Energy Management and Wake-up for IoT Networks Powered by Energy Harvesting

Abstract

The rapid growth of the Internet of Things (IoT) presents sustainability challenges, including increased maintenance requirements and overall higher energy consumption. This motivates self-sustainable IoT ecosystems based on Energy Harvesting (EH). This paper treats IoT deployments in which IoT devices (IoTDs) rely solely on EH to sense and transmit information about events/alarms to a base station (BS). The objective is to effectively manage the duty cycling of the IoTDs to prolong battery life and maximize the relevant data delivered to the BS. The BS can also selectively wake up specific IoTDs to gather extra information following initial detection. We propose a K-nearest neighbors (KNN)-based duty cycling management to optimize energy efficiency and detection accuracy by considering spatial correlations among IoTDs’ activity and their EH process. We evaluate machine learning approaches, including reinforcement learning (RL) and decision transformers (DT), to maximize information captured from events while managing energy consumption. All three approaches (KNN, RL, and DT) achieve significant energy savings over state-of-the-art methods. Moreover, the RL-based solution approaches the performance of a genie-aided benchmark as the number of IoTDs increases.