Dependable Content Distribution in D2D-Based Cooperative Vehicular Networks: A Big Data-Integrated Coalition Game Approach
Dependable Content Distribution in D2D-Based Cooperative Vehicular Networks: A Big Data-Integrated Coalition Game Approach
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DOI：
10.1109/tits.2017.2771519
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发表时间：
2018-03-01
影响因子：
8.5
通讯作者：
Rodriguez, Jonathan
中科院分区：
文献类型：
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作者：
Zhou, Zhenyu;Yu, Houjian;Rodriguez, Jonathan
在汽车工业和蜂窝技术演进发展的推动下，可靠的车辆连接已经成为实现未来智能交通系统(ITS)的关键。研究了如何将基于大数据的车辆轨迹预测和基于联盟形成博弈的资源分配相结合，在基于设备到设备(D2D)的协同车载网络中实现可靠的内容分发。首先，基于全球定位系统和地理信息系统数据预测车辆轨迹，这对于找到可靠和持久的车辆连接至关重要。然后，将具有不同寿命的内容分发组的确定表示为联盟形成博弈。我们以平均网络时延最小为基础建立效用函数，并根据每个联盟成员的贡献将其转化为个体收益。基于偏好关系迭代地实现了合并和拆分过程，并证明了最终的划分收敛于纳什稳定均衡。最后，我们基于真实世界地图和真实车辆交通对提出的算法进行了评估。数值结果表明，与其他启发式算法相比，该算法在平均网络时延和内容分发效率方面具有更好的性能。
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.