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

2015Book part Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorLanza-Gutierrez, Jose M.
dc.contributor.authorGomez-Pulido, Juan A.
dc.contributor.authorMendes, Silvio
dc.contributor.authorM. Ferreira
dc.contributor.authorPereira, J. S.
dc.date.accessioned2025-10-24T15:09:16Z
dc.date.available2025-10-24T15:09:16Z
dc.date.issued2015
dc.descriptionSource title Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings
dc.description.abstractThis 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.eng
dc.description.sponsorshipThis work was partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the contract TIN2012-30685 (BIO project), and by the Government of Extremadura, with the aid GR10025 to the group TIC015.
dc.identifier.citationLanza-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
dc.identifier.doi10.1007/978-3-319-16549-3_11
dc.identifier.isbn9783319165486
dc.identifier.isbn9783319165493
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10400.8/14370
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-319-16549-3_11
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofApplications of Evolutionary Computation
dc.rights.uriN/A
dc.subjectCoverage
dc.subjectDeployment
dc.subjectEnergy
dc.subjectIndoor
dc.subjectMultiobjective
dc.subjectNSGA-II
dc.subjectReliability
dc.subjectSPEA2
dc.subjectWireless sensor network
dc.titlePlanning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary Techniqueseng
dc.typebook part
dspace.entity.typePublication
oaire.citation.titleApplications of Evolutionary Computation
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameLanza-Gutierrez
person.familyNameMendes
person.familyNamePereira
person.givenNameJose M.
person.givenNameSilvio
person.givenNameJoão
person.identifier.ciencia-id1513-13E9-C8A6
person.identifier.ciencia-idBD1E-268C-60A0
person.identifier.orcid0000-0002-1083-5015
person.identifier.orcid0000-0002-1667-5745
person.identifier.orcid0000-0002-4303-2876
person.identifier.scopus-author-id56269586400
relation.isAuthorOfPublicationea664ff6-edc3-4da6-9b94-91d305492138
relation.isAuthorOfPublicatione23cc83a-4e70-4088-a73d-075808bda28f
relation.isAuthorOfPublicationd236a326-78d1-4be7-afca-0adcdcc4d4ae
relation.isAuthorOfPublication.latestForDiscoveryea664ff6-edc3-4da6-9b94-91d305492138

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