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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Section: New Results

Protocols and Models for Wireless Networks - Application to VANETs

Predicting Vehicles Positions using Roadside Units: a Machine-Learning Approach

Participants : Samia Bouzefrane ( Cnam), Soumya Banerjee ( Birla Institute Of Technology, Mesra), Paul Mühlethaler, Mamoudou Sangare.

We study positioning systems using Vehicular Ad Hoc Networks (VANETs) to predict the position of vehicles  [35]. We use the reception power of the packets received by the Road Side Units (RSUs) and sent by the vehicles on the roads. In fact, the reception power is strongly influenced by the distance between a vehicle and a RSU. To predict the position of vehicles in this context, we adopt the machine learning methodology. As a pre-requisite, the vehicles know their positions and the vehicles send their positions in the packets. The positioning system can thus perform a training sequence and build a model. The system is then able to handle a prediction request. In this request, a vehicle without external positioning will request its position from the neighboring RSUs. The RSUs which receive this request message from the vehicle will know the power at which the message was received and will study the positioning request using the training set. In this study, we use and compare three widely recognized techniques : K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Random Forest. We study these techniques in various configurations and discuss their respective advantages and drawbacks. Our results show that these three techniques provide very good results in terms of position predictions when the error on the transmission power is small.

Predicting transmission success with Machine-Learning and Support Vector Machine in VANETs

Participants : Samia Bouzefrane ( Cnam), Soumya Banerjee ( Birla Institute Of Technology, Mesra), Paul Mühlethaler, Mamoudou Sangare.

We study the use of the Support Vector Machine technique to estimate the probability of the reception of a given transmission in a Vehicular Ad hoc NETwork (VANET). The transmission takes place between a vehicle and a RoadSide Unit (RSU) at a given distance and with a given transmission rate. The RSU computes the statistics of the receptions and is able to compute the percentage of successful transmissions versus the distance between the vehicle and the RSU and the transmission rate. Starting from this statistic, a Support Vector Machine (SVM) scheme can produce a model. Then, given a transmission rate and a distance between the vehicle and the RSU, the SVM technique can estimate the probability of a succcessful reception. This probability can be used to build an adaptive technique which optimizes the expected throughput between the vehicle and the RSU. Instead of using transmission values of a real experiment, we use the results of an analytical model of CSMA that is customized for 1D VANETs. The model we adopt to perform this task uses a Matern selection process to mimic the transmission in a CSMA IEEE 802.11p VANET. With this model we obtain a closed formula for the probability of successful transmissions. Thus with these results we can train an SVM model and predict other values for other couples : distance, transmission rate. The numerical results we obtain show that SVM seems very suitable to predict the reception probability in a VANET.

TDMA scheduling strategies for vehicular ad hoc networks: from a distributed to a centralized approach

Participants : Mohammed Hadded, Anis Laouiti ( Telecom Sud-Paris, Paul Mühlethaler.

We focus on vehicular safety applications based on the Dedicated Short Range Communication (DSRC) standard. We propose a new mechanism to alleviate channel congestion by reducing the beacons load while maintaining an accurate awareness level. Our scheme is based on the collective perception concept which consists in sharing perceived status information collected by vehicles equipped with different types of sensors (radars, lidars, cameras, etc.). To achieve our goal, we propose two main schemes [30]. The first one consists in implementing the collective perception capability on vehicles and adding a new category of status messages to share locally collected sensor data in order to reduce channels load and enhance vehicles' awareness. The second scheme concerns the accuracy level of the received information from the collective perception enabled vehicles by fixing a prior error threshold on the position. The method proposed is validated by simulations and the results obtained are compared to those of an application based on the traditional beaconing scheme of the IEEE802.11p standard. The simulations show that the proposed scheme is able to significantly reduce the load on the control channel incurred by the beacons and the packet error ratio for different network densities and built-in sensors characteristics.

A Collaborative Environment Perception Approach for Vehicular Ad hoc Networks

Participants : Sadia Ingrachen, Nadjib Achir ( Paris 13), Paul Mühlethaler, Tounsia Djamah ( Paris 13), Amine Berqia ( Paris 13).

We focus on vehicular safety applications based on the Dedicated Short Range Communication (DSRC) standard. We propose a new mechanism to alleviate channel congestion by reducing the beacons load while maintaining an accurate awareness level. Our scheme is based on the collective perception concept which consists in sharing perceived status information collected by vehicles equipped with different types of sensors (radars, lidars, cameras, etc.). To achieve our goal, we propose two main schemes [31]. The first one consists in implementing the collective perception capability on vehicles and adding a new category of status messages to share locally collected sensor data in order to reduce channels load and enhance vehicles' awareness. The second scheme concerns the accuracy level of the received information from the collective perception enabled vehicles by fixing a prior error threshold on the position. The method proposed is validated by simulations and the results obtained are compared to those of an application based on the traditional beaconing scheme of the IEEE802.11p standard. The simulations show that the proposed scheme is able to significantly reduce the load on the control channel incurred by the beacons and the packet error ratio for different network densities and built-in sensors characteristics.