An Incentive Mechanism for Vehicular Crowdsensing with Security Protection and Data Quality Assurance

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 8; pp. 1 - 15
• Main Authors: Cai, Xuelian, Zhou, Lingling, Li, Fan, Fu, Yuchuan, Zhao, Pincan, Li, Changle, Yu, F. Richard
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: blockchain; Blockchains; Contracts; Crowdsensing; Cryptography; Data integrity; data quality; incentive mechanism; Low cost; Particle measurements; Quality assurance; Quality control; Security; Sensors; Smart contracts; Task analysis; Vehicular crowdsensing
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2023.3262800

With the increase of on-board sensors, as a new paradigm of mobile crowdsensing (MCS), vehicular crowdsensing (VCS) shows great potential in realizing low-cost, large-scale sensing tasks. In order to improve the user engagement and task completion quality of VCS, an appropriate incentive mechanism can promote enough users to participate in the sensing activities and further provide high-quality sensing data. However, due to the contradiction between personal interests and user data security protection, the development of the incentive mechanism is seriously affected. To deal with these challenges, this paper aims to propose a security protection incentive mechanism with data quality assurance (SPIM-DQA) for the VCS system. First, we adopt the blockchain-enabled VCS framework, and propose a series of smart contracts to ensure the automatic execution of the incentive mechanism, which solves the user data security issues existing in the traditional incentive mechanism. Then, based on this framework and these smart contracts, a data quality-aware incentive mechanism is proposed from the perspective of data quality. After selecting low-cost and high-quality users to perform the crowdsensing task, user reputation is updated by evaluating the quality of the provided data. In particular, there is a correlation between user reputation and reward distribution, which incentivizes users to consistently provide high-quality data to increase their rewards. Finally, extensive simulation results show that SPIM-DQA can effectively improve data quality while meeting security requirements.