. Early devices were largely focused on reading sensors, via fairly simple connections such as SPI and I2C. The next generation offers a much broader range of options, including ethernet drivers, CAN buses, high speed USB, dedicated audio drivers and more. In addition, some devices offer separate programable peripheral cores to allow users to develop their own drivers without impacting the main application processor.
AI at the edge
The concept of AI functionality on a small battery powered device may seem strange, given the typical image of AI as something delivered by vast server farms. However, small AI inference engines are appearing
on IoT devices. The idea is not to run a Large Language Model (LLM) general purpose AI, which would be unfeasible. Rather, the concept is to run an inference engine, targeted at a limited problem domain, using an AI model trained on some larger system. Potential applications could be simple speech recognition, face detection, or intelligent presence detection.
The aim of such systems is to achieve more efficient processing than conventional manually created logic, and thus reduce power, enable new applications, or perhaps reduce the complexity of sensors systems. AI enhanced edge computing can also reduce network traffic by locally analysing and summarising data, and provide a low latency response locally.
Such an application of AI may turn out to be more immediately useful and profitable than the grand vision of general intelligence via LLMs, which comes with major issues of cost and resource
www.cieonline.co.uk
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44
