An effective metaheuristic approach to node localization in wireless sensor networks

Due to the need of limiting costs and energy consumption, in real deployments of wireless sensor networks the exact positions of only a restricted number of nodes are generally available. Since a lot of applications require to know where all the nodes have been placed, the issue of estimating the locations of the remaining nodes has attracted a lot of interest in the literature. In this paper, we discuss how the localization problem can be solved by using a two-objective evolutionary algorithm which concurrently aims to maximize the localization accuracy and minimize the number of connectivity constraints non-satisfied by the candidate geometry codified in the chromosomes. The proposed approach has been applied to different network configurations and compared in terms of normalized localization error with a state-of-the-art method based on semi-definite programming. The results show that our approach outperforms the compared method in all the configurations.