Hybrid grey wolf sunflower optimisation algorithm for energy‐efficient cluster head selection in wireless sensor networks for lifetime enhancement

Nagarajan, L; Thangavelu, Shankar

doi:10.1049/cmu2.12072

Cited by 41 publications

(24 citation statements)

References 41 publications

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“…The simulation experiments of the HGDEOA and the competitive HFGWOA-CHS, ISSHSOA-CHS, FHSOA-OCS, and HWSPSO-CHS-based CH selection approaches are conducted using MATLAB 2016 [32][33][34][35] with the system configuration of Intel i5 processor, 1 TB HD, and 4 GB RAM working with Windows 10 at 2.6 GHz. The simulation environment considered for investigation covers 500 sensor nodes deployed randomly in a two-dimensional (2D) network with the area of 1500 Â 1500m 2 .…”

Section: Simulation Results and Discussionmentioning

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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Section: Simulation Results and Discussionmentioning

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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“…An energy-efficient cluster head selection in wireless sensor networks for lifetime enhancement using hybrid grey wolf optimization algorithm based sunflower optimization algorithm was demonstrated by Nagarajan et al [27]. Network survivability index, throughput and residual energy were the parameters utilized to enhance the efficiency and network lifetime.…”

Section: Theoretical Backgrounds On Cluster Based Routing Protocolsmentioning

confidence: 99%

“…( 25), the d T l and d t l denotes the current position and new chaser fish position. b; È; LðÞ and a signifies the probability distribution function, entry-wise multiplication factor, levy distribution factor and levy index respectively [27]. From Eq.…”

Section: Chasing Processmentioning

confidence: 99%

“…The network lifetime determines the total rounds when the energy is completely exhausted from the total sensor nodes. The comparative analysis is carried out for the proposed approach and various other existing approaches namely ant colony integrated glow warm swarm optimization (ACI-GWO) [32], butterfly optimization algorithm and ant colony optimization (BACO) [22], flower pollination algorithm (FPA) [23], Hybrid grey wolf sunflower optimization (HGWS) [27]. The experimental analysis is performed and the analysis revealed that the network lifetime of the proposed approach was high than other existing approaches.…”

Section: Performance Analysismentioning

confidence: 99%

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Modified Optimization for Efficient Cluster-based Routing Protocol in Wireless Sensor Network

Almasri¹,

Alajlan²

2022

Intelligent Automation &Amp; Soft Computing

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Wireless Sensor Networks (WSN) comprise numerous sensor nodes for monitoring specific areas. Great deals of efforts have been achieved to obtain effective routing approaches using clustering methods. Clustering is considered an effective way to provide a better route for transmitting the data, but cluster head selection and route generation is considered as a complicated task. To manage such complex issues and to enhance network lifetime and energy consumption, an energy-effective cluster-based routing approach is proposed. As the major intention of this paper is to select an optimal cluster head, this paper proposes a modified golden eagle optimization (M-GEO) algorithm to figure out the most significant issue of choosing an optimal cluster head in every cluster. The M-GEO algorithm selects an optimal cluster head among all the sensors by employing diverse factors namely the residual energy, node degree, distance among the nearby sensors, centrality of sensor nodes as well as distance between the cluster head and sink node. Additionally, the yellow saddle goatfish (YSG) optimization algorithm is employed in generatingan optimal routing path from the cluster head to the base station. Also, the YSG optimization algorithm detectsthe shortest routing path thereby minimizing the energy consumption. Then later, the performance analyses for various parameters are performed to evaluate the performance of the proposed approach.

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“…Normal CHS techniques and optimum CHS techniques are two types of CHS systems. The best CHS techniques are still classified as meta-heuristic and heuristic procedures [3]. These methods are designed to provide answers for the optimization of algorithms using concise and simple concepts, which are frequently explorative.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Hamza¹,

Hashim²,

Elkamchouchi³

et al. 2023

Computer Systems Science and Engineering

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Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

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Hybrid grey wolf sunflower optimisation algorithm for energy‐efficient cluster head selection in wireless sensor networks for lifetime enhancement

Cited by 41 publications

References 41 publications

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

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

Modified Optimization for Efficient Cluster-based Routing Protocol in Wireless Sensor Network

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

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