Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks

Kathiroli, Panimalar; Selvadurai, Kanmani

doi:10.1016/j.jksuci.2021.08.031

Cited by 65 publications

(36 citation statements)

References 17 publications

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“…Node clustering begins after the deployment of the sensors to the area. The CHs are chosen both randomly [52] and based on the use of some criteria such as the CH signal that was the greatest [53], [54], distance, and residual energy of the CH [55]. Energy-aware fuzzy clustering algorithm (EAFCA) proposed by [56] is employed in the formation of the clusters and the choice of CHs.…”

Section: Nodes Clusteringmentioning

confidence: 99%

An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

et al. 2023

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The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage.

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Section: Nodes Clusteringmentioning

confidence: 99%

An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

et al. 2023

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“…In Wireless Sensor Networks, a hybrid SSA with DE is designed to alleviate the energy efficiency issue by cluster head selection [ 25 ]. The proposed approach combined the SSA's high-level search efficiency with DE's lively potential, extending node lifespan.…”

Section: Methods Of Ssamentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

192

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Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

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“…The sparrow search algorithm (SSA) was proposed as a new algorithm to solve optimization problems in 2020 [62] , [63] . The core principle of SSA is from the group predatory behavior of sparrows in nature [63] , [64] , [65] . SSA divides sparrow group into finder and follower.…”

Section: Methodsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks

Cited by 65 publications

References 17 publications

An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

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