
Spatial and temporal robustness for scheduling a target tracking mission using wireless sensor networks
Delavernhe, Florian; Rossi, André; Sevaux, Marc (2021), Spatial and temporal robustness for scheduling a target tracking mission using wireless sensor networks, Computers and Operations Research, 132, p. 105321. 10.1016/j.cor.2021.105321
Type
Article accepté pour publication ou publiéDate
2021Journal name
Computers and Operations ResearchVolume
132Publisher
Elsevier
Pages
105321
Publication identifier
Metadata
Show full item recordAuthor(s)
Delavernhe, FlorianRossi, André
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Sevaux, Marc
Abstract (EN)
Robust scheduling for target tracking with a wireless sensor network (WSN), focuses on the deployment of a WSN in a remote area to monitor a set of moving targets. Each sensor operates on a battery and is able to communicate with reachable sensors in the network. The targets are typically moving vehicles (planes, trains, cars,…) passing through the area. In order to monitor the targets, an activation schedule is sought such that the sensor network is continuously collecting data about the targets. Additionally, the transfer of the data collected to a base station deployed near the network also has to be planned. In this work, we consider that the trajectories of the targets are estimated. i.e., during the mission, at each time instant t, there is a given position where the target is expected. However, such estimations are inaccurate and deviations can occur. In this work, we formulate the problem of spatial robust scheduling. The aim is to produce an activation schedule for the sensors such that the targets are covered as long as they remain no farther from their estimated positions than a maximized value, called the spatial stability radius of the schedule. Afterwards, we formulate the spatio-temporal robustness problem. It is a bi-objective problem, with a spatial stability radius and a temporal stability radius for covering delays and advances. Two algorithms are proposed to solve these problems, and we show their efficiency through several numerical experiments.Subjects / Keywords
Linear programming; Sensor network; Robust optimization; Target tracking; Spatio-temporal uncertaintiesRelated items
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