Leveraging vehicular cloud computing through location and request prediction

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In recent years, the implementation of Vehicular Ad-hoc networks (VANET) has been acknowledged as a promising solution for monitoring road conditions. Despite the many benefits they can bring to society, the growing demand for communication, storage, and processing capabilities is giving rise to new challenges. For instance, the heightened need for communication in VANETs can cause network congestion. Additionally, the real-time nature of VANET applications, such as traffic management, accident prevention, and navigation, necessitates rapid and reliable communication, which can be difficult to achieve in a network that is constantly changing. Moreover, managing and scaling the resources of a large scale network like VANET with a large number of vehicles and road-side units (RSUs) is a significant challenge. There are essential contributions to deal with these problems, such as utilizing MEC (Mobile Edge Computing), and a hybrid architecture of cloud and fog computing which can create an efficient and adaptable resource management system. However, having unpredictable events such as accidents or road closures can cause rapid changes in the network topology, making it difficult to allocate resources effectively. Also, unpredictable events can lead to a lack of information, making it difficult to obtain accurate and up-to-date information about the network and hard to allocate resources effectively. Furthermore, allocating resources at the right time when unpredictable events happen without network congestion is another challenging problem that causes us to think about proposing a model that can satisfy delay sensitive applications requirements and decrease the monetary cost in hybrid cloud and fog architecture in the presence of unpredictable congestion. To reach our goal, we first need to estimate the traffic flow after an accident, predict the level of congestion based on the number of requests from different vehicles, and then predict the location of potential vehicles as mobile fog nodes in advance to form Vehicular Clouds. Finally, we propose a layer-based architecture with two prediction models and different modules to support safety and non-safety applications, and a task scheduling mechanism to decrease monetary costs and delay for delay-sensitive applications during congestion times to serve the vehicles requests in that region.
Vehicle location prediction, Vehicular clouds, Time series, VANET, LSTM