Mobile Sensor Networks Self Localization based on Multi-dimensional Scaling

Abstract

In this paper, we define a mobile self-localization (MSL) problem for sparse mobile sensor networks, and propose an algorithm named mobility assisted MDS-MAP(P), based on multi-dimensional scaling (MDS) for solving the problem. For sparse sensor networks, all the existing localization algorithms fail to work properly due to the lack of distance or connectivity data to uniquely calculate the geo-locations. In MSL, we use mobile sensors to add extra distance constraints to a sparse network, by moving the mobile sensors in the area of deployment and recording distances to neighbors at some intermediate locations. MSL can also be used for localizing and tracking mobile objects in a robotic or body sensor network. Experiments and evaluations of the new algorithm are provided.