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

Lanza-Gutierrez, Jose M.; Gomez-Pulido, Juan A.; Mendes, Silvio; M. Ferreira; Pereira, J. S.

http://hdl.handle.net/10400.8/14370

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Authors

Lanza-Gutierrez, Jose M.

Gomez-Pulido, Juan A.

Mendes, Silvio

M. Ferreira

Pereira, J. S.

Abstract(s)

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.

Description

Source title Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings

Keywords

Coverage Deployment Energy Indoor Multiobjective NSGA-II Reliability SPEA2 Wireless sensor network

URI

http://hdl.handle.net/10400.8/14370

Citation

Lanza-Gutierrez, J.M., Gomez-Pulido, J.A., Priem-Mendes, S., Ferreira, M., Pereira, J.S. (2015). Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary Techniques. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_11