Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

By Jiawen Kang, Zehui Xiong, Dusist Niyato, Dongdong Ye, Dong In Kim, Jun Zhao. 2018

523 citations recorded by the source corpus.

In the Internet of Vehicles (IoV), data sharing among vehicles is critical for improving driving safety and enhancing vehicular services. To ensure security and traceability of data sharing, existing studies utilize efficient delegated proof-of-stake consensus scheme as hard security solutions to establish blockchain-enabled IoV (BIoV). However, as the miners are selected from miner candidates by stake-based voting, defending against voting collusion between the candidates and compromised high-stake vehicles becomes challenging. To address the challenge, in this paper, we propose a two-stage soft security enhancement solution: 1) miner selection and 2) block verification. In the first stage, we design a reputation-based voting scheme to ensure secure miner selection. This scheme evaluates candidates’ reputation using both past interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among active miners, a newly generated block is further verified and audited by standby miners. To incentivize the participation of the standby miners in block verification, we adopt the contract theory to model the interactions between active miners and standby miners, where block verification security and delay are taken into consideration. Numerical results based on a real-world dataset confirm the security and efficiency of our schemes for data sharing in BIoV.

Subfields: Artificial Intelligence, Automotive Engineering, Information Systems

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Citations in the corpus

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach (2019, methodology): “, contributed CPU-cycle f∗ n and the corresponding incentive R∗ n [12].”
Blockchain for the Internet of Vehicles Towards Intelligent Transportation Systems: A Survey (2020, background): “mechanism particularly for blockchain-based IoV application is presented in [122].”
Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory (2019, methodology): “where a ∈ [0, 1] is the coefficient representing the degree of uncertainty effect on reputation [9].”
6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities (2020, background): “A blockchain-based security solution (e.g., smart contract or consensus mechanism) in 6G-V2X is expected to not only allow veriﬁcation of the authenticity of a message, but also preserve the privacy of the sender [52], [53].”
Reliable Federated Learning for Mobile Networks (2019, methodology): “In this article, to avoid the potential risks of central reputation calculation and management, we employ a decentralized reputation calculation method named subjective logic model [7], and a consortium blockchain with the properties of immutability and decentralization to realize secure reputation ”
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach (2020, background): “in dozens of or even hundreds of organizations [43], [44].”
Vehicular Edge Computing and Networking: A Survey (2019, background): “The work in [141] develops a secure P2P data sharing strategy, where the improved delegated proof-of-stake consensus scheme is employed as a hard security solution.”
Proof-of-Stake Consensus Mechanisms for Future Blockchain Networks: Fundamentals, Applications and Opportunities (2019, background): “APPLICATIONS OF POS CONSENSUS MECHANISMS TO INTERNET-OF-VEHICLES NETWORKS The rapid development of the Internet-of-Things and networking technologies has driven the automotive industry towards smart vehicles with sensing and communication abilities, which in turns necessitates a platform for data co”