Exact and heuristic approaches for the maximum lifetime problem in sensor networks with coverage and connectivity constraints
RAIRO - Operations Research - Recherche Opérationnelle, Tome 51 (2017) no. 3, pp. 607-625.

The aim of the Connected Maximum Lifetime Problem is to define a schedule for the activation intervals of the sensors deployed inside a region of interest, such that at all times the activated sensors can monitor a set of interesting target locations and route the collected information to a central base station, while maximizing the total amount of time over which the sensor network can be operational. Complete or partial coverage of the targets are taken into account. To optimally solve the problem, we propose a column generation approach which makes use of an appropriately designed genetic algorithm to overcome the difficulty of solving the subproblem to optimality in each iteration. Moreover, we also devise a heuristic by stopping the column generation procedure as soon as the columns found by the genetic algorithm do not improve the incumbent solution. Comparisons with previous approaches proposed in the literature show our algorithms to be highly competitive, both in terms of solution quality and computational time.

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Accepté le :
DOI : 10.1051/ro/2017032
Classification : 90C05, 90C06, 90C59
Mots clés : Maximum lifetime, wireless sensor network, column generation, genetic algorithm, steiner tree, partial coverage
Carrabs, Francesco 1 ; Cerulli, Raffaele 1 ; D’Ambrosio, Ciriaco 1 ; Raiconi, Andrea 1

1 Department of Mathematics, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy.
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     title = {Exact and heuristic approaches for the maximum lifetime problem in sensor networks with coverage and connectivity constraints},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
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Carrabs, Francesco; Cerulli, Raffaele; D’Ambrosio, Ciriaco; Raiconi, Andrea. Exact and heuristic approaches for the maximum lifetime problem in sensor networks with coverage and connectivity constraints. RAIRO - Operations Research - Recherche Opérationnelle, Tome 51 (2017) no. 3, pp. 607-625. doi : 10.1051/ro/2017032. http://www.numdam.org/articles/10.1051/ro/2017032/

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