Genetic Algorithm Based Wireless Sensor Network Localization

Abstract

In most sensor network applications, the information gathered by sensors will be meaningless without the location of the sensor nodes. Node localization has been a topic of active research in recent years. Accurate self-localization capability is highly desirable in wireless sensor network. This paper proposes a genetic algorithm based localization (GAL). The proposed genetic algorithm adopts two new genetic operators: single-vertex-neighborhood mutation and the descend-based arithmetic crossover. Four example problems are used to evaluate the performance of the proposed algorithm. Simulation results show that our algorithm can achieve higher accurate position estimation than semi-definite programming with gradient search localization (SDPL) [11] and simulated annealing based localization (SAL)[13]. Compared to the usual crossover operator: simple arithmetic crossover, whole arithmetic crossover and single-point crossover, the proposed crossover can obtain a lower mean position error.