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A parallel particle swarm optimization for improving wireless sensor networks longevity-based dynamic clustering method

2026-03Artigo científico Acesso aberto
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dc.contributor.authorAbdelaziz, Ahmed
dc.contributor.authorMahmoud, Alia Nabil
dc.contributor.authorSantos, Vítor
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier Science Publisher B.V.
dc.date.accessioned2026-01-14T15:23:18Z
dc.date.available2026-01-14T15:23:18Z
dc.date.issued2026-03
dc.descriptionAbdelaziz, A., Mahmoud, A. N., & Santos, V. (2026). A parallel particle swarm optimization for improving wireless sensor networks longevity-based dynamic clustering method. Array, 29, Article 100633. https://doi.org/10.1016/j.array.2025.100633 --- This work has been supported by Portuguese funds through FCT - Fundaçao ~ para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020, Lisbon, Portugal, and this work has been supported by Information Management Research Center (MagIC) - NOVA Information Management School, Lisbon, Portugal.
dc.description.abstractDetermining the optimal configuration for wireless sensor networks (WSNs) can be challenging due to the multitude of possible setups. To address this issue, our team has developed the Parallel Particle Swarm Optimization-based Self-Organizing Network Clustering (PPSOPM) method. By taking into account variables like remaining node energy, predictable energy usage, proximity to the base station, and number of nearby nodes, PPSOPM dynamically enhances wireless sensor node clusters. Achieving a balance between these factors is crucial to effectively organize nodes into clusters and select a surrogate node as the cluster's head. In comparison to alternative methods, PPSOPM significantly improves network structure by 44.39 % and extends network lifespan. However, node density may impact network longevity by increasing the distance between nodes. Also, when the base station is far from the sensor area, creating additional clusters can help conserve energy. On average, PPSOPM requires 0.57 s to complete, with a standard deviation of 0.04.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent7000633
dc.identifier.doi10.1016/j.array.2025.100633
dc.identifier.issn2590-0056
dc.identifier.otherPURE: 123409518
dc.identifier.otherPURE UUID: 9be6b20e-86d3-42c9-97b9-e5c366c48674
dc.identifier.otherScopus: 105024208571
dc.identifier.otherWOS: 001640782100002
dc.identifier.otherORCID: /0000-0002-4223-7079/work/199423671
dc.identifier.urihttp://hdl.handle.net/10362/198925
dc.identifier.urlhttps://www.scopus.com/pages/publications/105024208571
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001640782100002
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.subjectWireless sensor networks
dc.subjectParallel particle swarm optimization
dc.subjectClustering
dc.subjectEnergy consumption
dc.subjectGeneral Computer Science
dc.titleA parallel particle swarm optimization for improving wireless sensor networks longevity-based dynamic clustering methoden
dc.typejournal article
degois.publication.titleArray
degois.publication.volume29
dspace.entity.typePublication
rcaap.rightsopenAccess

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