基于社会感知的边缘计算系统中的隐私感知任务分配
Towards Privacy-aware Task Allocation in Social Sensing based Edge Computing Systems
摘要：
随着移动计算、物联网和无处不在的无线连接的发展，基于社会感测的边缘计算（SSEC）已经成为一种新的计算范例，其中人们和他们个人拥有的设备从物理世界收集传感器测量结果并在网络边缘处理它们。本文重点研究了一个隐私感知的任务分配问题，其目标是在尊重用户自定义隐私设置的同时优化SSEC系统中的计算任务分配。为了实现这一目标，提出了一种新的基于博弈论的隐私感知任务分配（G-PATA）框架。G-PATA包括（i）一个自下而上的博弈模型，在满足终端用户隐私设置的同时，在终端设备上产生最大收益;（ii）一个自上而下的激励机制，调整任务的奖励，以确保终端设备做出的任务分配决策满足应用程序的服务质量（QoS）要求。此外，该框架结合了一个有效的负载均衡和迭代减少组件，以适应终端设备的状态和隐私配置的动态变化。G-PATA框架是在一个真实的边缘计算平台上实现的，该平台由异构终端设备（Jetson TX 1和TK 1板以及Raspberry Pi 3）组成。我们比较G-PATA与国家的最先进的任务分配方案，通过两个现实世界的社会传感应用。结果表明，在各种隐私设置下，G-PATA的性能明显优于现有方法（与基线相比，我们的方案在应用程序延迟减少方面实现了47%的改进，终端设备的收益增加了15%）。
英文摘要：
With the advance in mobile computing, Internet of Things, and ubiquitous wireless connectivity, social sensing based edge computing (SSEC) has emerged as a new computation paradigm where people and their personally owned devices collect sensor measurements from the physical world and process them at the edge of the network. This paper focuses on a privacy-aware task allocation problem where the goal is to optimize the computation task allocation in SSEC systems while respecting the users' customized privacy settings. It introduces a novel Game-theoretic Privacy-aware Task Allocation (G-PATA) framework to achieve the goal. G-PATA includes (i) a bottom-up game-theoretic model to generate the maximum payoffs at end devices while satisfying the end user's privacy settings; (ii) a top-down incentive scheme to adjust the rewards for the tasks to ensure that the task allocation decisions made by end devices meet the Quality of Service (QoS) requirements of the applications. Furthermore, the framework incorporates an efficient load balancing and iteration reduction component to adapt to the dynamic changes in status and privacy configurations of end devices. The G-PATA framework was implemented on a real-world edge computing platform that consists of heterogeneous end devices (Jetson TX1 and TK1 boards, and Raspberry Pi3). We compare G-PATA with state-of-the-art task allocation schemes through two real-world social sensing applications. The results show that G-PATA significantly outperforms existing approaches under various privacy settings (our scheme achieved as much as 47% improvements in delay reduction for the application and 15% more payoffs for end devices compared to the baselines.).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
