Joint detection and tracking of boundaries using cooperative mobile sensor networks

This paper considers a boundary tracking problem using mobile sensor networks, in which we design controllers for the mobile sensors to obtain the boundary of physical events. We set the boundary estimation problem as a classification problem of the region in which the physical events occurs, and employ support vector learning (SVL). By using the hyper-dimensional radius function obtained from SVL, we build the hyper-potential field to generate a velocity vector field which is globally attractive to a desired closed path with circulation at the desired speed. We also study stabilizing the collective configuration of the multiple mobile sensors. To coordinate the mobile sensors in the formation that encloses the boundary, we define virtual phases of mobile sensors and compute the desired speed of each mobile sensors minimizing the level of synchrony of the virtual phases. Both a simulation and an experiment is performed and the results demonstrate that this study provides good performance of the collective boundary tracking.