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

车辆传感器网络中基于社交网络的数据传播支持分布式监控服务

• 批准号: 436077-2013
• 负责人: Pazzi, Richard
• 金额: $ 1.46万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=574268
• 关键词: Social; Network; based; Data; Dissemination

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.

车辆和传感器网络的融合为分布式服务提供了可行的数据传播手段，如交通监控、安全警告、信息娱乐以及道路和街道的主动监控。然而，由于车辆的高机动性、网络拓扑结构的频繁变化、数据量大以及不同的数据分发和应用需求，车载传感器网络（VSNs）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需要向附近的车辆快速可靠地传输数据，这可以通过本地广播来实现。监测服务可能需要向一个地区或指挥中心连续提供数据，在这些地区或指挥中心，使用广播既不可行也不有效。因此，另一个挑战在于根据不同的车辆或用户的兴趣动态地适应最佳数据传播方法。 现有的解决方案单独解决了其中的一些问题，并且不适应不同的服务或用户需求。服务，用户和数据需求的异质性似乎是一个机会，应用基于社交网络的决策时，选择转发车辆。通过利用驾驶员之间的社会联系，尽可能接近真实的交通行为来绘制移动模式是可能的。这种映射在数据转发决策中将非常有用。因此，本研究计划将在一个统一的框架下调查这些问题，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。该计划将侧重于设计基于社交网络的方案，利用车辆之间的“互动”（即，常见的访问地点，遭遇，移动模式）以及驾驶员的社会关系，以改善VSN中的数据传播。预计该计划不仅将促进VSNs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工业界处于更具竞争力的地位。