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Robust scheduling of wireless sensor networks for target tracking under uncertainty

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  • Lersteau, Charly
  • Rossi, André
  • Sevaux, Marc

Abstract

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.

Suggested Citation

  • Lersteau, Charly & Rossi, André & Sevaux, Marc, 2016. "Robust scheduling of wireless sensor networks for target tracking under uncertainty," European Journal of Operational Research, Elsevier, vol. 252(2), pages 407-417.
  • Handle: RePEc:eee:ejores:v:252:y:2016:i:2:p:407-417
    DOI: 10.1016/j.ejor.2016.01.018
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    References listed on IDEAS

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    1. Yuri Sotskov & Albert Wagelmans & Frank Werner, 1998. "On the calculation of the stability radiusof an optimal or an approximate schedule," Annals of Operations Research, Springer, vol. 83(0), pages 213-252, October.
    2. L. R. Ford, Jr. & D. R. Fulkerson, 1956. "Solving the Transportation Problem," Management Science, INFORMS, vol. 3(1), pages 24-32, October.
    3. Sotskov, Yuri N. & Dolgui, Alexandre & Portmann, Marie-Claude, 2006. "Stability analysis of an optimal balance for an assembly line with fixed cycle time," European Journal of Operational Research, Elsevier, vol. 168(3), pages 783-797, February.
    4. Castaño, Fabian & Bourreau, Eric & Velasco, Nubia & Rossi, André & Sevaux, Marc, 2015. "Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks," European Journal of Operational Research, Elsevier, vol. 241(1), pages 28-38.
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    Cited by:

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    2. Keskin, Muhammed Emre, 2017. "A column generation heuristic for optimal wireless sensor network design with mobile sinks," European Journal of Operational Research, Elsevier, vol. 260(1), pages 291-304.
    3. Seddik, Yasmina & Hanzálek, Zdenek, 2017. "Match-up scheduling of mixed-criticality jobs: Maximizing the probability of jobs execution," European Journal of Operational Research, Elsevier, vol. 262(1), pages 46-59.
    4. Mac Cawley, Alejandro & Maturana, Sergio & Pascual, Rodrigo & Tortorella, Guilherme Luz, 2022. "Scheduling wine bottling operations with multiple lines and sequence-dependent set-up times: Robust formulation and a decomposition solution approach," European Journal of Operational Research, Elsevier, vol. 303(2), pages 819-839.
    5. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).
    6. Lersteau, Charly & Rossi, André & Sevaux, Marc, 2018. "Minimum energy target tracking with coverage guarantee in wireless sensor networks," European Journal of Operational Research, Elsevier, vol. 265(3), pages 882-894.

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