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<p>The Internet of Robotic Things (IoRT) integrates cloud robotics, artificial intelligence, and the Internet of Things to work collaboratively and is popularly employed in various autonomous systems. However, these devices often struggle to meet real-time applicability due to limited battery, low computational capability, and high latency, which necessitate high-performance computing and distributed architectures. Therefore, the present work develops an efficient task offloading mechanism by considering a multi-objective optimization approach to reduce energy consumption based on sampling rate, transmission interval, and data processing while achieving the deadline constraints and load balancing. The methodology is executed via fog computing to lower the communication overhead among edge devices and the cloud. Further, the <i>Q</i>-learning approach is integrated with the walrus optimization algorithm to develop <i>Q</i>WaOA. This integration helps improve the balance between exploration–exploitation by incorporating the intelligence of reinforcement learning. The effectiveness of the proposed work is confirmed by simulations, which show that the proposed strategy reduces the energy requirements by at least 27.14% compared to existing methods. The experimental findings validate the proposed schema compared to other existing approaches in enhancing the performance of IoRT devices.</p>

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A walrus optimization algorithm for sustainable internet of robotic things based on Q-Learning

  • 期刊论文

  • 发表日期：

    2025年10月14日

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  • Hirdesh Varshney,
  • Avtar Singh

摘要

The Internet of Robotic Things (IoRT) integrates cloud robotics, artificial intelligence, and the Internet of Things to work collaboratively and is popularly employed in various autonomous systems. However, these devices often struggle to meet real-time applicability due to limited battery, low computational capability, and high latency, which necessitate high-performance computing and distributed architectures. Therefore, the present work develops an efficient task offloading mechanism by considering a multi-objective optimization approach to reduce energy consumption based on sampling rate, transmission interval, and data processing while achieving the deadline constraints and load balancing. The methodology is executed via fog computing to lower the communication overhead among edge devices and the cloud. Further, the Q-learning approach is integrated with the walrus optimization algorithm to develop QWaOA. This integration helps improve the balance between exploration–exploitation by incorporating the intelligence of reinforcement learning. The effectiveness of the proposed work is confirmed by simulations, which show that the proposed strategy reduces the energy requirements by at least 27.14% compared to existing methods. The experimental findings validate the proposed schema compared to other existing approaches in enhancing the performance of IoRT devices.