Optimal cluster head selection by hybridisation of firefly and grey wolf optimisation

Murugan, T. Senthil; Sarkar, Amit Kumar

doi:10.1504/ijwmc.2018.092373

Cited by 27 publications

(13 citation statements)

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“…However, the last DN round number needs improvement. Murugan and Sarkar [33] proposed a hybrid optimisation using firefly algorithm and grey wolf optimisation, called firefly cyclic grey wolf optimisation. The work focused on the regulation of energy and lowering of separation distance and minimisation of delay.…”

Section: Optimal Chs Methodsmentioning

confidence: 99%

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

Nagarajan

Thangavelu

2020

IET Communications

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Wireless sensor networks (WSNs) are expected to find extensive applicability and accelerating deployment in the future. However, the main challenge faced in WSN is its perishing lifetime. The process of clustering a network is a popular mechanism employed for the purpose of extending the lifespan of WSNs and thereby making efficient data transmission. The main aim of a clustering algorithm is to elect an optimal cluster head (CH). The recent research trend suggests meta‐heuristic algorithms for the selection of optimal CHs. Meta‐heuristic algorithms possess the advantages of being simple, flexible, derivation‐free, and avoids local optima. This research proposes a novel hybrid grey wolf optimiser‐based sunflower optimisation (HGWSFO) algorithm for optimal CH selection (CHS) under certain factor constraints such as energy spent and separation distance, such that the network lifetime is enhanced. Sunflower optimisation (SFO) is employed for a broader search (exploration) where the variation of the step‐size parameter brings the plant closer to the sun in search of global refinement, thus increasing the exploration efficiency. Grey wolf optimisation (GWO) is employed for a narrow search (exploitation), where the parameter coefficient vectors are deliberately required to emphasise exploitation. This balances the exploration‐exploitation trade‐off, prolongs the network lifetime, increases the energy efficiency, and enhances the performance of the network with respect to overall throughput, residual energy of nodes, dead nodes, alive nodes, network survivability index, and convergence rate. The superior characteristic of the suggested HGWSFO is validated by comparing its performance with various other existing CHS algorithms. The overall performance of the proposed HGWSFO is 28.58%, 31.53%, 48.8%, 49.67%, 54.95%, 70.76%, and 87.10%, better than that of GWO, SFO, particle swarm optimisation (PSO), improved PSO, low‐energy adaptive clustering hierarchy (LEACH), LEACH‐centralised, and direct transmission, respectively.

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Section: Optimal Chs Methodsmentioning

confidence: 99%

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

Nagarajan

Thangavelu

2020

IET Communications

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“…The firefly cyclic grey wolf optimization (FCGWO) proposed by Murugan et al. is a hybrid algorithm that uses the foraging behaviour of grey wolves and fireflies [25]. Most studies considered only energy problems in which they overlooked the computational complexity and reliability of communication aspects.…”

Section: Related Workmentioning

confidence: 99%

Cluster optimization in wireless sensor network based on optimized Artificial Bee Colony algorithm

Rekha

2021

IET Networks

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Wireless sensor networks (WSNs) have emerged as a potential research area owing to their wide range of applicability in various fields. Critical application areas of WSN include defence and military surveillance, weather monitoring, health care monitoring, and Internet of Things. Extensive research efforts have been made to improve energy and data delivery performance in WSN with different bio-inspired optimized clustering methodologies such as particle swarm optimization (PSO), and the bacterial foraging algorithm for optimization (BFAO). However, most constrained solutions are limited to data aggregation performance and enhance the energy efficiency of the network to some extent. Therefore, balancing energy and data delivery performance to a greater extent is crucial because of design limitations imposed on existing hierarchical solutions. This article introduces a novel clustering paradigm, namely optimal clustering using the Artificial Bee Colony (OCABC) algorithm, which improves energy efficiency based on a simplified and robust ABC algorithm. The central idea is to increase the network lifetime of the WSN by optimizing the cluster formation process. The implemented structured module of OCABC attempts to overcome challenges encountered in existing baselines. The extensive numerical analysis with respect to significant performance parameters assists in benchmarking the OCABC compared with the PSO and BFAO. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…This Glowworm swarm and Fruitfly algorithm‐based CH scheme was determined to be excellent in terms of residual energy, throughput, and network lifetime in the network. Then, integration of firefly and grey wolf optimization was proposed for balancing the energy of the network . Moreover, biogeography‐based optimization‐based clustering scheme was proposed for identifying the potential CHs in the network .…”

Section: Related Workmentioning

confidence: 99%

“…Then, integration of firefly and grey wolf optimization was proposed for balancing the energy of the network. 28 Moreover, biogeography-based optimization-based clustering scheme was proposed for identifying the potential CHs in the network. 29 This biogeography-based optimization-based clustering scheme was identified to be predominant in terms of throughput, residual energy, the percentage of alive nodes, and percentage of dead nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

An availability predictive trust factor‐based semi‐Markov mechanism for effective cluster head selection in wireless sensor networks

Amuthan

Arulmurugan

2019

Int J Communication

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Summary The longevity of the sensor networks purely depends on the effectiveness of a cluster head selection process that attributes towards effective network management in wireless sensor networks. However, the majority of the cluster head selection schemes are considered an unrealistic condition which ponders the sensor nodes that have the possibility of being selected as cluster heads are highly energy competitive and trustworthy. In this paper, an availability predictive trust factor‐based semi‐Markov mechanism (APTFSMM) was proposed for facilitating effective cluster head selection with the view to enhance its degree of longevity degree in wireless sensor networks. This proposed APTFSMM inherited the merits of semi‐Markov process for estimating availability predictive trust factor that quantifies the maximum likelihood probability under which it is selected as the cluster head through maximized exploration of multiple transition states of the sensors in the networks. This proposed APTFSMM is determined to be predominant in enhancing the lifetime of the sensor network by 31% with a significantly reduced energy consumption rate of 38% compared with the benchmarked cluster head selection approaches considered for investigation.

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Optimal cluster head selection by hybridisation of firefly and grey wolf optimisation

Cited by 27 publications

References 0 publications

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

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

Cluster optimization in wireless sensor network based on optimized Artificial Bee Colony algorithm

An availability predictive trust factor‐based semi‐Markov mechanism for effective cluster head selection in wireless sensor networks

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