Mobile cluster head selection using soft computing technique in wireless sensor network

Prabaharan, G.; Jayashri, S.

doi:10.1007/s00500-019-04133-w

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…According to the authors of [20,25], PSO performs exceedingly better than both LEACH and LEACH-C in terms of network duration and average throughput. A mobile data collection scheme in WSN using soft computing-based CH selection and clustering has been suggested to resolve the energy problem [21]. A fuzzy inference paradigm was used to construct it.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

SHANTHI¹,

PRASANNA²

2023

Preprint

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The primary goal of wireless sensor networks from a design point of view is to enhance the network's lifetime. Among the different options for reducing operational energy consumption, energy invested in routing and cluster head selection is considered to be very effective mechanisms. Both tasks have been considered as very challenging and difficult to obtain the efficient solution. Since it is difficult for traditional approaches to satisfy the requirements and difficulties, a heuristic solution focused on natural computation methods has provided a lot of naivety. The proposed work efforts to address these challenges using computational intelligence especially differential evolution and genetic algorithm. An energy efficient route discovery for dynamic network is designed with variations in DE, quick and adaptable routes were discovered for networks undergoing changes. A knowledge based DE has been designed depending on prior knowldge to redefine new routes for changing network. A hybrid mutation strategy under standard DE is designed for cluster head selection providing faster convergence characteristics. The proposed solutions were implemented under MATlab environment and the results have shown that the proposed solutions are performing better for different network configurations. Dynamic route discovery using KDE has achieved energy saving of 9.83 to 49.2 percentage as compared to RDE and 6.7 to 29.5 percentage as compared to PDE. Also energy saving attained in cluster head selection using proposed HMDE is 10 to 33 percentage better compared to dyPSO and 5 to 10 percentage better compared to SDE.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

SHANTHI¹,

PRASANNA²

2023

Preprint

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“…The method in Prabaharan & Jayashri (2019) selects the smart CH in WSNs to prolong the network lifetime. Optimal CH selection is performed for extending the network lifetime of WSN by using various attributes of sensor nodes ( Kalaimani, Zah & Vashist, 2020 ; Rajpoot & Dwivedi, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks

Muthukkumar

Garg

Maharajan

et al. 2022

PeerJ Computer Science

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Background The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge. Methods This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node. Results Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.

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“…It is highly and similarly significant to consider optimized quantity of heterogeneous nodes to be positioned in the network. 11 The heterogeneous sensor nodes on par with the normal nodes possess the capability of provisioning more amount of network resources with respect to memory, link range, link capacity, etc. 12 However, these heterogeneous nodes need to be less in number of the objective of balancing the cost factor.…”

Section: Introductionmentioning

confidence: 99%

“…In specific, the heterogeneity of network has the positive impact over network reliability and increases the probability of network lifetime in spite of the increase in the deployment cost. It is highly and similarly significant to consider optimized quantity of heterogeneous nodes to be positioned in the network 11 . The heterogeneous sensor nodes on par with the normal nodes possess the capability of provisioning more amount of network resources with respect to memory, link range, link capacity, etc 12 .…”

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

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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.

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Mobile cluster head selection using soft computing technique in wireless sensor network

Cited by 13 publications

References 29 publications

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous 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

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