Non-convex model for sensor network localization based on connectivity.

In this paper, a new model utilizing all the information derived from connectivity-based sensor network localization is introduced. The connectivity information between any pair of nodes is modeled as convex and non-convex constraints. The localization problem is solved by searching for a solution that would satisfy all the constraints established in the problem. A two-objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES) is used to solve the localization problem. The solution can reach the most suitable configuration of the unknown nodes because the information on both convex and non-convex constraints related to connectivity has been utilized. From simulation results, a relationship between the communication range and accuracy is obtained. Furthermore, a two-level range connectivity-based sensor network localization method is proposed to enrich the connectivity information. The two-level range/indication of connectivity between each pair of nodes would indicate three levels of connectivity: strong, weak, or nil. A comparison on accuracy between the one-level and two-level ranges of connectivity is carried out by simulation using six different topological networks all containing 100 nodes. Simulation results have shown that better solution can be obtained by using two-level range connectivity compared with the usual one-level range connectivity-based localization. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]