Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting
Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting
复制标题
通过能量收集实现医疗保健物联网基于学习的隐私感知卸载
DOI：
10.1109/jiot.2018.2875926
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Huaiyu Dai
中科院分区：
文献类型：
--
作者：
Minghui Min;Xiaoyue Wan;Liang Xiao;Ye Chen;Minghua Xia;Di Wu;Huaiyu Dai
移动的边缘计算可帮助具有能量收集功能的医疗物联网（IoT）设备为计算密集型应用提供满意的体验质量。我们提出了一种基于强化学习（RL）的隐私感知卸载方案，以帮助医疗物联网设备保护用户位置隐私和使用模式隐私。更具体地说，该方案使医疗物联网设备能够选择提高计算性能、保护用户隐私并节省物联网设备能量的卸载速率，而无需知道隐私泄露、物联网能耗和边缘计算模型。该方案使用迁移学习来减少初始学习过程中的随机探索，并应用Dyna架构，提供模拟卸载经验来加速学习过程。判决后状态学习方法使用已知的信道状态模型来进一步提高卸载性能。我们提供了该方案在三个典型的医疗物联网卸载场景中的隐私级别，能耗和计算延迟方面的性能界限。仿真结果表明，与基准方案相比，该方案能够降低计算延迟，节省能耗，提高医疗物联网设备的隐私水平。
Mobile edge computing helps healthcare Internet of Things (IoT) devices with energy harvesting provide satisfactory quality of experiences for computation intensive applications. We propose a reinforcement learning (RL)-based privacy-aware offloading scheme to help healthcare IoT devices protect both the user location privacy and the usage pattern privacy. More specifically, this scheme enables a healthcare IoT device to choose the offloading rate that improves the computation performance, protects user privacy, and saves the energy of the IoT device without being aware of the privacy leakage, IoT energy consumption, and edge computation model. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. A post-decision state learning method uses the known channel state model to further improve the offloading performance. We provide the performance bound of this scheme regarding the privacy level, the energy consumption, and the computation latency for three typical healthcare IoT offloading scenarios. Simulation results show that this scheme can reduce the computation latency, save the energy consumption, and improve the privacy level of the healthcare IoT device compared with the benchmark scheme.
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