Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks

Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN’s goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH...

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Authors: Muñoz-Exposito, Jose-Enrique, Yuste-Delgado, Antonio-Jesús, Triviño-Cabrera, Alicia, Cuevas-Martínez, Juan-Carlos
Format: article
Status:Versión aceptada para publicación
Publication Date:2024
Country:España
Institution:Universidad de Jaén
Repository:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/3198
Online Access:https://doi.org/10.3390/s24175548
https://www.mdpi.com/1424-8220/24/17/5548
https://hdl.handle.net/10953/3198
Access Level:Open access
Keyword:wireless sensor network
fuzzy logic
clustering
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spelling Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor NetworksMuñoz-Exposito, Jose-EnriqueYuste-Delgado, Antonio-JesúsTriviño-Cabrera, AliciaCuevas-Martínez, Juan-Carloswireless sensor networkfuzzy logicclusteringWireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN’s goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.MDPI, Basel, Switzerland.202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.3390/s24175548https://www.mdpi.com/1424-8220/24/17/5548https://hdl.handle.net/10953/3198reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésSensors 2024, 24, 5548.Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/31982026-06-24T12:41:07Z
dc.title.none.fl_str_mv Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
title Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
spellingShingle Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
Muñoz-Exposito, Jose-Enrique
wireless sensor network
fuzzy logic
clustering
title_short Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
title_full Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
title_fullStr Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
title_full_unstemmed Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
title_sort Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
dc.creator.none.fl_str_mv Muñoz-Exposito, Jose-Enrique
Yuste-Delgado, Antonio-Jesús
Triviño-Cabrera, Alicia
Cuevas-Martínez, Juan-Carlos
author Muñoz-Exposito, Jose-Enrique
author_facet Muñoz-Exposito, Jose-Enrique
Yuste-Delgado, Antonio-Jesús
Triviño-Cabrera, Alicia
Cuevas-Martínez, Juan-Carlos
author_role author
author2 Yuste-Delgado, Antonio-Jesús
Triviño-Cabrera, Alicia
Cuevas-Martínez, Juan-Carlos
author2_role author
author
author
dc.subject.none.fl_str_mv wireless sensor network
fuzzy logic
clustering
topic wireless sensor network
fuzzy logic
clustering
description Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN’s goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/s24175548
https://www.mdpi.com/1424-8220/24/17/5548
https://hdl.handle.net/10953/3198
url https://doi.org/10.3390/s24175548
https://www.mdpi.com/1424-8220/24/17/5548
https://hdl.handle.net/10953/3198
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors 2024, 24, 5548.
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI, Basel, Switzerland.
publisher.none.fl_str_mv MDPI, Basel, Switzerland.
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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