Optimizing trajectories of mobile beacons to localize sensor networks

This paper deals with localizing nodes in a sensor network. More precisely, we consider the situation where sensor nodes localize themselves off a mobile beacon node traveling over the deployment area. We describe a genetic approach to derive semioptimal paths for a mobile beacon, where the optimal path is defined as the beacon trajectory resulting in the highest overall localization precision for sensor nodes, with certain constraints on path length. For this, we assume that the beacon periodically broadcasts its location. The sensors can extract this location information as well as the signal strength from received packets to estimate their location. In such localization scenarios, the trajectory of the beacon heavily affects the accuracy of location estimate. To evaluate paths, we employ Cramer Rao bounds (CRB) which provide an unbiased evaluation regardless of the location estimation algorithm. A genetic approach is employed to evolve paths toward an optimal trajectory, with a C++ simulator calculating sensor node CRB estimates as the objective function. We provide a description of the approach and provide insights on what the influence of our genetic algorithm is on the accumulative overall localization CRB.