Posts from category "réalisation"

Context Presentation

Fog computing has emerged as a strong distributed computation paradigm to support applications with stringent latency requirements. It offers almost ubiquitous computation capacities over a large geographical area. However, Fog systems are highly heterogeneous and dynamic which makes IoT services placement decision quite challenging considering nodes mobility that may decrease the placement decision quality over time.

IoT-Fog Services placement problem needs to be thoroughly investigated to ensure the efficiency of such environments. In this thesis, we consider various parameters such as nodes mobility, energy efficiency and applications Quality of Service (QoS) requirements to propose efficient strategies for IoT services placement in the Fog.

Keywords

Internet of Things, Optimization, Mobility, Fog Computing, QoS, Energy.

Scientific goals

•    Propose efficient approaches for IoT applications (services) placement in the Fog,

•    Analyze their impact on the energy consumption of Fog infrastructures and the Quality of Service (QoS) of  applications.

Contacts

tanissia.djemai@irit.fr, patricia.stolf@irit.fr, monteil@laas.fr, jean-marc.pierson@irit.fr

Context Presentation

We spend around 85% of our time indoors. However, indoor air is 5 to 10 times more polluted than outside air. Based on this observation, various laboratories at the Paul Sabatier - Toulouse III University are focusing part of their research on the development of tools for measuring and improving the Indoor Air Quality.

This is the case of LAAS (Systems Architecture Laboratory), LCC (Coordination Chemistry Laboratory) and LMDC (Materials and Construction Durability Laboratory), which work in collaboration on the development and optimization of MOx gaz sensor (semiconductor metal oxides) to detect different gases in the air (LAAS and LCC), and photocatalytic coatings (LMDC and LCC) to degrade or decrease the concentration of gaseous pollutants.

During this internship, the gases mainly treated are nitrogen oxides (NOx). The aim is to assess the effectiveness of the sensors developed by LAAS and the LCC in detecting variations of NOx concentration and the effectiveness of the coatings in degrading them.

Keywords

neOCampus, Indoor Air Quality, Sensors, Photocatalysis, Nitrogen Oxides, Metal Oxides Semiconductor

Scientific goals

•    To integrate the LAAS / LCC gas sensors into the LMDC test device (Figure 1).

•    To compare the detection capacity of the NOx sensors and analyzer currently present at the LMDC.

•    To carry out abatement tests to test the depollution efficiency of various photocatalytic coatings, in particular based on ZnO and TiO2, and compare the results obtained with the two measurement systems (analyzer and sensors).

Contacts

delaveau@etud.insa-toulouse.fr, hot@insa-toulouse.fr, menini@laas.fr, pierre.fau@lcc-toulouse.fr, katia.fajerwerg@lcc-toulouse.fr

Context Presentation

The 5G evolution is the key driving factor that provides promising support for efficient V2X (Vehicle to everything) communications. Various applications with different requirements have been designed on vehicular networks to improve the driving experience by offering multiple services. In this thesis, we are interested by safety-critical applications and focus on collision avoidance systems between vehicles and pedestrians. This type of applications imposes strict reliability, wide connectivity, and a minimum end to end delay requirements. The high dynamic topology and the different types of communication technologies raise up challenges such as scalability, heterogeneity, and high traffic load to handle.

We have chosen MEC (Multiple Access/Mobile Edge Computing) that offers direct communication exchange between the mobile nodes (vehicles and pedestrians) and the network Infrastructure and used the Network Slicing mechanism to separate the critical vehicular traffic with high priority and strict requirements from other traffic. In our scenario a network congestion risk while vehicles and pedestrians send their BSM and CAM messages to the network infrastructure. Indeed, periodical transmissions could be unnecessary or harmful especially when the network density is high. Therefore, an intelligent scheme should be developed to adapt the transmission frequency of these messages to a network server without overloading the network, while considering the dynamic network state.

The main contribution of our work is to exploit the rich environment Information and analyze those data to take future decisions and to predict the network state to decrease the traffic load and finally to meet the objective requirements. Thus, we argue for the use of advanced Machine Learning techniques to learn from the available network data and take the appropriate action by choosing the best parameters' configuration.

Keywords

Autonomous vehicles, V2X Communications, Resource Allocation, Network Slicing, MEC, Machine Learning

Scientific goal

•    Improve the performance of V2X Communications in the context of critical road safety applications.

Contacts

Chaima.Zoghlami@irit.fr, Rahim.Kacimi@irit.fr, Riadh.Dhaou@irit.fr

Context Presentation

The theme addressed in this PhD concerns autonomous and connected vehicles. It is essential, after making a vehicle more reliable, to study how several connected autonomous vehicles will be able to interact to maximize the behavior of the collective (fluidity, fuel consumption and pollution). The Society of American Automotive Engineers (SAE International) has defined 5 levels of autonomy: level 1, in which the driver performs all maneuvers, at level 5, in which the vehicle is completely autonomous and can do without driver and / or passenger.This PhD concerns both level 4 autonomous vehicles in which the vehicle drives and the supervised driver can regain control of driving, as well as level 5 vehicles (total autonomy of the vehicle).

In this PhD, the aim is to study how each vehicle communicates with its neighboring vehicles and how it behaves / reacts in response to the information provided by its neighborhood. A lock consists in determining the information that is relevant to communicate among all the data recovered from the numerous sensors scattered in the vehicle and effectors. 

Keywords

Intelligent Transport System, Connected and Autonomous Vehicles, Multi-Agent System

Scientific goal

•    Learn how to use any data and which ones are the best to communicate

Contact

guilhem.marcillaud@irit.fr

Context Presentation

Several manufacturers now offer road vehicles that are almost autonomous (Tesla, Uber, Apple, EasyMile, ...). But they still require certain situations where human control is essential, which is why research is still important in this field. Experimentation is an essential means to evaluate software in a context close to reality. Evaluation in real conditions has several disadvantages: (i) user safety is difficult to guarantee; (ii) the cost of experimentation is very high; (iii) experimental sites in urban areas are very rare; (iv) the volumes of communications with the infrastructure and the necessary processing capacities are difficult to guarantee.

In order to carry out this research and evaluate it experimentally, we are developing an environment in which model cars move in a realistic environment.

 Keywords

Semi-real simulation, Intelligent Transport System

Scientific goal

•    Experiment algorithm with miniature model of vehicles

Contact

guilhem.marcillaud@irit.fr

Context Presentation

Soon, Air Traffic Control (ATC) will have to cope with a radical change in the structure of air transport. Apart from the increase in traffic that will push the system to its limits, the insertion of new aerial vehicles such as UAVs into the airspace, with different flight performances, will increase its heterogeneity. Previous work investigated the collision avoidance management problem using a decentralized distributed approach. To do so, an autonomous and generic multi-agent system has been proposed to address this complex problem.

The aim of this work is to test the genericity of the proposed multi-agent system (CAAMAS), already tested in simulation with airplanes, using it with real drones in ENAC’s drone aviary. Work carried out during the semester has allowed 1) to interface CAAMAS with ENAC drone software, Paparazzi UAV, 2) ease scenarists work by creating a user friendly interface to generate flight plans, 3) make CAAMAS easier to use by creating some configuration interfaces.Further work will focus on the trajectories modification of the agents to take into account the specificity of the UAV’s.

 Keywords

Autonomous UAV; Multi-Agent System; Self-Avoidance ; Self-Adaptation ; Artificial Intelligence ;

Scientific goals

•    Self-Organization of an autonomous vehicles trafic

Contacts

augustin.degas@irit.fr, elsy.kaddoum@irit.fr

Context Presentation

Connected autonomous vehicles of level 3 are vehicles in which the human driver delegates driving control in specific situations. During these situations, it may be necessary for the human to regain control of the driving activity.

The main objective of this thesis is to develop a system for the safe and efficient transition of two-way control between the human and the autonomous vehicle.For this, the system must identify by self-observation and in real time situations in which the current driver will no longer be able to ensure driving. He must also provide a context for assessing the criticality of the situation as quickly as possible in order to anticipate and react to it as best as possible. The driving context is composed of indicators that characterize the elements that describes part of the driving process. The system is based on self-adaptive multi-agent learning systems.

Keywords

self-adaptive multi-agent systems, autonomous vehicle

Scientific goals

•    Dynamic learning using multi-agent systems

•    Generic approach to supervise the activity of a system

•    Insure the acceptability of the system by human driver

Contacts

kristell.aguilar-alarcon@irit.fr, marie-pierre.gleizes@irit.fr, loic.caroux@univ-tlse2.fr

Context Presentation

The ANR SWITCH project is focused on studying the impact of smart city new transport modes on infrastructure. Although the new transports (electric, autonomous vehicles, transport on demand, bicycles, etc.) can facilitate intra-urban mobility and improve the quality of life, they can also create new constraints for which cities must be prepared. Then urban planners need tools to evaluate the impact of urban policies in terms of mobility and infrastructure to explore “What if? " scenarios.

These new simulation tools require mobility simulations on a city scale and on different time scales. As part of the meta-model design for the Smart City simulation, this internship is about developing an urban mobility model allowing to couple a flow-based circulation model with a multi-agent model allowing fine modeling of driver behavior. The internship must carry out a proof of concept on the Gama platform of this type of system on the scale of an agglomeration.

Keywords

Traffic Simulation, GAMA, Multi-Agent Simulation, Smart City, Transport

Scientific goal

•    To validate a mesoscopic traffic multi-agent model considering various transports facilities

Contacts

loic.sadou@gmail.com, nicolas.verstaevel@irit.fr, frederic.amblard@ut-capitole.fr

Context Presentation

MyGates is a part of autOCampus project to experiment and validates the smart mobility particularly for electrics, connected and autonomous vehicles in a dense urban environment.

In that way, a gates management system for autonomous vehicles will make easier experiments. It combines an automatic license plate with a car model recognizer to make it usable for classic car. It also contains database to keep information over authorized car and application designed for security officer to give temporary authorization for delivering driver with a photo of the vehicle and the plate.

MyGates is designed to be added on existing gates and only create the automation system with SALTO automaton that already are managing gates. MyGates will belong to the AIoT platform that will come out in the regional platform of research and innovation (PRRI) context containing smart camera and other smart devices.

Keywords

Autonomous Vehicles, Gates, CNN, ALPR, Salto, Nvidia Jetson Nano, autOCampus

Scientific goals

•    Create automatic and connected gates managements with plate and car model recognition

•    Create a classifier with many classes that are very similar

Contacts

vincent-nam.dang@irit.fr, thiebolt@irit.fr

Context Presentation

This thesis project answers to the desire of the IRIT-SMAC and CLLE-LTC teams to work together to develop intelligent innovative solutions for the car of the future, both acceptable to humans and technologically feasible. As such, this thesis in cognitive ergonomics focuses on “drivengers”/ partially automated cars interactions.

The general goal is to investigate the conditions allowing to increase drivenger’s acceptability of automated driving, and performance when taking over the control of vehicle from automation.The project will combine several experimental studies, first conducted on a driving simulator (Simul’auto platform - CCU platform - UT2J), then on a closed city experimental circuit (autOCampus circuit - UPS (under design)).

Keywords

User experience, Mobility, Safety, Human Factor, Automated Cars

Contact

maxime.delmas@univ-tlse2.fr