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Energy-Efficient and Reliability-Driven Management of IoT Systems


The Internet of Things (IoT) integrates heterogeneous devices, ranging from sensors to smartphones, tablets and edge servers, and can provide a variety of services, beyond the traditional Internet. Unfortunately, due to its unprecedented scale and ubiquity, IoT faces a maintainability challenge and a set of interrelated problems. With the emergence of edge computing, IoT devices execute various tasks that consume a significant amount of power to deliver high quality of service, which can drain their battery in short time. High peak power increases the device temperature stress, which worsens the impact of transistors and interconnects reliability degradation mechanisms. Such mechanisms lead to early device failures and are costly to fix. In this dissertation, we focus on novel solutions for energy-efficient and reliability-driven management of IoT systems. We introduce a simulation framework called RelIoT to enable reliability evaluation and analysis in IoT networks, which paves the way for the development of new network management solutions. We develop a dynamic reliability management technique based on computation offloading for IoT edge computing architectures. Our approach achieves 20.5% longer mean time to failure than the next best network management solution. We also present an adaptive and distributed reliability-aware routing protocol using reinforcement learning. We show that our routing protocol can improve reliability of a network up to 73.2% compared to state-of-the-art routing approaches. The main focus inall our solutions is to use device batteries efficiently, satisfy QoS requirements, and improve overall network lifetime by mitigating reliability degradation. Lastly, we complement this to specifically study battery health and associated degradation mechanisms, as the traditional techniques developed for optimizing the energy consumption of networks do not yield optimal battery life. An improvement in network lifetime up to 68.5% can be achieved with our approach compared to energy consumption optimization approaches.

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