Title: An integer linear programming approach for optimising energy consumption in mobile wireless sensor networks under realistic constraints
Authors: Khadidja Fellah; Bouabdellah Kechar
Addresses: Industrial Computing and Networks Laboratory (RIIR), Department of Computer Sciences, Faculty of Exact and Applied Sciences, University of Oran1 Ahmed Ben Bella, P.O. Box 1524 El M'naouar, Oran, Algeria ' Industrial Computing and Networks Laboratory (RIIR), Department of Computer Sciences, Faculty of Exact and Applied Sciences, University of Oran1 Ahmed Ben Bella, P.O. Box 1524 El M'naouar, Oran, Algeria
Abstract: Recent advances in miniature mobile robotics have fostered the emergence of mobile wireless sensor networks (MWSN). As sensor nodes are battery-powered devices, the first important constraint is how to reduce the energy consumption to extend the lifetime of the network. In this paper, we propose an optimisation approach based on integer linear programming (0-1 ILP) to significantly reduce energy consumption in MWSN. To do so, we formulate the problem as an objective function aiming to minimise the overall energy consumption related to communication operations and the mobility of the sensor nodes and/or sink. The developed formulation is performed under some realistic and relevant constraints of MWSN, such as sensing coverage, connectivity, adjustable transmission range and the case of adjustable sensing range. The proposed approach is evaluated using flat and cluster-based topologies by doing intensive experiments using the CPLEX solver. The obtained results reveal that the mobility factor in MWSN coupled with the considered additional constraints makes it possible to extend significantly the network lifetime.
Keywords: adjustable sensing range; adjustable transmission range; coverage and connectivity; energy saving; integer linear programming; mobility; MWSN; optimisation.
International Journal of Mathematical Modelling and Numerical Optimisation, 2017 Vol.8 No.2, pp.162 - 182
Received: 04 Feb 2017
Accepted: 05 Jun 2017
Published online: 13 Sep 2017 *