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Article Dans Une Revue Computer Networks Année : 2022

A method for predicting ITS cooperative applications performances

Résumé

Vehicular cooperation taking place in Intelligent Transportation Systems (ITS) aims to solve traffic issues or to improve road safety for vehicles and vulnerable road users. Such cooperation is achieved through distributed applications running in the vehicular network (VANET), composed of communicating units in vehicles and infrastructure. For safety and legal reasons, it is expected that ITS services reach a satisfactory level of efficiency in the context they are deployed. However, predicting the behavior of distributed applications running in dynamic topologies remains a complex problem, which limits their deployment. In this paper, we propose a method able to predict the performances of cooperative ITS (C-ITS) applications in dynamic vehicular networks. It concerns applications relying on messages exchanges between close vehicles or between vehicles and infrastructure. The proposed method is based on an analysis of the application with respect to the vehicular traffic for determining the conditions ensuring the success of its underlying distributed algorithms and protocols. This is formalized as performances properties depending on the network dynamic. It becomes then easy to check whether these conditions are fulfilled or not in a given road traffic, either captured (and anonymized) for an existing road or expected for a new one. Thanks to an appropriate modeling, the conditions are sought and the performances of the C-ITS applications are predicted. We believe that our approach fills a gap between formal studies, VANET simulations and road tests. It will be of particular interest for the deployment of C-ITS applications. This is illustrated by several use-cases studied in realistic conditions.
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Dates et versions

hal-03988061 , version 1 (14-02-2023)

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Guillaume Béduneau, Ghada Jaber, Bertrand Ducourthial. A method for predicting ITS cooperative applications performances. Computer Networks, 2022, 216, pp.109148. ⟨10.1016/j.comnet.2022.109148⟩. ⟨hal-03988061⟩
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