An Energy Efficient Routing Based on Swarm Intelligence for Wireless Sensor Networks

Abstract

Wireless Sensor Networks are characterized by having specific requirements such as limited power, memory and functionality to support communications. In sensor networks, minimization of energy consumption is considered a major performance criterion to provide maximum network lifetime. Traditional routing protocols do not take into account that a node contains only a limited energy supply. In this paper, we describe a novel energy efficient routing approach which  combines swarm intelligence, especially the Ant colony based meta heuristic, with a novel variation of Reinforcement learning for Wireless Sensor Networks (ARNet). The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using an improved swarm intelligence based optimization. ARNet balances the energy consumption of nodes in the network and extends the network lifetime. Simulation results show that ARNet can obviously improve adaptability and effectively reduce the average energy consumption compared with the traditional EEABR algorithm.