Optimal Cluster Head Selection and Clustering for WSN Using PSO

Shinde, Aparna; Bichkar, Rajankumar S.

doi:10.35940/ijitee.b7607.019320

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Clustering in sensor nodes has been widely pursued by the research community in order to solve the scalability, energy and lifetime issues of sensor networks. Clustering algorithms limit the communication in a local domain and transmit only necessary information [40,41]. A group of nodes form a cluster and cluster members generally communicate with the cluster head and the collected data are aggregated and fused by the cluster head to conserve energy.…”

Section: Step1-clustering Via K-means Clusteringmentioning

confidence: 99%

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

K B,

2023

IJEER

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Due to its general use in various practical applications, number of innovations in Wireless Sensor Networks (WSN) is receiving a lot of consideration from researchers. It shows significant technological development with excessive capacity since it gives useful information to users in a particular field through real-time monitoring. Due to its characteristics, such as infrastructure-less adoption and resource limitations, wireless sensor networks bring several problems that could impair the system's operation. Cluster based routing in WSN is the major concern in this field that could conflicts with the effectiveness of energy, suitable Cluster Head (CH) selection, protected data transport as well as network lifetime augmentation, demand major consideration, etc. However, it remains challenging to make optimal clustering process of WSN that makes the routing process more effective. Thereby, this work proposes a new Namib Merged Jelly Search Optimization Algorithm (NMJSOA) approach for the optimal CH selection in WSN by the different constraints. Initially, the sensor nodes are grouped together to form the clusters by the k-means clustering techniques. Subsequently, allocating the CH for each cluster by computing the weight function for each cluster depends on the conditions such as energy, delay, distance and security. According to the NMJSOA method, the Namib Beetle Optimization (NBO) searching position and the Jellyfish swam old position are added together to get the best optimum position. Finally, the performance of the suggested model is investigated over the conventional methods in terms of different performance measures.

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Section: Step1-clustering Via K-means Clusteringmentioning

confidence: 99%

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

K B,

2023

IJEER

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“…(2020) [34] They use PSO to select the CHs, and then the network is formed based on the signal strength received from the nodes.…”

Section: Tabibi and Et Almentioning

confidence: 99%

Analytical Study for Optimization Techniques to Prolong WSNs Life

2023

IJCCCE

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Wireless Sensor Networks (WSNs) have become the most cost-effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life. Index Term— Cluster Head selection, Clustering, PSO, Routing, Wireless Sensor Networks.

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“…al. [12] suggested an algorithm called Enhanced Particle Swarm Optimization (PSO) to perform clustering and select CHs. B. Mamalis et.…”

Section: Introductionmentioning

confidence: 99%

Optimized Routing Protocol Using Metaheuristic Techniques for Improving Energy Efficiency in WSN

Shalika

Meena

Agarwal³

2023

Preprint

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A Wireless Sensor Network is a group of small, autonomous sensor nodes which are connected wirelessly and have sensing, processing, and communication capabilities. These nodes are responsible for gathering and monitoring physical information from the surrounding environment. Since these nodes are typically compact and operate with a battery, they have a limited power supply which leads to a constrained network lifetime. Therefore, the use of energy effective techniques that lengthen the network’s lifespan is highly significant. Hence, the objective of this research work is to reduce energy usage and increase the network’s lifespan. Clustering and optimization techniques are commonly used in sensor network to enhance its stability and lifespan. In a clustered sensor network, cluster heads play a crucial role, as they are responsible for performing various tasks that consume more energy. This research work aims to enhance the effectiveness of the network by proposing a hybrid nature-inspired optimization algorithm named as Energy Efficient Yellow Saddle Goatfish Pelican Optimization algorithm (EEYSGPO) which uses Yellow Saddle Goatfish Algorithm to identify the optimum cluster head from a set of nodes. The parameters like residual energy, distance, delay, load, and communication quality are all used to select the optimized cluster head in the clusters. After choosing the optimized cluster head, pelican optimization technique is used to determine the best route for communication between cluster head and the base station, which is calculated on the basis of distance and residual energy. The MATLAB simulator is used for simulation and the obtained results of suggested technique were compared with several existing energy-efficient techniques such as EECHIGWO, SSMOECHS, FGWSTERP and LEACH-PRO using performance measures such as throughput, dead nodes and energy consumption. Simulation findings reveal that the optimal selection of cluster heads and routes in EEYSPO algorithm resolved the issues related to premature convergence and increase the lifetime and scalability of WSN. When compared to the EECHIGWO, SSMOECHS, LEACH-PRO and FGWSTERP protocols, the proposed methodology improves network stability by 57.28 %, 324.5 %, 571.72 % and 91.37 % respectively.

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Optimal Cluster Head Selection and Clustering for WSN Using PSO

Cited by 4 publications

References 4 publications

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

Analytical Study for Optimization Techniques to Prolong WSNs Life

Optimized Routing Protocol Using Metaheuristic Techniques for Improving Energy Efficiency in WSN

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