PLATOS：面向雾计算中医疗物联网的功耗与延迟感知任务导向调度策略
PLATOS: A Power and Latency-Aware Task-Oriented Scheduling Strategy for Healthcare IoT in Fog Computing
中文总结 AI 辅助
针对医疗物联网在雾计算中的延迟和能耗问题，提出PLATOS四阶段任务导向调度策略，在iFogSim2中实现能耗降低18.72%、延迟降低8.65%。
中文摘要 AI 辅助
医疗物联网（Healthcare Internet of Things, HIoT）技术通过实现实时数据收集和分析以支持个性化患者护理，正在彻底改变医疗行业。然而，HIoT技术的快速扩展带来了诸多挑战，例如在雾计算环境中延迟增加和能耗升高，尤其是在管理电池供电设备时。为解决这些问题，本研究提出了一种新颖的调度策略，通过面向任务的调度来优化HIoT任务的功耗和延迟。所提出的策略名为PLATOS（功耗与延迟感知任务导向调度），分四个连续阶段实施。在第一阶段，HIoT任务被分为三类：优先级导向型、存储导向型和计算导向型。第二阶段侧重于延迟优化，通过为每个任务类别识别产生最低执行延迟的雾计算资源来实现。第三阶段通过选择最小化能耗的资源来实现功耗优化。最后，在决策阶段，高性能雾资源被分配给高优先级任务，而其余任务则根据从延迟和功耗优化阶段获得的映射列表进行调度。在iFogSim2中进行的仿真实验表明，与最先进的方法相比，PLATOS将能耗降低了18.72%，延迟降低了8.65%。这些改进提升了HIoT系统的效率和响应能力，并有助于实现更有效的患者护理和主动式医疗服务交付。
英文摘要
Healthcare Internet of Things (HIoT) technology is revolutionising the healthcare industry by enabling real-time data collection and analysis for personalised patient care. However, the rapid expansion of HIoT technology introduces challenges such as increased latency and higher energy consumption in fog computing environments, particularly when managing battery-operated devices. To address these issues, this work proposes a novel scheduling strategy that optimises both power consumption and latency through task-oriented scheduling for HIoT tasks. The proposed strategy, named PLATOS (Power and Latency Aware Task Oriented Scheduling), is implemented in four sequential phases. In the first phase, HIoT tasks are categorised into three groups: priority-oriented, storage-oriented, and computational-oriented. The second phase focuses on latency optimisation by identifying the fog computing resources that yield the lowest execution delay for each task category. In the third phase, power optimisation is achieved by selecting the resources that minimise energy consumption. Finally, in the decision-making phase, high-performance fog resources are allocated to high-priority tasks while the remaining tasks are scheduled based on a mapped list derived from the latency and power optimisation phases. Simulation experiments conducted in iFogSim2 demonstrate that PLATOS reduces energy consumption by 18.72% and latency by 8.65% when compared to the state-of-the-art. These improvements enhance the efficiency and responsiveness of HIoT systems and contribute to more effective patient care and proactive healthcare service delivery.
