An efficient truthfulness privacy-preserving tendering framework for vehicular fog computing. (May 2020)

• Record Type: Journal Article
• Authors: Alamer, Abdulrahman; Basudan, Sultan
• Is Part Of: Engineering applications of artificial intelligence. Volume 91(2020)
• Publication Date: 2020-05
• Subjects: Vehicular fog computing -- Tendering -- Incentive mechanism -- Privacy preservation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103583
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents a proposal for a tendering-based incentive framework in order to encourage vehicle owners to join in announced tasks in the vehicular fog computing. The truthfulness of users is ensured by using the incentive mechanism that also assists a fog node server to choose suitable resources for the task. An illustrative language, which is a novel approach to guaranteeing fairness amongst vehicles, is designed based on heterogeneous vehicular resource types. The signcryption technique and a homomorphic concept are integrated in the proposed framework in order to preserve vehicles privacy. Moreover, a detailed performance analysis demonstrates that the communication and computational overheads of this privacy-preserving scheme are significantly more efficient than the available alternatives.