SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities

It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed decisions about their travel plans, thereby preventing congestion altogether or mitigating its nefarious effects

Vehicular Crowdsourcing for Congestion Support in Smart Cities

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems

Study on vehicular network application and simulation

VANET is an emerging mobile ad hoc network paradigm that facilitates vehicle-to-vehicle and vehicle-to-infrastructure communication. The most important application of the VANET is for driving safety. Road condition-awareness is critical for driving safety. Existing VANET-based systems usually assume drivers detect and report safety related road conditions, which however may be untrue because, drivers may not be willing to perform these duties, or such duties may distract drivers and thus make driving even unsafe. Therefore, automatic detection without human intervention is desired. As the first contribution of this thesis work, an automatic road condition detection system has been designed based on the idea of collecting and analysing the footprints of vehicles to infer anomaly. It has also been studied how to utilize inexpensive roadside devices, such as sensors, to facilitate the information collection and analysis, especially in the absence of connectivity between vehicles. Due to the difficulty of conducting large-scale experiments on real roads, simulation plays an important role in VANET research. To make simulation close to the reality, it is desired to include detailed and realistic simulation of vehicle behaviour under various road conditions, and this is especially needed for studies targeted at driving safety. In the past, however, the simulation of vehicle behaviours are often overly simplified and implemented as a trivial extension of the network simulator. As a second contribution of this thesis work, a detailed and realistic simulator of vehicle behaviour has been developed based on the car-following and lane-changing models. As the simulation of vehicle behaviour and that of communication behaviour are different tasks, they should be implemented separately for better modularity and meanwhile they should be seamlessly integrable. As another contribution of this thesis work, the online and seamless integration of vehicle behaviour simulator and network simulator has been studied. Specifically, a set of APIs has been designed and implemented atop the vehicular behaviour simulator to facilitate its integration with network simulator. Being a concrete example, the integration of ns2 and SUMO, an open-source vehicular behaviour simulator, has been implemented, and applied to simulate an electric vehicular network