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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| Published in: | Ad hoc networks Vol. 174; p. 103837 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.07.2025
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| ISSN: | 1570-8705 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 103837 |
| Author | Jiaqi, Zhou Jing, Li Zhaohui, Zhang |
| Author_xml | – sequence: 1 givenname: Zhang orcidid: 0009-0003-1254-3758 surname: Zhaohui fullname: Zhaohui, Zhang – sequence: 2 givenname: Zhou surname: Jiaqi fullname: Jiaqi, Zhou – sequence: 3 givenname: Li orcidid: 0000-0003-3041-7600 surname: Jing fullname: Jing, Li email: lijingmath@xidian.edu.cn |
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| Keywords | Wireless sensor networks Ant colony optimization Semi-fixed clustering Q-learning Network lifetime Energy efficiency |
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| SubjectTerms | Ant colony optimization Energy efficiency Network lifetime Q-learning Semi-fixed clustering Wireless sensor networks |
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