Section: New Results

Connected Cars

Participants : Nathalie Mitton, Valeria Loscri, Joao Batista Pinto Neto.


Connected car technology promises to drastically reduce the number of accidents involving vehicles. Nevertheless, this technology requires the vehicle precise location to work. The adoption of Global Positioning System (GPS) as a navigation device imposes limitations to geolocation information under non-line-of-sight conditions. [22] introduces the Time Series Dead Reckoning System (TedriS) as a solution for dead reckoning navigation when the GPS fails. TedriS uses Time Series Regression Models (TSRM) and the data from the rear wheel speed sensor of the vehicle to estimate the absolute position. The process to estimate the position is carried out in two phases: training and predicting. In the training phase, a novel technique applies TSRM and stores the relationship between the GPS and the rear wheel speed data; then in the predicting phase, this relationship is used. We analyze TedriS using traces collected at the campus of Federal University of Rio de Janeiro (UFRJ), Brazil, and with indoor experiments with a robot. Results show an accuracy compatible with dead-reckoning navigation state-of-art systems.

Data forwarding

Intelligent inter-vehicle communication is a key research field in the context of vehicular networks that applies in real-life applications (e.g., management of accidents, intelligent fuel consumption, smart traffic jams, etc.). Considering different roles of nodes based on their “social aptitude” to relay information could provide a social component in the vehicular structure that can be useful in getting a clear prediction of the topological evolution in time and space proving to be very effective in managing intelligent data forwarding. In [36], we characterize a vehicular network as a graph using the link layer connectivity level and we classify nodes on the basis of specific attributes characterizing their “social aptitude” to forward data. Two forwarding approaches are presented, based on different socialites that allow to (i) select the most social node (i.e., a social hub) or (ii) choose among various social nodes.

Internet of vehicles

Internet, in its most recent evolution, is going to be the playground where a multitude of heterogeneous interconnected “things” autonomously exchange information to accomplish some tasks or to provide a service. Recently, the idea of giving to those smart devices the capability to organize themselves according to a social structure, gave birth to the so-called paradigm of the Social Internet of Things. The expected benefits of SIoT range from the enhanced effectiveness, scalability and speed of the navigability of the network of interconnected objects, to the provision of a level of trustworthiness that can be established by averaging the social relationships among things that are “friends”. Bearing in mind the beneficial effects of social components in IoT, we consider a social structure in a vehicular context i.e., Social Internet of Vehicles (SIoV). In SIoV, smart vehicles build social relationships with other social objects they might come into contact, with the intent of creating an overlay social network to be exploited for information search and dissemination for vehicular applications. In [43], we aim to investigate the social behavior of vehicles in SIoV and how it is affected by mobility patterns. Specifically, through the analysis of simulated traffic traces, we distinguish friendly and acquaintance vehicles based on the encounter time and connection maintenance.