Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged o...

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Bibliographic Details
Published in:World journal of engineering 2022-03, Vol.19 (2), p.147-156
Main Authors: Kaur, Jasleen, Rani, Punam, Dahiya, Brahm Prakash
Format: Article
Language:English
Subjects:
Algorithms
Clusters
Communication
Energy consumption
Energy efficiency
Food
Fruit flies
Literature reviews
Nodes
Optimization
Particle swarm optimization
Search algorithms
Sensors
Wireless networks
Wireless sensor networks
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Summary:Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.
ISSN:1708-5284
1708-5284
DOI:10.1108/WJE-03-2021-0170