Linearly decreasing inertia weight PSO and improved weight factor-based clustering algorithm for wireless sensor networks

In wireless sensor networks (WSNs), clustering is one of the most effective routing protocols. Most clustering algorithms include two stages of operations: cluster head selection and cluster formation. Cluster heads are selected from the sensor nodes based on several key parameters like residual ene...

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Vydané v:Journal of ambient intelligence and humanized computing Ročník 14; číslo 6; s. 6661 - 6679
Hlavní autori: Choudhary, Shilpa, Sugumaran, S., Belazi, Akram, El-Latif, Ahmed A. Abd
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Clustering
Communication
Computational Intelligence
Energy consumption
Engineering
Inertia
Literature reviews
Microelectromechanical systems
Nodes
Optimization algorithms
Optimization techniques
Original Research
Parameters
Particle swarm optimization
Protocol
Residual energy
Robotics and Automation
Sensors
User Interfaces and Human Computer Interaction
Wireless sensor networks
Wireless sensor networks
Clustering
Particle swarm optimization (PSO)
Energy-efficient
ISSN:1868-5137, 1868-5145
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Popis
Shrnutí:In wireless sensor networks (WSNs), clustering is one of the most effective routing protocols. Most clustering algorithms include two stages of operations: cluster head selection and cluster formation. Cluster heads are selected from the sensor nodes based on several key parameters like residual energy of the cluster heads candidates, the distance of cluster heads from their cluster members, and the distance between cluster head and the base station. Cluster formation deals with the association of sensor nodes with one of the selected cluster heads. This paper presents an energy-efficient clustering algorithm with linearly decreasing inertia weight particle swarm optimization (PSO) and improved weight factor. The interia weight PSO is based on cluster head selection and the improved weight factor-based cluster formation. The merit of the proposed approach is the robust formulation of a linear weight factor that leads to efficient cluster formation. The efficacy of the proposed algorithm is verified via different scenarios with varying numbers of nodes and different positions of base stations. Results are compared with some of the existing algorithms, and it is found that the proposed approach outperforms other approaches in terms of various evaluation parameters.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-021-03534-w