Energy Efficient Cluster Head Selection using Hybrid Squirrel Harmony Search Algorithm in WSN

Lavanya, N.; Shankar, T.

doi:10.14569/ijacsa.2019.0101265

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…It also made each sensor member to initiate route discovery that aids in constructing the reliable path to the CH. Squirrel and Harmony Search Optimization Algorithm (SHAOA)‐based clustering was proposed by Lavanya and Shankar 20 for facilitating optimal CH selection that results in potential energy stability in the network. It adopted the fitness function that evaluated the parameters of energy distance during each round for the objective of optimal CH selection.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm‐based clustering for guaranteeing energy stability and network lifetime in WSN

Rajarajeswari¹,

Shyamala²,

Mohana³

2023

Int J Communication

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Summary Wireless sensor networks (WSNs) plays an indispensable role in the human life by supporting a diversified number of applications that includes military, environment monitoring, manufacturing, education, agriculture, etc. However, the sensor node batteries cannot be replaced under its deployment in an unattended or remote area due to their wireless existence. Cluster‐based routing is significant in handling the issue of energy stability and network lifetime. The meta‐heuristic algorithms‐based cluster head (CH) selection is determined to be highly promising for attaining the objective of CH selection that results in acquiring an optimal network performance. In this paper, a Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm (HGICSOA)‐based clustering scheme is proposed for attaining potential CH selection and guarantee significant sink mobility‐based data transmission. The capability of GHOA that controls the rate of exploitation and exploration degree is utilized for CH selection. It specifically adopted OBL‐based GHOA for optimal CH selection based on the objective function, which is formulated using node density, residual energy, and distance between sensor node and sink. It incorporated new CSOA for mobility‐based data transmission for increasing population diversity. It also utilized the benefits of ICSOA with a predominant local search strategy for achieving better sink mobility‐based data transmission. Simulation and statistical results confirmed that the proposed HGICSOA is better in attaining maximum energy stability by 17.21% and improved network lifetime by 23.82%, compared to the benchmarked schemes used for investigation. Moreover, the prevention rate of worst sensor nodes selected as CH is improved by 21.38%, better than baseline approaches.

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Section: Related Workmentioning

confidence: 99%

Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm‐based clustering for guaranteeing energy stability and network lifetime in WSN

Rajarajeswari¹,

Shyamala²,

Mohana³

2023

Int J Communication

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“…An Integrated Squirrel Search and Harmony Search Optimization Algorithm‐based CH selection scheme is proposed by Lavanya and Shankar 20 by considering the energy and distance factors for achieving equalized energy balance among sensor nodes during the clustering process. The first node death and last node death of sensor nodes visualized during the implementation of ISSHSOA‐CHS are fairly healthier than the competitive approaches of the literature.…”

Section: Related Workmentioning

confidence: 99%

Hybrid grasshopper and differential evolution algorithm for prolonging network life expectancy in wireless sensor networks (WSNs)

Manoharan¹,

Ponnusamy²

2022

Int J Communication

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In wireless sensor networks (WSNs), restricted battery power, balanced energy consumption, and collaborative data processing are considered as key challenges that need to be handled with utmost care. Clustering in WSNs is an optimal methodology for conserving energy of sensor nodes and attaining efficient data processing that attributes toward the maximization of network lifetime. An efficient cluster head (CH) selection scheme is essential for achieving superior collaborative data processing in WSNs. Swarm intelligent metaheuristic algorithm-based CH selection approaches are identified to be better for designing energy-efficient schemes that select optimal CHs from nodes in a fair way. In this paper, Hybrid Grasshopper and Differential Evolution-based Optimization Algorithm (HGDEOA) is proposed for targeting on the objective of attaining energy stability and prolonging network lifetime. This HGDEOA incorporates an adaptive strategy into differential evolution (DE) for improving the global searching capability during the process of optimization. It is proposed for improving the convergence efficiency and maintaining population diversity. It also possesses the capability of enhancing the degree of convergence speed and precision in calculation. This integration of GOA into DE prevents the premature convergence of the algorithm, since the deviation amid the scaling individuals induces the population to be more randomly distributed, targeting at the retention of population diversity. The simulation results confirm that the proposed HGDEOA sustains residual energy by 19.32%, improves throughput by 16.21%, prolongs network lifetime by 18.76%, and maintains stability by18.94% when compared to the benchmarked approaches used for investigation.

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“…After the section of sub cell, the next section is to find out the available sub-node selection and the equation number (11) shows the selection method. Node position error estimation rate.…”

Section: Active Sub Cell Processmentioning

confidence: 99%

“…The hierarchical systems also use a clustering system of nodes and in this situation; the nodes make cluster groups where each node joined a cluster. This clustering scheme is more energy-efficient and easily for the manager [10,11].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Strategy for WSN Technology Using Modified HGAF Technique

Aznaoui

Raghay²,

Ullah

et al. 2021

Int. J. Onl. Eng.

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<p class="0affiliation">Due to the rapid growth in technologies has led to the development of sensor nodes. As we know that wireless sensor network is collection of small and large number of sensor node. These sensor nodes are used for different domain like environmental research, health care, monitor, military, and record the physical activity. These nodes communicate with each other and forward the message to base station. For communication of these node different algorithms used Geographical adaptive fidelity (GAF) is one of them. Dropping energy utilization in wireless sensor network directly affects the network lifetime. HGAF is one of the important multiple location based on routing system algorithm. The main function of HGAF technique is to turn-off the unnecessary nodes in the network without interrupting the other connected node. In this paper we proposed a technique known as modified HGAF and it design as a power saving method. In the proposed technique the size of cell structure in grid changed and communication method improve due to those changes. Based on the result the proposed technique increase 25% in dead node ratio and also increase the network.</p>

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Energy Efficient Cluster Head Selection using Hybrid Squirrel Harmony Search Algorithm in WSN

Cited by 10 publications

References 25 publications

Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm‐based clustering for guaranteeing energy stability and network lifetime in WSN

Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm‐based clustering for guaranteeing energy stability and network lifetime in WSN

Hybrid grasshopper and differential evolution algorithm for prolonging network life expectancy in wireless sensor networks (WSNs)

Energy Efficient Strategy for WSN Technology Using Modified HGAF Technique

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