Iterative search with emulating nodes´ local movement on range-free sensor network localization

This paper proposes an iterative evolutionary algorithm with emulating nodes´ local movement for searching the best localization accuracy in range-free scenario. All localization methods face a trade-off between the solution quality and computation cost. When sensor network localization in range-free scenario is considered as a constraint satisfaction problem, localization will reach the highest accuracy but with huge computation complexity. To solve the constraint satisfaction, some characteristics that only exist in range-free localization problem are utilized as heuristics in the search of nodes´ positions. They are summarized as simple and complex movement to emulate nodes´ local movement, and proved to be effective to find a suitable searching direction and jump out of local-minimums existing in the localization. Those emulations are then included in each iteration of a two-objective evolutionary algorithm minimizing the number of node-pairs with violated connectivity, as well as the value quantitating how worse of the violations. Simulation results show that the proposed algorithm can greatly decrease the reach high-accurate positions within limited iterations.