Performance Analysis of Hybrid Optimization Algorithm for Virtual Head Selection in Wireless Sensor Networks

R, Praveen Kumar; Raj, Jennifer S.; Smys, S.

doi:10.1007/s11277-021-09222-4

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…The two-stage method chooses the eligible node in the initial stage with GA and in the next level CH is chosen by the presented approach. In [15], an enhanced convolute system is utilized for predicting whether a node is trustworthy or malicious. The system employs training pattern to decrease the malicious node prediction-error rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Wind Driven Optimization based Energy Aware Clustering Scheme for Wireless Sensor Networks

2024

Teh. vjesn.

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Wireless Sensor Networks (WSNs) are utilised in a variety of applications due to their capacity to capture and transmit environmental data. Clustering has emerged as an efficient method for improving energy efficiency in WSNs. To resolve these issues, we propose an Adaptive Wind Driven Optimisation based Energy Aware Clustering Scheme (AWDO-EACS) for WSNs. The AWDO-EACS model presents an extended form of the Wind Driven Optimisation (WDO) algorithm, designated AWDO, with optimised inherent term values. The proposed model takes into account multiple objectives, such as energy consumption, distance, and end-to-end latency, in order to achieve superior energy efficiency and an extended network lifetime. To validate the efficacy of the AWDO-EACS model, extensive experiments with varying node counts were carried out. In terms of network stability, energy efficiency, end-to-end latency, packet delivery ratio, throughput, packet loss rate, and network lifetime, the results demonstrate that the AWDO-EACS outperforms contemporary clustering strategies. Specifically, the AWDO-EACS obtained a significant increase in energy efficiency, with a 27.35 percent improvement over existing clustering techniques for 20 nodes and an 83.41 percent improvement for 100 nodes. In addition, the end-to-end latency was considerably reduced, with a 96-round lifetime for 20 nodes and a 74-round lifetime for 100 nodes, compared to 37 and 20 rounds, respectively, for GA-LEACH and MW-LEACH. In addition, the AWDO-EACS demonstrated superior packet delivery performance, with a 99.32% delivery ratio for 100 nodes, eclipsing the 76.90% and 82.65% of GA-LEACH and MW-LEACH, respectively. Moreover, the AWDO-EACS model demonstrated a remarkably low packet loss rate of 0.68 percent for 100 nodes, compared to 23.10 percent for GA-LEACH and 17.35 percent for MW-LEACH. The effectiveness of the proposed AWDO-EACS model in enhancing the overall performance of WSNs is demonstrated.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive Wind Driven Optimization based Energy Aware Clustering Scheme for Wireless Sensor Networks

2024

Teh. vjesn.

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“…Praveen Kumar et al 25 have presented the analysis performance of hybrid optimization approach for the selection of virtual head in WSNs. In this, a hybrid optimization technique was presented for improving the energy efficacy and lifetime of network through combined modified particle swarm optimization (PSO) along a genetic approach.…”

Section: Literature Surveymentioning

confidence: 99%

Blockchain‐enabled joint trust (RF‐AOA‐HTSA) algorithm‐based multiobjective clustering protocol for wireless sensor network

Meenakshi¹,

Karunkuzhali²,

Ali³

2023

Concurrency and Computation

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SummaryIn this article, blockchain‐enabled hybrid Red Fox optimization and arithmetic optimization approach‐based cluster head selection along Hazelnut tree search algorithm (HTSA)‐based optimal trust path selection is proposed to secure data transmission at wireless sensor network. The proposed BC‐Hyb‐RF‐AOA‐HTSA‐WSN method consists of two phases: (i) to find optimum cluster head (CH) and (ii) to find optimal trust path. Firstly, hybrid Red Fox optimization approach and arithmetic optimization algorithm are employed to select cluster head accurately. After CH selection, HTSA is used to find trust route from several routes, which is finalized optimally with the joint trust that depends on trust parameters. Finally, blockchain is provided with optimized, carefully chosen trust routes for communication. The proposed BC‐Hyb‐RF‐AOA‐HTSA‐WSN method is activated in NS2 tool. The proposed technique achieves lesser delays of 98.38%, 92.34%, and 97.45%, better delivery ratios of 89.34%, 83.12%, and 88.96%, and lower packet drops of 91.25%, 79.90%, and 92.88% compared with the existing techniques, such as BC‐FA‐ROA‐WSN, BC‐RDA‐WSN, and BC‐HRDSS‐WSN.

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Packet Delivery Comparison Using Artificial Bee Colony Algorithm with Dynamic Technique

Beg

Waoo

2022

Human-Centric Smart Computing

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Performance Analysis of Hybrid Optimization Algorithm for Virtual Head Selection in Wireless Sensor Networks

Cited by 9 publications

References 28 publications

Adaptive Wind Driven Optimization based Energy Aware Clustering Scheme for Wireless Sensor Networks

Adaptive Wind Driven Optimization based Energy Aware Clustering Scheme for Wireless Sensor Networks

Blockchain‐enabled joint trust (RF‐AOA‐HTSA) algorithm‐based multiobjective clustering protocol for wireless sensor network

Packet Delivery Comparison Using Artificial Bee Colony Algorithm with Dynamic Technique

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