Optimization of sensor deployment using multi-objective evolutionary algorithms

Abstract

Many designs of wireless sensor network applications require the determination of the optimal locations of sensor nodes to be placed in a sensor field. Coverage enables us to evaluate the supervision quality of each point within an area of interest. In this paper, we address the problem of target coverage in wireless sensor networks. This concern is trivial if each target must be covered by a single sensor. However, it becomes an NP-complete problem when the choice of the position of the sensor must take into account the targets that it should cover in its vicinity. Using a multi-objective evolutionary-based approach, we propose a stochastic method to search for network configurations that achieve good coverage with the fewest sensors. A comparative experimental study of the model with well-known multi-objective algorithms such as NSGA-II, SPEA2, SMSEMOA and MOEA/D indicate that NSGA-II performs better than others on most of the test instances.