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<p>Wireless sensor networks have the major challenge of ensuring energy efficiency, scalability, and data delivery. The achievement of these goals becomes challenging when the nodes have limited battery energy, the network topology changes frequently, and the data transfer is not distributed among the nodes. The routing protocols have been found wanting in various ways, as they consume more energy than required, have difficulties selecting the appropriate nodes as cluster heads, and cannot be adapted when the network topology changes frequently. This eventually reduces the lifespan of the network and affects its performance as a whole. To mitigate these problems, this paper presents a Reinforcement Learning-based Multipath Hybrid Whale-Grey Wolf Optimization framework. Deep Reinforcement Learning is incorporated throughout this framework for adaptive sleep scheduling and node activation. The DRL agent learn and schedules tasks based on residual energy, node centrality, and node proximity to the cluster head as primary factors. This research also suggests a new hybrid optimization algorithm, where the global search capability of the Whale Optimization Algorithm (WOA) and the strong convergence and exploitation capabilities of the Grey Wolf Optimizer (GWO) are hybridized for more effective cluster head selection. This hybridization process is done for a more effective and energy-efficient selection of cluster heads. It has also been proposed that a multipath routing technique will be used to develop multiple stable and energy-efficient paths between the cluster heads and the base station. The stability of the paths, the energy left, and the quality of the paths will be considered while establishing the paths using the hybrid WOA–GWO algorithm. The effectiveness of the proposed framework will be validated through extensive simulations, which prove that the proposed model performs well with a packet delivery ratio of 97.8%, total energy consumption of 320&#xa0;J, and a total throughput at the base station of 72 kbps.</p>

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Reinforcement learning enabled hybrid optimisation for energy-efficient multipath routing in wireless sensor networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • R. Nareshkumar,
  • Prabu Selvam,
  • Karthikeyan Kaliyaperumal,
  • Hussain. A,
  • P. Uma Maheswari

摘要

Wireless sensor networks have the major challenge of ensuring energy efficiency, scalability, and data delivery. The achievement of these goals becomes challenging when the nodes have limited battery energy, the network topology changes frequently, and the data transfer is not distributed among the nodes. The routing protocols have been found wanting in various ways, as they consume more energy than required, have difficulties selecting the appropriate nodes as cluster heads, and cannot be adapted when the network topology changes frequently. This eventually reduces the lifespan of the network and affects its performance as a whole. To mitigate these problems, this paper presents a Reinforcement Learning-based Multipath Hybrid Whale-Grey Wolf Optimization framework. Deep Reinforcement Learning is incorporated throughout this framework for adaptive sleep scheduling and node activation. The DRL agent learn and schedules tasks based on residual energy, node centrality, and node proximity to the cluster head as primary factors. This research also suggests a new hybrid optimization algorithm, where the global search capability of the Whale Optimization Algorithm (WOA) and the strong convergence and exploitation capabilities of the Grey Wolf Optimizer (GWO) are hybridized for more effective cluster head selection. This hybridization process is done for a more effective and energy-efficient selection of cluster heads. It has also been proposed that a multipath routing technique will be used to develop multiple stable and energy-efficient paths between the cluster heads and the base station. The stability of the paths, the energy left, and the quality of the paths will be considered while establishing the paths using the hybrid WOA–GWO algorithm. The effectiveness of the proposed framework will be validated through extensive simulations, which prove that the proposed model performs well with a packet delivery ratio of 97.8%, total energy consumption of 320 J, and a total throughput at the base station of 72 kbps.