Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Liu, Yang; Xiao, Jing; Li, Chaoqun; Qin, He; Zhou, Jie

doi:10.1155/2021/5511745

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…The quantum computing with a clone operation in the GWO algorithm avoids falling it into a local optimal solution. The optimal design of the sensor duty cycle in industrial WSNs using the quantum clone GWO improves convergence speed and network lifetime compared to GA and SA algorithms [54].…”

Section: Role Of Gwo Algorithm In Optimal Ch Selectionmentioning

confidence: 99%

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

Reddy

Chandra²,

Venkatramana

et al. 2023

Computers

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The internet of things (IoT) and industrial IoT (IIoT) play a major role in today’s world of intelligent networks, and they essentially use a wireless sensor network (WSN) as a perception layer to collect the intended data. This data is processed as information and send to cloud servers through a base station, the challenge here is the consumption of minimum energy for processing and communication. The dynamic formation of cluster heads and energy aware clustering schemes help in improving the lifetime of WSNs. In recent years, grey wolf optimization (GWO) became the most popular feature selection optimizing, swarm intelligent, and robust metaheuristics algorithm that gives competitive results with impressive characteristics. In spite of several studies in the literature to enhance the performance of the GWO algorithm, there is a need for further improvements in terms of feature selection, accuracy, and execution time. In this paper, we have proposed an energy-efficient cluster head selection using an improved version of the GWO (EECHIGWO) algorithm to alleviate the imbalance between exploitation and exploration, lack of population diversity, and premature convergence of the basic GWO algorithm. The primary goal of this paper is to enhance the energy efficiency, average throughput, network stability, and the network lifetime in WSNs with an optimal selection of cluster heads using the EECHIGWO algorithm. It considers sink distance, residual energy, cluster head balancing factor, and average intra-cluster distance as the parameters in selecting the cluster head. The proposed EECHIGWO-based clustering protocol has been tested in terms of the number of dead nodes, energy consumption, number of operating rounds, and the average throughput. The simulation results have confirmed the optimal selection of cluster heads with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by using minimum energy levels in WSNs. Using the proposed algorithm, there is an improvement in network stability of 169.29%, 19.03%, 253.73%, 307.89%, and 333.51% compared to the SSMOECHS, FGWSTERP, LEACH-PRO, HMGWO, and FIGWO protocols, respectively.

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Section: Role Of Gwo Algorithm In Optimal Ch Selectionmentioning

confidence: 99%

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

Reddy

Chandra²,

Venkatramana

et al. 2023

Computers

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“…The objective of this paper is to increase the overall lifetime of WSN depending on the density of sensor nodes in the neighbours' "sink" node [6]. A "sink" node is a data collection node sent by the sensor nodes that make up WSN.…”

Section: Introductionmentioning

confidence: 99%

SIPF Protocol for Wireless Sensor Networks with Optimal Energy Efficiency Depending on Density

Boudhiafi¹,

Ezzedine²

2022

Advances in Intelligent Systems Research

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Wireless sensor networks (WSN) are viewed as a group of sensor nodes that monitor an area and interact collectively to provide data directly to the base station (BS) or intermediate sensor nodes through single-hop routing, multi-hop routing. The main characteristics of a WSN are: lifetime, rate of loss and security: The lifetime of a WSN is highly dependent on the energy consumption of the sensor nodes. This limitation makes extending the sensor node's life more difficult. In this paper, we present an upgrade to the Sink Initiated Path Formation (SIPF) protocol that minimizes power consumption and packet loss rate as a function of the density of sensor nodes in the vicinity of the "sink" sensor node.

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Use of Various Optimization Algorithms in the Energy Minimization Problem Domain of WSN: A Survey

De¹,

Banerjee

Majumder

et al. 2022

Lecture Notes in Electrical Engineering

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Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Cited by 7 publications

References 23 publications

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization Algorithm

SIPF Protocol for Wireless Sensor Networks with Optimal Energy Efficiency Depending on Density

Use of Various Optimization Algorithms in the Energy Minimization Problem Domain of WSN: A Survey

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