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Consortium Blockchain for Secure Resource Sharing in Vehicular Edge Computing: A Contract-Based Approach

Siming Wang, Dongdong Ye, Xumin Huang, Rong Yu, Yongjian Wang, Yan Zhang

2020IEEE Transactions on Network Science and Engineering99 citationsDOI

Abstract

Vehicular edge computing (VEC) extends edge computing to vehicular networks by exploiting computation resources of vehicles to offload tasks from other vehicles and pedestrians. However, VEC faces several critical challenges such as the potential security issues caused by untrusted and opaque environment and the lack of incentive mechanism under asymmetric information scenario. To solve the above challenges, we propose a consortium blockchain for secure resource sharing in VEC. We first design multi-step smart contracts to achieve secure resource sharing and defend against the malicious behaviours of service requesters and vehicles with selfish purposes. Then, a byzantine fault tolerance-based proof-of-stake (BFT-based PoS) consensus protocol is applied in consortium blockchain to reach consensus efficiently. Furthermore, we design a contract-based incentive mechanism to motivate vehicles to share their computation resources with service requesters. The optimal contracts are derived to maximize the service requesters' expected utility as well as social welfare. Finally, simulation results demonstrate that the proposed incentive mechanism is more effective and efficient than the traditional schemes.

Topics & Concepts

Computer scienceIncentiveSmart contractEdge computingComputer securityProtocol (science)BlockchainProof-of-work systemDistributed computingEnhanced Data Rates for GSM EvolutionShared resourceService (business)Mechanism designComputer networkByzantine fault toleranceResource allocationResource (disambiguation)Mobile edge computingFault toleranceServerBusinessInternet of ThingsMicroeconomicsMedicineMarketingAlternative medicineTelecommunicationsEconomicsPathologyBlockchain Technology Applications and SecurityIoT and Edge/Fog ComputingPrivacy-Preserving Technologies in Data
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