Leader election of dynamic wireless intelligent control machine in sensor network distributed processing

Strategies to streamline energy resources in wireless sensor networks (WSN) are still a hot topic. Many researchers have proposed various models of the WSN protocol so that the base station (BS)/sink can receive data collected from each sensor node. The energy in the WSN, which has a long life span,...

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Main Authors: Eko Sediyono, Ferry Wahyu Wibowo, Hindriyanto Dwi Purnomo
Format: Article
Language:English
Published: Springer 2022-11-01
Series:Journal of King Saud University: Computer and Information Sciences
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Online Access:http://www.sciencedirect.com/science/article/pii/S1319157822003202
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author Eko Sediyono
Ferry Wahyu Wibowo
Hindriyanto Dwi Purnomo
author_facet Eko Sediyono
Ferry Wahyu Wibowo
Hindriyanto Dwi Purnomo
author_sort Eko Sediyono
collection DOAJ
description Strategies to streamline energy resources in wireless sensor networks (WSN) are still a hot topic. Many researchers have proposed various models of the WSN protocol so that the base station (BS)/sink can receive data collected from each sensor node. The energy in the WSN, which has a long life span, will convey a lot of data to the sink until the sensor nodes die. This paper has proposed modeling the WSN protocol by utilizing the classification and clustering of sensor nodes using the chimpanzee leader election optimization (CLEO) method. The CLEO algorithm is a meta-heuristic algorithm inspired by a social model that applies the pattern of leader selection to the chimpanzee community. The optimization method has the advantage of getting a fast fitness value which is one of the critical factors in utilizing meta-heuristic models in the WSN field. The closest sensor nodes at a certain distance to the sink are marked as classification, while other sensor nodes are marked as clusters. The clustering of sensor nodes utilizes a cluster function using the CLEO algorithm. This paper also carried out the selection of group leaders to obtain an energy efficiency model from WSN-CLEO. The results of the proposed method, WSN-CLEO, have been compared to other protocol models.
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publishDate 2022-11-01
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spelling doaj-art-971bf5692d724c77bd89a3494df6c9ad2025-08-20T03:48:31ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-11-0134109146916210.1016/j.jksuci.2022.08.037Leader election of dynamic wireless intelligent control machine in sensor network distributed processingEko Sediyono0Ferry Wahyu Wibowo1Hindriyanto Dwi Purnomo2Doctoral Program of Computer Science, Information Technology Faculty, Satya Wacana Christian University, Salatiga, IndonesiaDoctoral Program of Computer Science, Information Technology Faculty, Satya Wacana Christian University, Salatiga, Indonesia; Informatics Department, Computer Science Faculty, Universitas Amikom Yogyakarta, Jl. Ring Road Utara, Condong Catur, Depok, Sleman, Yogyakarta, Indonesia; Corresponding author at: Doctoral Program of Computer Science, Information Technology Faculty, Satya Wacana Christian University, Salatiga, Indonesia.Doctoral Program of Computer Science, Information Technology Faculty, Satya Wacana Christian University, Salatiga, IndonesiaStrategies to streamline energy resources in wireless sensor networks (WSN) are still a hot topic. Many researchers have proposed various models of the WSN protocol so that the base station (BS)/sink can receive data collected from each sensor node. The energy in the WSN, which has a long life span, will convey a lot of data to the sink until the sensor nodes die. This paper has proposed modeling the WSN protocol by utilizing the classification and clustering of sensor nodes using the chimpanzee leader election optimization (CLEO) method. The CLEO algorithm is a meta-heuristic algorithm inspired by a social model that applies the pattern of leader selection to the chimpanzee community. The optimization method has the advantage of getting a fast fitness value which is one of the critical factors in utilizing meta-heuristic models in the WSN field. The closest sensor nodes at a certain distance to the sink are marked as classification, while other sensor nodes are marked as clusters. The clustering of sensor nodes utilizes a cluster function using the CLEO algorithm. This paper also carried out the selection of group leaders to obtain an energy efficiency model from WSN-CLEO. The results of the proposed method, WSN-CLEO, have been compared to other protocol models.http://www.sciencedirect.com/science/article/pii/S1319157822003202ClassificationChimpanzee leader election optimizationClusteringEnergy efficiencyIntelligent wireless sensor network
spellingShingle Eko Sediyono
Ferry Wahyu Wibowo
Hindriyanto Dwi Purnomo
Leader election of dynamic wireless intelligent control machine in sensor network distributed processing
Journal of King Saud University: Computer and Information Sciences
Classification
Chimpanzee leader election optimization
Clustering
Energy efficiency
Intelligent wireless sensor network
title Leader election of dynamic wireless intelligent control machine in sensor network distributed processing
title_full Leader election of dynamic wireless intelligent control machine in sensor network distributed processing
title_fullStr Leader election of dynamic wireless intelligent control machine in sensor network distributed processing
title_full_unstemmed Leader election of dynamic wireless intelligent control machine in sensor network distributed processing
title_short Leader election of dynamic wireless intelligent control machine in sensor network distributed processing
title_sort leader election of dynamic wireless intelligent control machine in sensor network distributed processing
topic Classification
Chimpanzee leader election optimization
Clustering
Energy efficiency
Intelligent wireless sensor network
url http://www.sciencedirect.com/science/article/pii/S1319157822003202
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AT ferrywahyuwibowo leaderelectionofdynamicwirelessintelligentcontrolmachineinsensornetworkdistributedprocessing
AT hindriyantodwipurnomo leaderelectionofdynamicwirelessintelligentcontrolmachineinsensornetworkdistributedprocessing