A novel cluster head selection using Hybrid Artificial Bee Colony and Firefly Algorithm for network lifetime and stability in WSNs

Sengathir, J.; Rajesh, A.; Dhiman, Gaurav; Vimal, S.; Yogaraja, C.A.; Viriyasitavat, Wattana

doi:10.1080/09540091.2021.2004997

Cited by 30 publications

(23 citation statements)

References 39 publications

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“…Furthermore, IBOA enables local search (exploitation) for modifying the location using Equation (11).…”

Section: Iboa-based Ch Selectionmentioning

confidence: 99%

“…These optimization schemes attribute toward global intelligence through local and simple behaviors. The majority of the present meta‐heuristic CH selection mechanisms are determined to be significant in exploitation or exploration, but not both, as they fail to balance the rate of exploration as well as exploitation in the optimization of parameters 11 . The prevailing meta‐heuristic CH selection schemes confirmed that integration of a global search improved meta‐heuristic algorithm would be the best option of balancing the tradeoffs between exploitation and exploration rate.…”

Section: Introductionmentioning

confidence: 95%

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Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Janakiraman¹

2023

Int J Communication

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Summary In this paper, improved bat and enhanced artificial bee colony optimization algorithm‐based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)‐based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.

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“…Furthermore, IBOA enables local search (exploitation) for modifying the location using Equation (11).…”

Section: Iboa-based Ch Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Janakiraman¹

2023

Int J Communication

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“…The algorithm's performance is validated using different node scenarios, achieving a better network lifetime. Sengathir et al [52] proposed a hybrid modified algorithm, namely artificial bee colony and firefly algorithm (HMABCFA), to elect the optimal cluster head. The author improved the exploration and exploitation process in the standard ABC and FA algorithm and achieved a trade-off between search processes.…”

Section: Hybrid Metaheuristic Approachesmentioning

confidence: 99%

An Opposition-based Grey Wolf Optimization for Cluster Head Selection in Wireless Sensor Networks

Ramalingam

Dinesh

et al. 2022

Preprint

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Clustering is considered one of the practical approaches for boosting the lifespan of the Wireless Sensor Networks (WSNs). It involves in gathering the sensor nodes into groups and elects the cluster heads (CHs) in each group. CHs gather the data from cluster members and transfer the aggregate information to Base Station (BS). However, the most significant obligation in WSN is to elect the optimal CH to enhance the network's lifespan. This paper proposes an optimal cluster head election framework in WSN. A novel hybrid technique selects the optimal CHs: an oppositional grey wolf optimization (OGWO) algorithm that collaborates with generic GWO and opposition-based learning techniques. The hybrid OGWO algorithm dynamically balances between intensification and diversification search process in electing optimal CHs. In addition, the parameters like energy, distance, node degree, and node centrality aid in selecting the optimal CHs in the network. This CHs selection framework improves the efficacy of the network capability and enhances the network lifespan. Further, the superiority of the proposed OGWO technique is validated based on the various impacts like energy, alive nodes, BS location, and several packet delivery aspects. Accordingly, the proposed OGWO technique provides a better network lifetime of ~ 20%, ~ 30% and ~ 45% compared with GWO, ABC and LEACH techniques.

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“…In addition, the nodes which are located in farthest proximity from the sink nodes derive the benefits of other cooperating sensor nodes in the network for forwarding their own data packets to the sink node [10]. The sensor nodes, which are very close to the sink node drains maximum amount of energy due to their act of data forwarding even though it is not transmitting its own data [11].…”

Section: Figure 1: Lustering-based Wireless Sensor Network (Wsns)mentioning

confidence: 99%

“…The observation as well as movement is modelled to mislead victims when hunting in the local search. To deal with the likelihood of being observed when approaching nearer to prey, a random value μ ∈ ⟨0, 1⟩ set in the iteration for foxes in the populace that defines the foxes' actions is given in Equation (11).…”

Section: Navigating Through the Local Habitat (Local Search Stage)mentioning

confidence: 99%

Hybrid Red Fox and Improved Whale Optimization Algorithm-based Clustering scheme for improving network longevity in Wireless Sensor Networks

Anandkumar¹,

Rao²,

Rajaboina

2022

Preprint

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Guaranteeing energy stability is highly challenging in Wireless Sensor Networks (WSNs) as it contributes towards an enhanced network lifespan. Clustering with improved energy efficacy is found to be an NP-hard optimization problem with the possibility of offering extended network lifespan. Several computational methods that comprise of nature inspired meta-heuristic as well as reinforcement schemes along with evolutionary algorithms are used for supporting effectual clustering in WSNs. The heuristic and meta-heuristic schemes of CH selection designed for dealing with optimization problems using empirical behaviour was identified to be ideal in better clustering process. In this paper, Hybrid Red Fox and Improved Whale Optimization Algorithm (HRFIWOA)-based Clustering scheme is proposed enhancing lifespan expectation and energy constancy in WSNs. This HRFIWOA is proposed with the global optimization competence of the Red Fox Optimization Algorithm (RFOA) and local optimization capability of Improved Whale Optimization Algorithm (IWOA). It specifically included IWOA into RFOA for achieving and sustaining a balance amid the stages of exploration and exploitation which is demanded for optimised CH selection. The adopted IWOA helps in better exploitation of the solution that comprises of information pertaining to the sensor nodes which can be potentially identified as CH in the network. The simulation results of the proposed HRFIWOA confirmed promising capability in improving the packet deliver rate by 20.96%, network lifespan by 18.54%, throughput by 17.62% with reduced latency of 19.28%, and minimized energy consumption of 22.39%, better than the competitive CH selection schemes used for investigation.

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A novel cluster head selection using Hybrid Artificial Bee Colony and Firefly Algorithm for network lifetime and stability in WSNs

Cited by 30 publications

References 39 publications

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

An Opposition-based Grey Wolf Optimization for Cluster Head Selection in Wireless Sensor Networks

Hybrid Red Fox and Improved Whale Optimization Algorithm-based Clustering scheme for improving network longevity in Wireless Sensor Networks

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