A two-phase localization algorithm for wireless sensor network

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. A fundamental problem in distance-based sensor network localization is whether a given sensor network is uniquely localizable or not. Flip ambiguity is a main problem that can make the sensor network not uniquely localized. It causes large errors in the location estimates. This paper proposes a two-phase localization algorithm (TPLA) for wireless sensor network. During the first phase, genetic algorithm (GA) is used to obtain an accurate estimation of location. During the second phase, simulated annealing algorithm (SAA) is used to refine the location estimates of those nodes that are likely to have flip ambiguity problem. 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).