An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

被引:1
|
作者
Yao, Liang [1 ]
Xu, Xiaolong [1 ,2 ]
Dou, Wanchun [2 ]
Bilal, Muhammad [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Software, Nanjing 210044, Peoples R China
[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210044, Peoples R China
[3] Hankuk Univ Foreign Studies, Dept Comp & Elect Syst Engn, Yongin 17035, South Korea
基金
中国国家自然科学基金;
关键词
Industries; Privacy; Energy consumption; Transportation; Computational efficiency; Encryption; Protection; Intelligent transportation system; Deep reinforcement learning; Edge computing; Privacy protection; RESOURCE-ALLOCATION; SECURE;
D O I
10.23919/cje.2023.00.111
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As a pivotal enabler of intelligent transportation system (ITS), Internet of vehicles (IoV) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive, and privacy-aware vehicular applications in IoV result in the transformation from cloud computing to edge computing, which enables tasks to be offloaded to edge nodes (ENs) closer to vehicles for efficient execution. In ITS environment, however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
引用
收藏
页码:910 / 919
页数:10
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