Efficient Routing Protocol for Wireless Sensor Network based on Reinforcement Learning

Abstract

Wireless sensor nodes are battery-powered devices which makes the design of energy-efficient Wireless Sensor Networks (WSNs) a very challenging issue. In this paper, we propose a new routing protocol for WSN based on distributed Reinforcement Learning (RL). The proposed approach optimises WSN lifetime and energy consumption. This routing protocol learns, over time, the optimal path to the sink node(s). With a dynamic path selection, our algorithm ensures higher energy efficiency, postpones nodes death and isolation. We consider while routing messages the distance between nodes, available energy and hop count to the sink node. The effectiveness of the proposed protocol is demonstrated through simulations and comparisons with some existing algorithms over different lifetime definitions.