Robust scheduling of wireless sensor networks for target tracking under uncertainty

• Published in: European journal of operational research Vol. 252; no. 2; pp. 407 - 417
• Main Authors: Lersteau, Charly, Rossi, André, Sevaux, Marc
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.07.2016; Elsevier Sequoia S.A; Elsevier
• Subjects: Computer Science; Construction; Detection; Energy efficiency; Linear programming; Moving targets; Operations Research; Remote sensors; Robustness; Scheduling; Scheduling algorithms; Sensors; Stability radius; Studies; Target tracking; Tracking; Tracking control systems; Uncertainty; Wireless networks; Wireless sensor networks; Wireless sensor networks; Target tracking; Uncertainty; Robustness; Stability radius; robustness; wireless sensor networks; stability radius; uncertainty; target tracking
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2016.01.018

•This paper addresses a target tracking robustness problem in Wireless Sensor Networks.•A maximum stability radius schedule is computed with a pseudo-polynomial algorithm.•Two classes of upper bounds are exploited to speed up convergence.•Large problem instances with up to 1000 sensors can be solved in less than 20 seconds. An object tracking sensor network (OTSN) is a wireless sensor network designed to track moving objects in its sensing area. It is made of static sensors deployed in a region for tracking moving targets. Usually, these sensors are equipped of a sensing unit and a non-rechargeable battery. The investigated mission involves a moving target with a known trajectory, such as a train on a railway or a plane in an airline route. In order to save energy, the target must be monitored by exactly one sensor at any time. In our context, the sensors may be not accessible during the mission and the target can be subject to earliness or tardiness. Therefore, our aim is to build a static schedule of sensing activities that resists to these perturbations. A pseudo-polynomial two-step algorithm is proposed. First, a discretization step processes the input data, and a mathematical formulation of the scheduling problem is proposed. Then, a dichotomy approach that solves a transportation problem at every iteration is introduced; the very last step is addressed by solving a linear program.