Road Sensing through a Connected Vehicle Fog Approach for Smart Cities

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Vehicular road sensing for smart cities involves a large number of Internet of Things (IoT) devices that can generate a massive volume of data. Vehicles can be the "eyes and ears" as a very efficient urban sensing tool, collecting and distributing data about road condition, traffic status, weather, air quality, vehicle status, just to mention a few. The amount of data collected by the various sensors in a smart vehicle has been estimated to be around 25 GB/h. In this kind of environment, data needs to be mined, filtered, stored, processed, and then disseminated to target players in a timely fashion. However, one of the major difficulties in large scale and dynamic systems such as vehicular networks lies in accessing information objectively and efficiently. Current solutions for storing and processing IoT data in smart cities involve centralized cloud approaches. However, centralized methods lack real-time computation properties and fail to provide quick feedback responses, among other limitations when applied to vehicular networks. Another challenge lies in how to deliver road sensing data to intelligent transportation system (ITS) application's end users in a more intelligent and selective way than existing mechanisms. In order to overcome the shortcomings of existing schemes, a vehicular fog computing resource orchestration scheme is highly needed while considering real-time processing constraints, vehicles mobility, and resource heterogeneity. Therefore, this research will investigate, design, implement and evaluate algorithms and tools to facilitate the collection, processing, and distribution of road sensing data through a connected vehicle fog platform for smart cities. More specifically, this research program will: (a) investigate the existing vehicular fog computing resource management schemes; (b) Design and develop a novel vehicular fog computing resource allocation scheme; (c) Study how contextual data can be integrated into the proposed road sensing platform; (d) Design and develop a smart and selective data delivery system; (e) Evaluate the proposed technologies using the most relevant benchmarks through simulations; (f) Design and implement an ITS application, e.g., an innovative slippery road warning system, to showcase the proposed road sensing platform. It is highly expected that the outcomes of this research will foster innovative advances of strong interest to ICT companies, government agencies, municipalities, and other stakeholders. Thus, the proposed road sensing platform is timely and can definitely provide an efficient tool to harvest and distribute urban data and aid in traffic and resource management in a smart city environment. This work will enable service providers to develop novel applications that will tap into the proposed data distribution platform and provide Canadians with smart and tailored services to improve their commutes and safety, reduce carbon emissions, and pave the way for innovative solutions.