Fog Computing-based Traffic Management Support forIntelligent Transportation Systems
Traffic in large urban centers contributes to problems ranging from decreasing the population's quality of life and security to increasing financial costs for people, cities, and companies. Considering the advance of communication, processing, and sensing technologies, Intelligent Transport Systems (ITS) have emerged as an alternative to mitigate these problems. The interoperability of ITS with new technologies, such as vehicular networks (VANETs) and Fog computing, make them more promising and effective. VANETs ensure that vehicles have the computing power and wireless communication capabilities providing a new range of security and entertainment services for drivers and passengers can be developed. However, these types of services, especially traffic management, demand a continuous analysis of vehicle ﬂow conditions on roads. Thereby, a huge network and processing resources are required making the development of ITS solutions more complex and difficult to scale. Fog computing is a decentralized computing infrastructure in which data, processing, storage, and applications are distributed at the network edge, thereby increasing the system's scalability. In the literature, traffic management systems do not adequately address the scalability problem, resulting in load balancing and response time problems. This doctoral thesis proposes a traffic management system based on the Fog computing paradigm to detect, classify, and control traffic congestion. The proposed system presents a distributed and scalable framework that reduces the aforementioned problems in relation to state of the art. Therefore, using Fog computing's distributed nature, the solution implements a probabilistic routing algorithm that balances traffic and avoids the problem of congestion displacement to other regions. Using the characteristics of Fog computing, a distributed methodology was developed based on regions that collect data and classify the roads concerning the traffic conditions shared by the vehicles. Finally, a set of communication algorithms/protocols was developed which, compared with other literature solutions, reduces packet loss and the number of messages transmitted. The proposed service was compared extensively with other solutions in the literature regarding traffic metrics, where the proposed system was able to reduce downtime by up to 70% and up to 49% of the planning time index. Considering communication metrics, the proposed service can reduce packet collision by up to 12% reaching 98% coverage of the scenario.
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