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

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 comp...

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Veröffentlicht in:	The Journal of supercomputing Jg. 81; H. 15; S. 1464
Hauptverfasser:	Varshney, Hirdesh, Singh, Avtar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 14.10.2025 Springer Nature B.V
Schlagworte:	Algorithms Artificial intelligence Compilers Computation offloading Computer Science Cost control Data processing Edge computing Energy consumption Energy efficiency Energy requirements Internet of Things Interpreters Machine learning Multiple objective analysis Neural networks Optimization Performance enhancement Processor Architectures Programming Languages Real time Robot learning Robotics Internet of robotic things Multi-objective optimization Load balancing Cloud robotics Task offloading Energy optimization
ISSN:	1573-0484, 0920-8542, 1573-0484
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Abstract	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 Q 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.
AbstractList	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. 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 Q 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.
ArticleNumber	1464
Author	Varshney, Hirdesh Singh, Avtar
Author_xml	– sequence: 1 givenname: Hirdesh surname: Varshney fullname: Varshney, Hirdesh email: hirdeshv.cs.21@nitj.ac.in organization: Department of Computer Science and Engineering, Dr B. R. Ambedkar National Institute of Technology Jalandhar – sequence: 2 givenname: Avtar surname: Singh fullname: Singh, Avtar organization: Department of Computer Science and Engineering, Dr B. R. Ambedkar National Institute of Technology Jalandhar
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SubjectTerms	Algorithms Artificial intelligence Compilers Computation offloading Computer Science Cost control Data processing Edge computing Energy consumption Energy efficiency Energy requirements Internet of Things Interpreters Machine learning Multiple objective analysis Neural networks Optimization Performance enhancement Processor Architectures Programming Languages Real time Robot learning Robotics
Title	A walrus optimization algorithm for sustainable internet of robotic things based on Q-Learning
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