Q-learning-based semi-fixed clustering routing algorithm in WSNs

In recent years, cluster-based routing protocols have emerged as a core technology for Wireless Sensor Networks (WSNs), attracting significant attention from researchers. This paper introduces a novel semi-fixed clustering algorithm, SFC-QL-IACO, designed to maintain energy balance in WSNs. The algo...

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Veröffentlicht in:Ad hoc networks Jg. 174; S. 103837
Hauptverfasser: Zhaohui, Zhang, Jiaqi, Zhou, Jing, Li
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.07.2025
Schlagworte:
Ant colony optimization
Energy efficiency
Network lifetime
Q-learning
Semi-fixed clustering
Wireless sensor networks
Wireless sensor networks
Ant colony optimization
Semi-fixed clustering
Q-learning
Network lifetime
Energy efficiency
ISSN:1570-8705
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Zusammenfassung:In recent years, cluster-based routing protocols have emerged as a core technology for Wireless Sensor Networks (WSNs), attracting significant attention from researchers. This paper introduces a novel semi-fixed clustering algorithm, SFC-QL-IACO, designed to maintain energy balance in WSNs. The algorithm employs semi-fixed clustering to redistribute cluster nodes for initial load balancing and utilizes Q-Learning and enhanced ant colony optimization to construct data transmission paths. Clusters are dynamically adjusted when the energy difference exceeds a specified threshold to ensure energy balance. A dynamic energy threshold is implemented to prevent network disruptions caused by the depletion of cluster head energy, with cluster head rotation occurring as needed. Simulation results show that SFC-QL-IACO outperforms existing algorithms in terms of energy consumption, load balancing, and network lifetime.
ISSN:1570-8705
DOI:10.1016/j.adhoc.2025.103837