Genetic Algorithm for Energy Efficiency in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a popular solution for several monitoring applications, due to their low cost and ease of deployment, while they can collect a plethora of data. To acquire such information WSNs consist of distributed sensor nodes, usually inch scale with limited energy resources. Since WSNs are formed in an ad hoc manner, the distribution of sensor nodes in a geographic location may not be known prior to the deployment of the nodes, hence, routing paths between nodes and a base station should be found. Finding routing paths requires energy while even with routing paths established, transmitting data between nodes will increase the total energy consumption in the network. When a node runs out of energy it may no longer be used as part of a routing path. Therefore conserving energy will prolong the node lifespan, increasing the lifespan of the WSN. In this work, a Genetic Algorithm (GA) is used as a clustering strategy to improve energy efficiency in a large-scale WSN. According to simulation results, GA can achieve better performance in terms of energy consumption and is a promising approach to extend the network lifespan.