Intelligent mobility assisted mobile sensor network localization

The trajectories of mobile seeds have a great influence on localization accuracy and efficiency. This paper presents a novel information-driven intelligent mobility-assisted wireless sensor network localization algorithm. Without requiring any prior knowledge of the sensing field, seeds' or pseudo-seeds' (common sensors which have been positioned) trajectories are scheduled dynamically aiming at position estimates of neighboring non-positioned common sensors. With an information-theoretic utility measure as the objective function, mobile seeds or pseudo-seeds actively determine their motion directions for minimizing the uncertainty in position estimates of neighboring sensors. At the first level, seeds estimate the neighboring sensor nodes' positions with bearing measurements by means of extended Kalman filters and optimize their motion directions by maximizing the mutual information between the position estimates and the motions of seeds. Afterwards the seeds forward the position estimates to the corresponding sensor nodes, which then act as pseudo-seeds. By repeating this process at the following levels, all sensor nodes can obtain position estimates. Compared with heuristic mobility and random mobility-assisted mobile sensor network localization algorithms, the proposed algorithm requires fewer maneuvers of seed or pseudo-seeds for quick convergence to good position estimates. Extensive simulations show that this algorithm can provide more accurate position estimates with fewer maneuvers, especially in the case of limited seeds.