Abstract
With increasing vehicle density and the growth of accidents in urban areas, navigation management becomes a serious problem. Even though there is a multitude of navigation systems, ambulances, taxis, or even ordinary vehicles, sometimes find it challenging to reach their destinations on time. There are two main reasons for this difficulty: (i) lack of local knowledge of the area of navigation solutions and (ii) their inflexibility against unforeseeable situations that may occur on the roads. Indeed, the majority of navigation solutions are based only on the distance, the journey time, or even statistics related to the density of vehicles to plan the full paths, while neglecting the dynamic nature of the vehicle traffic. Also, their respective centralized architectures are unable to monitor both the traffic and unexpected events continuously and in real-time without not being overloaded by the flow of message exchanges between the road entities and the central processing entity. To address these issues, we propose in this paper a novel three-tier architecture, called SDN-enabled Approach for Vehicle Path-Planning (SEARCH), to enhance the situation awareness on urban roads, efficiently collect traffic information in real-time, and decide the best navigation strategy. The proposed architecture exploits Unmanned Aerial Vehicles (UAVs), Vehicular Ad hoc Networks (VANETs), 5G based cellular systems, and Software-Defined Networking (SDN) to provide better and faster communication to changing road conditions. Based on these technologies, some parameters related to vehicles and driving environments, such as speed, distance, traffic jams, incidents, and travel flow, are efficiently collected and dynamically exploited to achieve faster paths between any existing pairs of locations. Furthermore, the deployed architecture of SEARCH can provide sufficient bandwidth to support all data traffic needs to update vehicles during their journey efficiently and in real-time. To evaluate the performance of our architecture, we conduct a series of simulations and perform a set of comparisons with relevant route planning algorithms. We found that the proposed architecture works effectively in terms of saving on driving time to reach any target destinations.