GoDeep:云边缘计算中具有隐私保护的智能车联网服务部署和执行

Wentao Liu, Xiaolong Xu, Lianyong Qi, Xuyun Zhang, Wanchun Dou
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引用次数: 5

摘要

云计算(cloud computing, CC)具有强大的计算能力,可以满足车联网(Internet of vehicle, IoV)的业务执行,但云计算的远距离通信往往带来较高的时延。考虑到CC的不足,边缘计算(edge computing, EC)将业务执行转移到边缘,从而减少了执行延迟。由于边缘服务器存储和计算资源的限制,如何将部分业务动态部署到边缘服务器仍然是一个难题。此外,单一资源受限的ES更容易受到攻击,增加了车辆隐私泄露的风险。本文提出了一种在云边缘计算中具有隐私保护的智能车联网服务部署和执行方法GoDeep。具体来说,采用同态加密技术构建了一种面向车联网服务执行的隐私感知机制。此外,提出了一种基于深度确定性策略梯度(DDPG)的业务部署方案。最后,通过仿真实验对GoDeep的性能进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GoDeep: Intelligent IoV Service Deployment and Execution with Privacy Preservation in Cloud-edge Computing
With the powerful computing capability, cloud computing (CC) is employed to accommodate services execution for the Internet of vehicles (IoV), but the long-distance communication of CC often brings high latency. Considering the shortcomings of CC, edge computing (EC) shifts service execution to the edge, which decreases the execution delay. Due to the storage and computing resource limits of edge servers (ESs), how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the single resource-constrained ES is more vulnerable to attacks, which increases the leakage risk of vehicle privacy. In this article, a method for intelligent IoV service deployment and execution with privacy preservation in cloud-edge computing, named GoDeep, is proposed. Specifically, homomorphic encryption is adopted to construct a privacy-aware mechanism for IoV service execution. Besides, a service deployment scheme based on deep deterministic policy gradient (DDPG) is proposed. Eventually, the performance of GoDeep is evaluated by the simulation experiments.
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