Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 6, Pages 4518-4532Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3105547
Keywords
Crowdsensing; Task analysis; Games; Sensors; Security; Privacy; Blockchains; Autonomous vehicular networks (AVNs); blockchain; coalition game; vehicular crowdsensing
Categories
Funding
- National Natural Science Foundation of China [61901341, 62071356, U1808207]
- China Postdoctoral Science Foundation [2021TQ0260]
- National Natural Science Foundation of Shaanxi Province [2020JQ-301, 095920201322]
- Fundamental Research Funds for the Central Universities of Ministry of Education of China [XJS200109, JB210113]
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This article introduces a blockchain-based collaborative crowdsensing scheme to address the challenges of ensuring AV privacy, enabling secure rewards for ECDs, and minimizing task execution costs. By designing a secure crowdsensing environment, proposing a coalition game with transferable rewards, and optimizing coalition formation algorithms, the new scheme leads to lower task execution costs and higher rewards compared to conventional schemes.
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.
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