WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks

[EN] Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are esse...

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Autores: Kumar, Navneet, Singh, Karan, Lloret, Jaime|||0000-0002-0862-0533
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/220001
Acesso em linha:https://riunet.upv.es/handle/10251/220001
Access Level:acceso abierto
Palavra-chave:Wireless sensor networks
Whale optimization algorithm
Ant colony optimization
Energy-efficient routing
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spelling WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networksKumar, NavneetSingh, KaranLloret, Jaime|||0000-0002-0862-0533Wireless sensor networksWhale optimization algorithmAnt colony optimizationEnergy-efficient routing[EN] Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs; clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid Whale-Ant Optimization Algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the Whale Optimization Algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony Optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.ElsevierDepartamento de ComunicacionesEscuela Politécnica Superior de GandiaUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/220001reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2200012026-06-13T07:49:27Z
dc.title.none.fl_str_mv WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
title WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
spellingShingle WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
Kumar, Navneet
Wireless sensor networks
Whale optimization algorithm
Ant colony optimization
Energy-efficient routing
title_short WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
title_full WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
title_fullStr WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
title_full_unstemmed WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
title_sort WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks
dc.creator.none.fl_str_mv Kumar, Navneet
Singh, Karan
Lloret, Jaime|||0000-0002-0862-0533
author Kumar, Navneet
author_facet Kumar, Navneet
Singh, Karan
Lloret, Jaime|||0000-0002-0862-0533
author_role author
author2 Singh, Karan
Lloret, Jaime|||0000-0002-0862-0533
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Comunicaciones
Escuela Politécnica Superior de Gandia
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Wireless sensor networks
Whale optimization algorithm
Ant colony optimization
Energy-efficient routing
topic Wireless sensor networks
Whale optimization algorithm
Ant colony optimization
Energy-efficient routing
description [EN] Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs; clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid Whale-Ant Optimization Algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the Whale Optimization Algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony Optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/220001
url https://riunet.upv.es/handle/10251/220001
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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