Dependable Content Distribution in D2D-Based Cooperative Vehicular Networks: A Big Data-Integrated Coalition Game Approach

Zhenyu Zhou, Houjian YU, Chen Xu, Yan Zhang, Shahid Mumtaz, Jonathan Rodriguez

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid


Driven by the evolutionary development of automobile industry and cellular technologies, dependable vehicular connectivity has become essential to realize future intelligent transportation systems (ITS). In this paper, we investigate how to achieve dependable content distribution in device-to- device (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with coalition formation game-based resource allocation. First, vehicle trajectory is predicted based on global positioning system and geographic information system data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay, which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm can achieve superior performance in terms of average network delay and content distribution efficiency compared with the other heuristic schemes.
Iaith wreiddiolSaesneg
Rhif yr erthygl99
Tudalennau (o-i)1-12
CyfnodolynIEEE Transactions on Intelligent Transportation Systems
Dyddiad ar-lein cynnar23 Ion 2018
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsE-gyhoeddi cyn argraffu - 23 Ion 2018

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