Vehicular Networks enable a vast number of innovative applications, which rely on the efficient exchange of information between vehicles and often also with infrastructure. However, efficient and reliable data dissemination is a particularly challenging task in the context of vehicular networks due to the underlying properties of these networks, limited availability of network infrastructure and variable penetration rates for distinct communication technologies. This paper presents a novel system and mechanism for information collection and dissemination based on virtual infrastructure selection in combination with multiple communication technologies. The system has been evaluated using a simulation framework, involving network simulation in conjugation with realistic vehicular mobility traces. Simulation results show the feasibility of the proposed mechanism to achieve maximum message penetration in a geographical area with reduced overhead. The judicious vehicle selection also enables scalable data collection and leads to improved network utilization through the offload of traffic to the short-range network.
V-Alert is a cooperative application to be deployed in the frame of Smart Cities with the aim of reducing the probability of accidents involving Vulnerable Road Users (VRU) and vehicles. The architecture of V-Alert combines short- and long-range communication technologies in order to provide more time to the drivers and VRU to take the appropriate maneuver and avoid a possible collision. The information generated by mobile sensors (vehicles and cyclists) is sent over this heterogeneous communication architecture and processed in a central server, the Drivers Cloud, which is in charge of generating the messages that are shown on the drivers’ and cyclists’ Human Machine Interface (HMI). First of all, V-Alert has been tested in a simulated scenario to check the communications architecture in a complex scenario and, once it was validated, all the elements of V-Alert have been moved to a real scenario to check the application reliability. All the results are shown along the length of this paper.
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.
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