Summary The digitization of objects has given birth to the concept of the Internet of Things (IoT) which is revolutionizing traditional objects by replacing them with intelligent objects such as smart vehicles, smartphones, smart home…etc. In this context, and with the emergence of vehicle networks, is born the need to increase the vehicular resources in order to benefit from several applications facilitating driving and ensuring the drivers safety, even going to think of the automated driving. The use of cloud computing has become the key solution to the lack of resources required to run compute‐intensive applications and the lack of storage space to back up all data related to roads and applications. In addition, we are witnessing the birth of the fog computing paradigm, which brings the functionalities of cloud computing at the edge of the network, thereby solving the latency problem for some time‐sensitive applications and also saving the bandwidth of the network because vehicle requests will not need to cross the entire network to be processed at the cloud level. In this paper, we discuss the different principles of cloud/fog computing and compare the two paradigms. We present a classification of data dissemination schemes in vehicular ad hoc networks (VANETs), vehicular cloud, and vehicular fog computing with their different architectures proposed. We finally present several cloud/fog computing applications.

Summary Nowadays, vehicles have become more and more intelligent and equipped with highly sophisticated systems. This allows them to communicate with each other and with the roadside units (RSUs). Furthermore, to ensure efficient data dissemination in vehicular ad hoc networks (VANETs), it is recommended that a Vehicle‐to‐Infrastructure (V2I) architecture be chosen where RSUs will be installed at intersections. Nevertheless, it is not advisable to place an RSU at each intersection because of their high cost. It is therefore appropriate to reduce the number of RSUs by choosing locations at intersections that maximize the surface covered of the urban area and minimize the area of overlapping zones. Moreover, deploying an optimal number of RSUs in an urban area meeting the above requirements is an NP‐hard problem since the number of combinations is very high when the number of intersections is very large. For this purpose, we used metaheuristic approaches. The first approach is represented by the standard version of genetic algorithms (GA‐Basic) and its improved version (GA‐Improved) while the second approach is based on the standard version of simulated annealing (SA‐Basic) and its improved version (SA‐Improved). The proposed approaches are evaluated over OMNET++ simulator. The results obtained showed that the GA‐Improved approach deploys a reduced number of RSUs (37.5%) while guaranteeing acceptable routing performance compared to the GA‐Basic, SA‐Basic, and SA‐Improved approaches which deploy 46.25%, 65%, and 52.50%, respectively, for the same routing performance.

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Road congestion and traffic jams are serious problems for drivers, causing them more delay in reaching their destinations. Motivated by these considerations, this article proposes an intelligent scheduling algorithm based on vehicular ad hoc networks (VANETs) and Cloud computing to schedule traffic lights to smooth road traffic and reduce waiting time on the road, called CCITL (cloud computing based intelligent traffic light protocol). In CCITL, we involve cloud computing by using conventional and vehicular clouds to have a global view of the road network and take advantage of the cloud computing capabilities to calculate the most appropriate traffic signal formulas and combinations with their phase durations in a dynamic way. This allows to ensure the fluidity of the traffic and the arrival at the destination in a short time while minimizing the waiting time on the different segments of the road network. Our proposed protocol has been developed and evaluated over OMNET++ simulator with the simulator of urban mobility (SUMO). The simulation results showed that CCITL greatly contributed to the alleviation of traffic congestion as well as to the reduction of the dwell time of vehicles in the different segments, thus reducing delays and waiting in intersections.