Blockchain-Based Secure Computation Offloading in Vehicular Networks

被引:44
|
作者
Zheng, Xiao [1 ]
Li, Mingchu [1 ]
Chen, Yuanfang [2 ]
Guo, Jun [1 ]
Alam, Muhammad [2 ]
Hu, Weitong [2 ]
机构
[1] Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China
[2] Hangzhou Dianzi Univ, Sch Cyberspace, Hangzhou 310018, Peoples R China
基金
中国国家自然科学基金;
关键词
Vehicular ad hoc networks; blockchain; software-defined networking; computation offloading; edge-cloud computing; deep reinforcement learning; RESOURCE-ALLOCATION; COMPUTING SYSTEMS; RESEARCH ISSUES; EFFICIENT;
D O I
10.1109/TITS.2020.3014229
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Vehicular ad hoc networks (VANETs) has become an important part of modern intelligent transportation systems (ITS). However, under the influence of malicious mobile vehicles, offloading vehicle tasks to the cloud server is threatened by security attacks. Edge cloud offloading (ECCO) has considered a promising approach to enable latency-sensitive VANET. How to solve the complex computation offloading of vehicles while ensuring the high security of the cloud server is an issue that needs urgent research. In this paper, we studied the safety and offloading of multi-vehicle ECCO system based on cloud blockchain. First, to achieve consensus in the vehicular environment, we propose a distributed hierarchical software-defined VANET (SDVs) framework to establish a security architecture. Secondly, to improve the security of offloading, we propose to use blockchain-based access control, which protects the cloud from illegal offloading actions. Finally, to solve the intensive computing problem of authorized vehicles, we determine task offloading via jointly optimizing offloading decisions, consensus mechanism decisions, allocation of computation resources and channel bandwidth. The optimization method is designed to minimize long-term system of delays, energy consumption, and flow costs for all vehicles. To better resolve the proposed offloading method, we develop a new deep reinforcement learning (DRL) algorithm via utilizing extended deep Q-networks. We evaluate the performance of our framework on access control and offloading through numerical simulations, which have significant advantages over existing solutions.
引用
收藏
页码:4073 / 4087
页数:15
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