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Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary Techniques

Lanza-Gutierrez, J. ; Gomez-Pulido , J. ; Mendes, S. P. M. ; Ferreira, M. ; Pereira, J.

Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary Techniques, Proc EvoStar European Conf. on the Applications of Evolutionary Computation - EvoApplications, Copenhagen, Denmark, Vol. 9028, pp. 128 - 139, April, 2015.

Digital Object Identifier: 10.1007/978-3-319-16549-3_11

Abstract
This work deals with how to efficiently deploy an indoor wireless sensor network, assuming a novel approach in which we try to leverage existing infrastructure. Thus, given a set of low-cost sensors, which can be plugged into the grid or powered by batteries, a collector node, and a building plan, including walls and plugs, the purpose is to deploy the sensors optimising three conflicting objectives: average coverage, average energy cost, and average reliability. Two MultiObjective (MO) genetic algorithms are assumed to solve this issue, NSGA-II and SPEA2. These metaheuristics are applied to solve the problem using a freely available data set. The results obtained are analysed considering two MO quality metrics: hypervolume and set coverage. After applying a statistical methodology widely accepted, we conclude that SPEA2 provides the best performance on average considering such data set.