考虑隐私的移动边缘计算任务卸载方法
Entropy-Aware Task Offloading in Mobile Edge Computing
- 基于隐私感知的马尔可夫决策过程建模
- 使用深度循环Q网络求解，提升卸载效率
- 适合关注边缘计算隐私安全的研究者
移动边缘计算（MEC）技术被引入以在网络边缘实现云计算，帮助资源受限的移动设备处理时间敏感的数据任务。在此范式下，移动设备可通过无线通信将计算密集型任务卸载至附近的高效MEC服务器。因此，研究重点主要集中在高效卸载方案的开发，而忽视了移动用户的隐私问题。尽管区块链技术被用作数据安全共享的可信机制，但无线通信引发的使用模式和位置隐私问题仍是本文的核心。本文分析了这些隐私顾虑对任务卸载马尔可夫决策过程（MDP）的影响，并采用深度循环Q网络（DRQN）求解该MDP。数值仿真结果表明所提方法的有效性。
原文摘要 · Abstract (English)
Mobile Edge Computing (MEC) technology has been introduced to enable could computing at the edge of the network in order to help resource limited mobile devices with time sensitive data processing tasks. In this paradigm, mobile devices can offload their computationally heavy tasks to more efficient nearby MEC servers via wireless communication. Consequently, the main focus of researches on the subject has been on development of efficient offloading schemes, leaving the privacy of mobile user out. While the Blockchain technology is used as the trust mechanism for secured sharing of the data, the privacy issues induced from wireless communication, namely, usage pattern and location privacy are the centerpiece of this work. The effects of these privacy concerns on the task offloading Markov Decision Process (MDP) is addressed and the MDP is solved using a Deep Recurrent Q-Netwrok (DRQN). The Numerical simulations are presented to show the effectiveness of the proposed method.
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