Connectivity-Based Sensor Localization for Anisotropic Networks by Stress Relaxation

We propose an algorithm for the connectivity-based sensor localization. We formulate the sensor localization into a variant of the classical MDS, in which the squared error between the measured inter-sensor distances and the calculated ones based on estimated sensor locations is used as a stress function. The proposed formulation is applicable even if the distances of several sensor pairs are unknown, and thus it does not require the minimum-hop-count assumption to estimate all intersensor distances. This feature is suitable especially for anisotropic networks, in which the minimum-hop-count assumption does not hold in general. We propose a technique, referred to as stress relaxation in this paper, which allows us to get a good solution while avoiding local minimums of the stress function. Simulation experiments verify that the proposed algorithm significantly outperforms the localization scheme based on the classical MDS.