In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the adversarial competition among multiple IoHT devices, and the ultra reliable and low latency communication (URLLC) constraints have imposed new challenges for task offloading optimization. In this article, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability, occurrence probability of extreme events, and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose a URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. URLLC awareness is achieved by dynamically balancing the URLLC constraint deficits and energy consumption through online learning. We provide a rigorous theoretical analysis to show that guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. Finally, the effectiveness and reliability of UTO-EXP3 are validated through simulation results.

中文翻译：

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用于健康物联网的基于学习的 URLLC 感知任务卸载

在基于健康物联网 (IoHT) 的电子健康范例中，大量计算密集型任务必须从资源有限的 IoHT 设备卸载到邻近的强大边缘服务器，以减少延迟并提高能源效率。然而，全局状态信息 (GSI) 的缺乏、多个 IoHT 设备之间的对抗性竞争以及超可靠和低延迟通信 (URLLC) 的限制为任务卸载优化带来了新的挑战。在本文中，我们将任务卸载问题表述为对抗性多臂老虎机 (MAB) 问题。除了基于平均值的性能指标外，还采用边界违反概率、极端事件的发生概率和超值的统计特性来表征 URLLC 约束。然后，我们提出了一种基于用于探索和利用的指数权重算法（EXP3）的 URLLC 感知任务卸载方案，名为 UTO-EXP3。URLLC 意识是通过在线学习动态平衡 URLLC 约束缺陷和能量消耗来实现的。我们提供了严格的理论分析，以表明仅基于本地信息的 UTO-EXP3 可以实现具有有界偏差的保证性能。最后，通过仿真结果验证了UTO-EXP3的有效性和可靠性。我们提供了严格的理论分析，以表明仅基于本地信息的 UTO-EXP3 可以实现具有有界偏差的保证性能。最后，通过仿真结果验证了UTO-EXP3的有效性和可靠性。我们提供了严格的理论分析，以表明仅基于本地信息的 UTO-EXP3 可以实现具有有界偏差的保证性能。最后，通过仿真结果验证了UTO-EXP3的有效性和可靠性。