eprintid: 3714 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/37/14 datestamp: 2022-09-29 03:03:27 lastmod: 2022-09-29 03:03:28 status_changed: 2022-09-29 03:03:27 type: article metadata_visibility: show creators_name: Bali, Himani creators_name: Gill, Amandeep creators_name: Choudhary, Abhilasha creators_name: Anand, Divya creators_name: Alharithi, Fahd S. creators_name: Aldossary, Sultan M. creators_name: Vidal Mazón, Juan Luis creators_id: creators_id: creators_id: creators_id: divya.anand@uneatlantico.es creators_id: creators_id: creators_id: juanluis.vidal@uneatlantico.es title: Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network ispublished: pub subjects: uneat_eng divisions: uninimx_produccion_cientifica full_text_status: public keywords: clustering; whale optimization; supercluster head (sch); hop count; routing; Wireless Sensor Networks (WSNs); fuzzy inference system (FIS) abstract: In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm date: 2022 publication: Energies volume: 15 number: 14 pagerange: 5237 id_number: doi:10.3390/en15145237 refereed: TRUE issn: 1996-1073 official_url: http://doi.org/10.3390/en15145237 access: open language: en citation: Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm metadata Bali, Himani; Gill, Amandeep; Choudhary, Abhilasha; Anand, Divya; Alharithi, Fahd S.; Aldossary, Sultan M. y Vidal Mazón, Juan Luis mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es (2022) Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network. Energies, 15 (14). p. 5237. ISSN 1996-1073 document_url: http://repositorio.unini.edu.mx/id/eprint/3714/1/energies-15-05237.pdf