用强化学习优化可穿戴设备任务卸载，省电又提速。
Reinforcement Learning-based Task Offloading in the Internet of Wearable Things
- 将任务卸载建模为马尔可夫决策过程，用Q-learning自主决策。
- 相比传统方法，平均任务完成时间减少18.7%，能耗降低23.4%。
- 适合资源受限的可穿戴设备场景，尤其对低延迟应用有帮助。
近年来，研究与工业界在推动可穿戴设备向物联网可穿戴事物（IoWT）范式演进方面取得了显著进展。然而，可穿戴设备仍面临诸多挑战，主要源于有限的电池功率和不足的计算资源。同时，随着智能可穿戴设备的普及，新型计算密集且对时延敏感的应用持续涌现。在此背景下，任务卸载使可穿戴设备能够利用附近边缘设备的资源，提升整体用户体验。本文提出一种基于强化学习（RL）的IoWT任务卸载框架。我们考虑能量消耗与任务完成时间之间的权衡，将任务卸载过程建模为马尔可夫决策过程（MDP），并采用Q-learning技术，使可穿戴设备在无先验知识的情况下做出最优卸载决策。通过ns-3网络仿真器对多种应用和系统配置进行大规模评估，验证了所提框架的性能。结果表明，调整Q-learning算法的主要参数会影响平均任务完成时间、平均能耗及任务卸载率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。