Achieving Asymptotically Optimal Throughput and Fairness for Energy Harvesting Sensors in IoT Network Systems

The rapid growth of Internet of Things (IoT) results in tremendous interest in research for low-power wireless sensors. The energy harvesting (EH) ability of IoT sensors determines the efficiency and reliability of network connectivity inside IoT. This work considers a wireless sensor network (WSN) where a fusion center (FC) gathers data packets from EH sensors which can store energy without battery overflow or leakage. The FC selects a subset of nodes per time slot to gather data from them under stochastic data arrival processes via its mutually orthogonal channels. EH processes and battery states of sensors are unknown to FC. Similarly, data arrival (DA) processes and buffer states of sensors are unknown to FC, which only knows previous transmission results. We aim to propose a simple, efficient policy that maximizes network throughput and fairness in IoT network systems, which is very important, especially for 5G and next-generation networks. Data transmission relies on not only scheduled nodes' gathered energy but also their buffered data. If it has data to send and sufficient energy for transmission, a node can transmit data when scheduled. This paper proposes a low-complexity algorithm that is almost throughput and fairness optimal for quite general EH and DA processes over finite time horizons. We show that by removing the battery capacity limit, the proposed approach yields asymptotically optimal throughput and fairness for general EH and DA processes over an infinite time horizon. It achieves nearly optimality in throughput and fairness across finite time horizons with finite-capacity batteries, according to numerical simulations whereas existing solutions become suboptimal.