A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Murugadass, G.; Poruran, Sivakumar

doi:10.1002/dac.4538

Cited by 19 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulation outcomes demonstrated that the proposed algorithm greatly surpassed the existing solutions' performance in noisy environments and encouraged further improvement and testing of the metaheuristic methods. For the network lifespan's extension, a proposal for a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO-CA-OCHS) scheme was given in Murugadass et al [15]. The proposed scheme utilized the belief space's merits, framed by the cultural algorithm for defining a separating operator that was potent for constructing new local optimal solutions within the search space.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

Veerapaulraj¹,

Karthikeyan²,

Sasipriya³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Routing strategies and security issues are the greatest challenges in Wireless Sensor Network (WSN). Cluster-based routing Low Energy adaptive Clustering Hierarchy (LEACH) decreases power consumption and increases network lifetime considerably. Securing WSN is a challenging issue faced by researchers. Trust systems are very helpful in detecting interfering nodes in WSN. Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem. The metaheuristic Elephant Herding Optimizations (EHO) algorithm is formulated based on elephant herding in their clans. EHO considers two herding behaviors to solve and enhance optimization problem. Based on Elephant Herd Optimization, a trust-based security method is built in this work. The proposed routing selects routes to destination based on the trust values, thus, finding optimal secure routes for transmitting data. Experimental results have demonstrated the effectiveness of the proposed EHO based routing. The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%, by 1.45%, and by 31.94% than LEACH, Elephant Herd Optimization, and Trust LEACH, respectively at Number of Nodes 3000. As the proposed routing is efficient in selecting secure routes, the average packet loss rate is significantly reduced, improving the network's performance. It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.

show abstract

Section: Related Workmentioning

confidence: 99%

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

Veerapaulraj¹,

Karthikeyan²,

Sasipriya³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…However, this CH selection approach struggles extensively in the process of handling the scalability issues. An integrated elephant herd optimization and cultural algorithm (EHOCA)-based CH selection scheme is proposed by Murugadass and Sivakumar 22 for prolonging the network lifetime with maximized energy stability. This EHOCA approach includes the advantages of belief space for a suitable definition of separating operator that aids in optimal balance between diversification and intensification.…”

Section: Intelligent Metaheuristic Optimization Algorithm-based Ch Selection Schemesmentioning

confidence: 99%

“…This ABOA is identified to be predominantly based on the different sets of evaluation conducted using benchmarked functions. 22 However, they exhibit some limitations when they are employed in the real-life engineering optimization problems such as CH selection that focus on network lifetime enhancement in WSNs. Moreover, the standard ABOA variants are identified to be incapable of determining the global optimal solution.…”

Section: Modified African Buffalo and Group Teaching Optimization Algorithm (Mabgtoa) For Cluster Head Selectionmentioning

confidence: 99%

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

Balamurugan

Janakiraman

Priya

2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are capable of offering data dissemination among the nodes such that the exploration of a network's potential could be performed based on the frequency range. It is highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issues of network lifetime, coverage, scheduling and data aggregation. Prolonging network lifetime confirms energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation.Clustering schemes are highly suitable for effectively utilizing the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. This problem of energy-aware optimization included in the clustering process is an NP optimization problem. At this juncture, metaheuristic optimization algorithms are potential candidates for optimizing energy that attributes towards predominant sustenance in network lifetime. In this article, a modified African buffalo and group teaching optimization algorithm (MABGTOA) is proposed for achieving energy stability and maintaining network lifetime by efficient cluster head (CH) selection during the process of clustering. This MABGTOA scheme is developed for sustaining the tradeoff existing between the rate of exploitation and exploration that aids in efficient selection of CHs, thus maintaining lifetime and energy stability in the network. The simulation experiments of the proposed MABGTOA confirm its predominance by offering increased throughput by 18.21% and sustenance in residual energy by 15.48% when compared to the benchmarked schemes taken for comparative investigation.

show abstract

“…14 But this work mainly concentrates on the development of hybrid swarm intelligent clustering approaches with load balancing for achieving heterogeneous arbitrary deployment, which minimizes the energy consumption and increases network lifetime. 15 Moreover, the proposed schemes considered load balancing and heterogeneity as the two eyes during clustering for improving the network efficiency. It adopted a load balancing-imposed clustering mechanism for constructing the clusters in such a way that only minimized load is imposed over the clusters that are constructed near to the BS.…”

Section: Introductionmentioning

confidence: 99%

“…The main motivation of this proposed work, nevertheless, focusses on the improvement of energy efficiency in the network 14 . But this work mainly concentrates on the development of hybrid swarm intelligent clustering approaches with load balancing for achieving heterogeneous arbitrary deployment, which minimizes the energy consumption and increases network lifetime 15 . Moreover, the proposed schemes considered load balancing and heterogeneity as the two eyes during clustering for improving the network efficiency.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

Kumar,

Subramanyam,

Kumar

2023

Int J Communication

View full text Add to dashboard Cite

SummaryClustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real‐life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade‐off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.

show abstract

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Cited by 19 publications

References 39 publications

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

Modified African buffalo and group teaching optimization algorithm‐based clustering scheme for sustaining energy stability and network lifetime in wireless sensor networks

Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

Contact Info

Product

Resources

About