Active Engineering Clean Energy

IDEAL: Reducing Carbon Footprints of IoT Devices through Extension of Active Lifespans

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AI plain-English summary

The Internet of Things is on track to reach 30 billion devices by 2030, and most of their carbon footprint comes from manufacturing them, not powering them. This matters because the typical useful life of an IoT device is only a few years, after which it is replaced. The embodied carbon from that manufacturing cycle dominates the device’s total emissions. To make ICT sustainable, devices must last decades, not years. The IDEAL project aims to extend IoT device lifetimes from several years to several decades. It will embed ultra-low-power sensors directly into integrated circuits to monitor degradation, then use local machine learning to detect and correct anomalies before they cause failure. A formal method called session types will guarantee secure, correct communication between the monitoring system and the devices. If successful, the technology could slash the full-lifecycle carbon footprint of IoT systems. It would also enable a circular economy in which devices are repurposed for new tasks as their capabilities decline, and could give rise to entirely new businesses focused on device monitoring, adaptation, and reuse.

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The CO2 emissions from manufacture (the so-called embodied carbon) of end-user ICT devices makes up the majority of their carbon footprint over the typical current useful life of such devices. To achieve sustainable ICT, in addition to reducing run time energy consumption, it is therefore essential to extend the active life, ideally to several decades. The largest growth in end-user devices is in the Internet of Things (IoT) market, projected to rise to 30 billion devices by 2030. Our vision with the IDEAL project is to demonstrate the possibility of extending the lifetime of IoT devices from several years to several decades through a combination of novel hardware design technologies, hardware/software co-design techniques, formal methods, machine learning and circular economy. As current business models assume short replacement cycles for end-user devices (typically a few years), in addition to the technologies to extend the useful life, we will also demonstrate the viability of alternative business models based on very long-lived devices, co-created with our partners. Our technology will allow not only to extensively prolong the useful life, but also to accurately assess the degradation of any given device, enabling the repurposing of devices with reduced capabilities for new tasks that match these capabilities. In this way, our proposal fits both with Sustainable ICT, as it will enable a drastic reduction in the embodied carbon of IoT devices, and the Circular Economy, as our technology will allow devices to be repurposed repeatedly throughout their useful life. Our proposed approach is to instrument the integrated circuits at the lowest level with a novel, ultra-low power, unobtrusive monitoring and data aggregation technology and additional self-healing capabilities. The generated data will be analysed using low power machine learning nodes executing close to the actual IoT devices rather than in the cloud for reliable and early detection of anomalies in the system operation, indicative of early system degradation, and produce the optimal strategy for addressing each anomaly before it can affect the lifetime of the system. Because this is a close-loop networked system with a considerable degree of control over the IoT devices, it is essential to have guarantees of correctness and security by design in the communication between the analysis and decision making nodes and the IoT devices, which will be enabled by the use of a formal mechanism know as session types. The system we propose to develop is a subsystem of any IoT system: an IoT system consists of end-user devices located at the so-called "edge", connected to a cloud data centre, with increasingly, part of the processing performed in the "fog" between the edge and the cloud. The aim is that the introduction and operation of our subsystem will substantially reduce the full-lifecycle carbon footprint of the overall system. In business terms, our system fits within an emerging environment of IoT-as-a-service and circular economy. Large-scale introduction of our technology and associated business models can result in the emergence of entirely new types of businesses taking care of the re-purposing of devices or offering monitoring and adaptation services.

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Researchers

Caitlin McElroy (Co-Investigator)Jose Cano Reyes (Co-Investigator)Kathryn Wheeler (Co-Investigator)Klaus D McDonald-Maier (Principal Investigator)Nobuko Yoshida (Co-Investigator)Wim Vanderbauwhede (Co-Investigator)Xiaojun Zhai (Co-Investigator)

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Original classification

Research and Innovation

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