Reinforcement Learning-based Task Offloading in the Internet of Wearable Things

• URL: http://arxiv.org/abs/2510.07487v1
• Date: Wed, 08 Oct 2025 19:36:35 GMT
• Title: Reinforcement Learning-based Task Offloading in the Internet of Wearable Things
• Authors: Waleed Bin Qaim, Aleksandr Ometov, Claudia Campolo, Antonella Molinaro, Elena Simona Lohan, Jari Nurmi,
• Abstract summary: This paper proposes a framework for Reinforcement Learning (RL)-based task offloading in the Internet of Wearable Things (IoWT)<n>We formulate the task offloading process considering the tradeoff between energy consumption and task accomplishment time.<n>We utilize the Q-learning technique to enable the wearable device to make optimal task offloading decisions without prior knowledge.
• Score: 35.87301608329458
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Over the years, significant contributions have been made by the research and industrial sectors to improve wearable devices towards the Internet of Wearable Things (IoWT) paradigm. However, wearables are still facing several challenges. Many stem from the limited battery power and insufficient computation resources available on wearable devices. On the other hand, with the popularity of smart wearables, there is a consistent increase in the development of new computationally intensive and latency-critical applications. In such a context, task offloading allows wearables to leverage the resources available on nearby edge devices to enhance the overall user experience. This paper proposes a framework for Reinforcement Learning (RL)-based task offloading in the IoWT. We formulate the task offloading process considering the tradeoff between energy consumption and task accomplishment time. Moreover, we model the task offloading problem as a Markov Decision Process (MDP) and utilize the Q-learning technique to enable the wearable device to make optimal task offloading decisions without prior knowledge. We evaluate the performance of the proposed framework through extensive simulations for various applications and system configurations conducted in the ns-3 network simulator. We also show how varying the main system parameters of the Q-learning algorithm affects the overall performance in terms of average task accomplishment time, average energy consumption, and percentage of tasks offloaded.

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