Covering the Target Objects with Mobile Sensors by Using Genetic Algorithm in Wireless Sensor Networks

Abstract

Wireless sensor networks have several applications, such as target detection and tracking, and monitoring battlefields. Coverage is one of the most important performance metrics for wireless sensor networks since it reflects how appropriate an event can be detected and monitored in the sensing field. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of sensor node deployment. In this paper, an efficient genetic algorithm is proposed to solve the coverage problem of the target objects. The optimal number of sensor nodes starts from fewer randomly deployed nodes and increases gradually in subsequence generations. The performance of the proposed genetic algorithm was evaluated, and the simulation results show that this approach can cover all the target objects as well as minimize the number of additional mobile sensor nodes.

An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA

“…However, a static WSN is assumed for this study. The WSN coverage can be classified into three methods: area coverage, point coverage, and barrier coverage [6,7]. We have used the barrier coverage method [8], which deals with the general detection of all movement crossing over sensor barriers.…”