Simulated Annealing based Wireless Sensor Network Localization

In this paper, we describe a novel localization algorithm for ad hoc wireless sensor networks. Accurate selforganization and localization capability is a highly desirable characteristic of wireless sensor networks. Many researchers have approached the localization problem from different perspectives. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase localization method based on the simulated annealing technique to address the issue. Simulated annealing is a technique for combinatorial optimization problems and unlike the gradient search method, it is robust against being trapped into local minima. In this paper we show that our simulated annealing based localization method can be used in ad hoc wireless sensor networks to estimate the location of nodes accurately. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have been affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed novel scheme gives accurate and consistent location estimates of the nodes, and mitigate errors due to flip ambiguity. The performance of the proposed algorithm is better than the performance of some well-known schemes such as DVhop method and convex optimization based semi-definite programming method.