A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer

Heidari, Ehsan; Movaghar, Ali; Motameni, Homayun; Barzegar, Behnam

doi:10.1002/dac.5148

Cited by 24 publications

(6 citation statements)

References 82 publications

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“…In [ 18 ], the combination of GA and Equilibrium Optimizer (EO) algorithm was utilized for clustering and routing in WSN. The first algorithm selects cluster head nodes and the second algorithm determines data transmission routes in the network.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs)

Zhang

Chen

et al. 2023

PLoS ONE

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WSN is one of the most efficient technologies in intelligent communication and because of its advantages, this technology has been utilized in various applications. By using WSNs, different types of data can be collected and analyzed in wide environments. The high variety of applications and types of data available in this network can cause several challenges about heterogeneous data routing. This research, presents a Fuzzy Model for Content-Centric Routing (FMCCR) in WSN to solve these challenges. The performance of FMCCR is based on two basic steps: "topology control", and "data transmission through content-centric and fuzzy logic-based routing algorithm". In the first step of FMCCR, the network topology is constructed. In the second step of the proposed method, data transmission paths are determined based on network topology and content type, and data transmission is performed. The performance of FMCCR has been evaluated in a simulation environment and the results have been compared with previous algorithms. The results show that FMCCR reduce energy consumption and improve the traffic load distribution in the network in addition to increasing the network lifetime. According to the results, FMCCR can increase network lifetime at least 10.74% and at the same time, deliver at least 88.1% more packets through the network, compared to previous methods. These results, prove the efficiency of the proposed method for using in real-world scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs)

Zhang

Chen

et al. 2023

PLoS ONE

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“…In [14], a novel clustering and routing approach was presented. It was mostly depending on GAs and equilibrium optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Barnwal

Prakash

Yadav

2023

Wireless Pers Commun

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Wireless sensor network (WSN) plays a crucial role in the Internet of Things (IoT), which assist to produce seamless information that have a great impact on the network lifetime. Despite the substantial application of the WSN numerous challenges like energy, load balancing, security, and storage exist. Energy efficacy is regarded as an integral part of the design of WSN; this can be achieved by clustering and multi-hop routing technique using metaheuristic optimization algorithm. This paper concentrates on design of metaheuristics cluster-based routing technique for energy-efficient WSN (MHCRT-EEWSN). The presented MHCRT-EEWSN technique mainly concentrates on the improvements of energy efficiency and lifespan of the WSN via clustering and routing process. For effectual clustering process, the MHCRT-EEWSN model utilizes whale moth flame optimization (WMFO) technique can be utilized by the use of fitness function involving intra-cluster distance, inter-cluster distance, energy, and balancing factor. Besides, the MHCRT-EEWSN model employs improved African buffalo optimization (IABO) based routing technique. To select optimal routes in WSN, the IABO algorithm designs a fitness function comprising multiple parameters like residual energy and distance factor. The experimental validation of the MHCRT-EEWSN model can be tested by making use of a series of simulations. A wide-ranging comparative study shows the promising performances of the MHCRT-EEWSN model than other recent methods.

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“…[ [24][25][26][27], [29][30], [32], [38] give the clustering algorithm using fuzzy and other optimization techniques. Krill herd algorithm-based optimized cluster head selection for wireless sensor networks is suggested in [28].…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

Rajpoot,

Avtar,

Pandey

et al. 2023

Preprint

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Wireless Sensor Networks have become a vast field of study. In the past few years, it has been seen that the use of wireless sensor networks in various fields, such as health monitoring, landslide detection, agriculture field, etc., is increased. In such real-life applications, many sensor nodes are required for monitoring and collecting data from the environment, and these sensor nodes work autonomously. The network of complete sensor nodes that works on an application is termed WSN. In a WSN, clustering refers to dividing the sensor nodes into several groups called clusters. A cluster head is selected among all clusters, temporarily acting as the particular cluster's base station. This cluster head is chosen based on several factors, including the node's residual energy, distance from the base station, and distance from other sensor nodes. Mainly the cluster head is responsible for increasing the efficiency and scalability of the entire network. Utilizing the Ordered Weighted Averaging (OWA) operator, we have suggested an algorithm for cluster head selection that works better and uses less energy during data transmission. Simulation results showed that our suggested algorithm outperforms others and extends network lifetime by 5–12%.

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A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer

Cited by 24 publications

References 82 publications

A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs)

A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs)

Improved African Buffalo Optimization-Based Energy Efficient Clustering Wireless Sensor Networks using Metaheuristic Routing Technique

Multi-Objective Clustering Algorithm using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

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