Power management for Internet of battery-less things
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The objective of the thesis is to develop a resource management framework for Internet of Battery-less Things (IoBT). With the proliferation of Internet of Things (IoT) and IoT-related services, how to power such a gigantic number of IoT systems in a cost-efficient and environment-friendly way becomes a bottleneck problem. Empowered by energy harvesting technologies, IoBT is a promising solution with prolonged lifetime and self-sustainable operation. To achieve sustainable operation of IoBT with high power efficiency, resource management strategies at both the node level and network level are investigated. At the node level, a redistribution-aware power manager is designed to minimize the charge redistribution energy loss incurred by the novel energy storage device, supercapacitor. Then, a predictive power management framework is designed to improve the energy utilization of IoBT systems. At the network level, an adaptive clustering framework is developed to achieve high and fair data collection rate with low overhead for IoBT-operated cluster. In addition, a collaborative in-network processing framework is developed to minimize the data processing latency for IoBT networks. With the node-level and network-level resource managers, the proposed research provides a holistic resource management framework for IoBT.