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Q-learning-based semi-fixed clustering routing algorithm in WSNs
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DOI：10.1016/j.adhoc.2025.103837.png)
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Abstract
En 中文
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.
Keywords:
Wireless sensor networks
Semi-fixed clustering
Ant colony optimization
Q-learning
Energy efficiency
Network lifetime