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

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外文摘要：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 uti-lized 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.

外文关键词：

Intelligent transportation systemDeep reinforcement learningEdge computingPrivacy protec-tion

作者：

Liang YAO、Xiaolong XU、Wanchun DOU、Muhammad Bilal

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作者单位：

School of Software,Nanjing University of Information Science and Technology,Nanjing 210044,China

State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210044,China

Department of Computer and Electronics Systems Engineering,Hankuk University of Foreign Studies,Yongin-si 17035,Korea

基金：

Natural Science Foundation of Jiangsu Province of ChinaNational Natural Science Foundation of ChinaFinancial and Science Technology Plan Project of Xinjiang Production and Construction Corps

项目编号：

BK20211284922671042020DB005

出版年：

2024

DOI：

10.23919/cje.2023.00.111

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(4)

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