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This paper proposes a novel hybrid approach to routing in Wireless Sensor Networks that combines Q-learning and Learning Automata models. It is designed to optimize the routing process by leveraging the strengths of both techniques: Q-learning’s ability to adapt to dynamic network conditions and Learning Automata’s fast adaptation and convergence in stable scenarios. Preliminary analysis indicates feasibility of the proposed approach, showing that it can improve the network lifetime and packet delivery ratio when compared with similar routing protocols.

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A Hybrid Q-Learning Automata Routing Protocol for Wireless Sensor Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Jakub Gąsior

摘要

This paper proposes a novel hybrid approach to routing in Wireless Sensor Networks that combines Q-learning and Learning Automata models. It is designed to optimize the routing process by leveraging the strengths of both techniques: Q-learning’s ability to adapt to dynamic network conditions and Learning Automata’s fast adaptation and convergence in stable scenarios. Preliminary analysis indicates feasibility of the proposed approach, showing that it can improve the network lifetime and packet delivery ratio when compared with similar routing protocols.