Mobile Self-Localization using Multi-Dimensional Scaling in Robotic Sensor Networks

In this paper, we define a mobile self-localization (MSL) problem for sparse and/or mobile robotic sensor networks, and propose an algorithm, MA-MDS-MAP(P), based on MultiDimensional Scaling (MDS) for solving the problem. For sparse robotic 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 MA-MDSMAP(P), we use one or more 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 these intermediate locations. MA-MDS-MAP(P) can also be used for localizing and tracking mobile objects in a robotic or body sensor network. Experiments and evaluations of the proposed algorithm are provided.

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