Revisiting Relative Location Estimation in Wireless Sensor Networks

Relative location estimation plays an important role of localization in wireless sensor networks (WSNs). In WSNs with planned deployment of anchor nodes, some prior information may be available. Existing work on relative location estimation rarely takes into account the lognormal fading effect of wireless channel and the prior probability of the link distance to each reachable anchor node. As a result, when applied to such environments, they may not work effectively. In this paper, we propose a new model called Probability-based Maximum Likelihood (PML) for relative location estimation. With some prior information, the estimation accuracy can be improved significantly. We also discuss the impact of over-estimation and under-estimation of the distance to each reachable anchor node on the accuracy of relative location estimation, and introduce the concept of the compensation factor to combat such effects. The simulation results show that the proposed PML outperforms existing solutions in terms of estimation accuracy for WSNs with planned deployment of anchor nodes.