Work-in-Progress: Q-Learning Based Routing for Transiently Powered Wireless Sensor Network

Abstract

Reliable communication is a critical concern in power-limited energy harvesting wireless sensor networks (EH-WSNs). The communication optimization is needed since the protocols in battery-powered WSNs cannot adapt to the intermittent harvestable energy sources. In this paper, a novel reinforcement learning (RL) based routing algorithm that fully exploits the capability of wake-up radio (WuR) is presented. This routing strategy aims at increasing the packet delivery rate by leveraging wake-up radio devices to enable receiver nodes to make the decentralized forwarding decision. Simulation results show that the performance of the proposed learning approach, which requires only limited knowledge of the energy harvesting process, has only a small degradation compared to the optimal routing decision with full knowledge of energy harvesting process.