BCC: Blockchain-Based Collaborative Crowdsensing in Autonomous Vehicular Networks

被引:30
作者
Hui, Yilong [1 ]
Huang, Yuanhao [1 ]
Su, Zhou [2 ,3 ]
Luan, Tom H. [4 ]
Cheng, Nan [1 ]
Xiao, Xiao [1 ]
Ding, Guoru [5 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China
[3] Xi An Jiao Tong Univ, Sch Cyber Sci & Engn, Xian 710049, Peoples R China
[4] Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China
[5] Army Engn Univ, Coll Commun Engn, Nanjing 210007, Peoples R China
来源
IEEE INTERNET OF THINGS JOURNAL | 2022年 / 9卷 / 06期
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Crowdsensing; Task analysis; Games; Sensors; Security; Privacy; Blockchains; Autonomous vehicular networks (AVNs); blockchain; coalition game; vehicular crowdsensing; RESOURCE-ALLOCATION; INTERNET; PRIVACY; ARCHITECTURE; MANAGEMENT; SERVICES; SCHEME; SECURE; GAMES;
D O I
10.1109/JIOT.2021.3105547
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.
引用
收藏
页码:4518 / 4532
页数:15
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