Social Network-based Data Dissemination in Vehicular Sensor Networks to Support Distributed Monitoring Services

车辆和传感器网络的融合为数据传播到分布式服务提供了可行的手段，例如交通监测、安全警告、信息娱乐和对道路和街道的主动监控。然而，由于车辆的高机动性、网络拓扑的频繁变化、数据量大以及不同的数据分发和应用需求，车辆传感器网络中的数据分发是一项具有挑战性的任务。例如，大多数安全警告应用程序都需要快速可靠地将数据传送到附近的车辆，这可以通过当地广播实现。监测服务可能需要向区域或指挥中心连续提供数据，而在这些区域或指挥中心使用广播是不可行或不有效的。因此，另一个挑战在于根据不同的工具或用户的兴趣动态调整以适应最佳的数据传播方法。现有的解决方案单独解决其中的一些问题，不适应不同的服务或用户要求。服务、用户和数据需求的异质性似乎是在选择转发工具时应用基于社交网络的决策的机会。通过利用司机之间的社会联系，可能会尽可能地绘制出接近真实交通行为的出行模式。这种映射在数据转发决策中非常有用。因此，本研究计划将在一个统一的框架下研究这些问题，然后开发新的混合自适应技术，利用现有数据传播策略的优势和弱点。该计划将专注于设计基于社交网络的方案，利用车辆之间的“互动”(即，经常访问的地点、相遇、移动模式)以及司机的社交关系来改善VSN中的数据传播。预计拟议的项目不仅将促进VSN和智能交通系统(ITS)的进步，还将为学生在这一要求苛刻的研究领域做好准备，并使他们在学术界和行业处于更具竞争力的地位。

The convergence of vehicle and sensor networks offers viable means for data dissemination to distributed services such as traffic monitoring, safety warnings, infotainment, and proactive surveillance of roads and streets. However, data dissemination in vehicular sensor networks (VSNs) is a challenging task due to high mobility of vehicles; frequent network topology changes; high volume of data; and different data dissemination and application requirements. For instance, most safety warning applications will require fast and reliable data delivery to nearby vehicles, which can be achieved by local broadcasts. A monitoring service may require continuous data delivery to a region or command centre, in which the use of broadcasts would not be feasible or efficient. Thus, another challenge lies in adapting on-the-fly to the best data dissemination approach depending on the different vehicle or user's interests. Existing solutions address some of these issues individually and do not adapt to different services or user requirements. The heterogeneity of services, users and data requirements seems to be an opportunity to apply social network-based decision making when selecting forwarding vehicles. It might be possible to map mobility patterns as close as possible to real traffic behavior by exploiting social ties among drivers. Such mapping would be extremely useful in data forwarding decisions. Therefore, this research program will investigate these issues under a unified framework and then develop novel hybrid adaptive techniques that take advantage of the strengths and deal with the weaknesses of existing data dissemination strategies. This program will focus in devising social network-based schemes that exploit "interactions" among vehicles (i.e., common visited places, encounters, mobility patterns) as well as drivers' social ties to improve data dissemination in VSNs. It is expected that the proposed program will not only foster advances in VSNs and intelligent transportation systems (ITS), but also prepare students in this demanding research field and place them in a more competitive position in the academia and industry.