Differential evolution meta-heuristic scheme for k-coverage and m-connected optimal node placement in wireless sensor networks

Abstract

A wireless sensor network (WSN) faces a wide range of issues, which includes coverage of the given set of targets under specified connectivity constraint. There is a need to monitor different targets in the sensor field for effective information communication to the base station from each wireless sensor node which monitors the target by maintaining required connectivity among them. The problem of ensuring every target covered by at least k sensors and each sensor directly communicate with m sensors is termed as k-coverage and m-connectivity problem in wireless sensor networks. As the wireless sensor nodes are battery driven and have limited energy, the primary challenge is to have an optimal placement of sensor nodes in the field of deployment to minimize energy consumption. The objective of this work is to deploy the optimal number of sensor nodes with k-coverage and m-connectivity constraints in an area of interest. In the last few years, many meta-heuristic algorithms have been proposed to solve different problems like clustering and localization in WSN. In this paper, we introduce a meta-heuristic based differential evolution algorithm to solve k-coverage and m-connectivity problem in WSN. The simulation result shows that the proposed meta-heuristic method out performs the genetic algorithm. MIR Labs.