Efficient estimator for distributed RSS-based localization in wireless sensor networks

We address the received signal strength (RSS) based target localization problem in large-scale cooperative wireless sensor networks (WSNs). Using the noisy RSS measurements, we formulate the localization problem based on the maximum likelihood (ML) criterion. Although ML-based solutions have asymptotically optimal performance, the derived localization problem is non-convex. To overcome this difficulty, we propose a convex relaxation leading to second-order cone programming (SOCP) estimator, which can be solved efficiently by interior-point algorithms. Moreover, we investigate the case where target nodes limit the number of cooperating nodes by selecting only those neighbors with the highest RSS. This simple procedure can reduce the energy consumption of an algorithm in both communication and computation phase. Our simulation results show that the proposed approach outperforms significantly the existing ones in terms of the estimation accuracy and convergence. Furthermore, the new approach does not suffer significant performance degradation when the number of cooperating nodes is reduced.