RLMan: An Energy Manager Based on Reinforcement Learning for Energy Harvesting Wireless Sensor Networks

Abstract

A promising solution for achieving autonomous wireless sensor networks is to enable each node to harvest energy in its environment. To address the time-varying behavior of energy sources, each node embeds an energy manager responsible for dynamically adapting the power consumption of the node in order to maximize the quality of service while avoiding power failures. A novel energy management algorithm based on reinforcement learning (RLMan) is proposed in this paper. By continuously exploring the environment, RLMan adapts its energy management policy to time-varying environment, regarding both the harvested energy and the energy consumption of the node. Linear function approximations are used to achieve very low computational and memory footprint, making RLMan suitable for resource-constrained systems, such as wireless sensor nodes. Moreover, RLMan only requires the state of charge of the energy storage device to operate, which makes it practical to implement. Exhaustive simulations using real measurements of indoor light and outdoor wind show that RLMan outperforms current state-of-the-art approaches, by enabling almost 70% gain regarding the average packet rate. Moreover, RLMan is more robust to variability of the node energy consumption.