To cope with an exponentially increasing demand on mobile data traffic in cellular network, proximity-based opportunistic vehicular communications can be exploited as a complementary mean to offload and reduce the load of cellular network. In this paper, we propose a two-phase approach for mobile data traffic offloading, which exploits opportunistic contact and future utility with user mobility. The proposed approach includes one phase of initial source selection and subsequent phase of data forwarding. In phase 1, we build a weighted reachability graph, which is a very useful high-level abstraction for studying vehicular communication over time. Then, we propose an initial source selection algorithm, named VRank, and apply it in the weight reachability graph to identify some influential vehicles to serve as initial sources according to the rank of VRank. In phase 2, we formulate the forwarding schedule problem as a global utility maximization problem, which takes heterogeneous user interest and future utility contribution into consideration. Then, we propose an efficient scheme MGUP to solve the problem by providing a solution that decides which object should be broadcast. The effectiveness of our algorithm is verified through extensive simulation using real vehicular trace.

中文翻译：

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通过机会车载通信卸载移动数据流量

为了应对蜂窝网络中对移动数据业务的指数增长的需求，可以利用基于邻近性的机会车辆通信作为减轻和减轻蜂窝网络负载的补充手段。在本文中，我们提出了一种用于移动数据流量卸载的两阶段方法，该方法利用机会联系和用户移动性的未来效用。所提出的方法包括初始源选择的一个阶段和数据转发的后续阶段。在第1阶段中，我们建立了加权可达性图，它是研究一段时间内车辆通信的非常有用的高级抽象。然后，我们提出了一个初始资源选择算法，名为VRank，并将其应用到重量可达性图中，以根据VRank的排名确定一些有影响力的车辆作为初始来源。在阶段2中，我们将转发调度问题表述为全局效用最大化问题，该问题考虑了异构用户的兴趣和未来效用的贡献。然后，我们提出了一种有效的方案MGUP，通过提供一种决定应广播哪个对象的解决方案来解决该问题。我们的算法的有效性通过使用真实车辆轨迹的大量仿真得到了验证。我们提出了一种有效的方案MGUP，通过提供一种决定应广播哪个对象的解决方案来解决该问题。我们的算法的有效性通过使用真实车辆轨迹的大量仿真得到了验证。我们提出了一种有效的方案MGUP，通过提供一种决定应广播哪个对象的解决方案来解决该问题。我们的算法的有效性通过使用真实车辆轨迹的大量仿真得到了验证。