Robust scheduling for target tracking using wireless sensor networks

•This work studies multi-target tracking under trajectory uncertainty.•A new upper bound on the stability radius for hop-communication networks is proposed.•Priority areas and long-term usage of the WSN are explicitly taken into account.•The proposed approach is based on a bisection method and on linear programming.•The proposed algorithms have been implemented and the solutions have been analyzed. A wireless sensor network (WSN) is a group of sensors deployed in an area, with all of them working on a battery and with direct communications inside the network. A fairly common situation, addressed in this work, is to monitor and record data with a WSN about vehicles (planes, terrestrial vehicles, boats, etc) passing by an area with damaged infrastructures. In such a context, an activation schedule for the sensors ensuring a continuous coverage of all the targets is required. Furthermore, the collected data, in order to be treated, have to be transmitted to a base station in the area, near the sensors. In this work, the future monitoring missions of the network are also taken into account, as well as the energy consumption of the current mission. We also consider that the spatial trajectories of the targets are known, whereas the speed of the targets along their trajectories are estimated, and subject to uncertainty. Hence, the main objective is to seek solutions that can withstand earliness and tardiness from the previsions. We propose a formulation of the problem with three different objectives and a solution method with experiments and results. The objectives are treated in a lexicographic order as follows (i) maximize the robustness schedule to cope with the advances and delaqui leys of the targets, (ii) maximize the minimum of monitoring time we can guarantee in priority areas, (iii) maximize the amount of energy left in the sensor batteries. We propose new upper bounds on the robustness measure, that are exploited by the solution approach whose complexity is shown to be pseudo-polynomial. The solution approach is based on a preprocessing step called discretisation, and the resolution of a series of linear programs.