Over the last few years, a lot of applications have been developed for Vehicular Ad Hoc NETworks (VANETs) to exchange information between vehicles. However, VANET is basically a Delay Tolerant Network (DTN) characterized by intermittent connectivity, long delays and message losses especially in low density regions [1]. Thus, VANET requires the use of an infrastructure such as Roadside Units (RSUs) that permits to enhance the network connectivity. Nevertheless, due to their deployment cost, RSUs need to be optimally deployed. Hence, the main objective of this work is to provide an optimized RSUs placement for delay-sensitive applications in vehicular networks that improves the end-to-end application delay and reduces the deployment cost. In this paper, we first mathematically model the placement problem as an optimization problem. Then, we propose our novel solution called ODEL. ODEL is a two-steps technique that places RSUs only in useful locations and allows both vehicle-to-vehicle and vehicle-toinfrastructure communication: (i) the first step is comprehensive study that looks for the RSUs candidates locations based on connectivity information, and (ii) the second step uses genetic algorithm and Dijkstra algorithm to reduce the number of RSUs based on the deliverance time requirement and the deployment cost. We show the effectiveness of our solution for different scenarios in terms of applications delay (reduced by up to 84%) and algorithm efficiency (computation performance reduced by up to 79% and deployment cost reduced at least by up to 23%).

No abstract

Due to environmental issues, electric mobility is one of the mobility alternatives that are receiving a huge attention nowadays. In fact, in the last few years electric vehicles have entered the world's car market. This revolutionary technology requires a fast deployment of electric charging stations since the key issue in this system is recharging the batteries. In this work, we propose an optimized algorithm to locate electric-vehicles charging stations. Different factors and limitations are considered and a real case study is given as an application. We first determine the appropriate strict constraints and cost of charging stations' location; and then we propose a mathematical formulation of the problem before solving it using our optimized algorithm named OLoCs (Optimized Location Scheme for electric charging stations). This latter is a heuristic solution; in which we adapt a genetic algorithm to solve the charging stations' location problem. We add a new operator to the classical genetic algorithm to prevent premature convergence and improve the efficiency of the algorithm. OLoCs determines the necessary number of charging stations and their best opening placement. Finally, we evaluate OLoCs performances by analyzing its convergence time and depicting the graphic placement results on a studied map.