Posts tagged "réseau"

IRIT– Toulouse University

Keywords

LoRaWAN, roaming, handover, mobility, experiments.

LoRaWAN is a promising enabling technology for connected things. Nowadays, it is largely used in many application areas thanks to its relevant features such as long-range, low-cost, and low power consumption. However, as with all wireless technologies, mobility remains a major concern bringing end-devices out of their home operator coverage. In this paper, we investigate the inter-operator roaming capability in mobile scenarios. We proposed a novel LoRaWAN roaming scheme to enable inter-operator roaming based on DNS resolution and end-device context migration between networks. Moreover, we extended the LoRaWAN architecture while maintaining the integrity of the existing mechanisms and with a minimum prior configuration requirements. In order to validate our solution, we designed and implemented a test-bed platform integrating our extensions to Chirpstack. An extensive experimental study under various traffic loads demonstrates that the context migration delay perfectly fits the Class A LoRaWAN requirements.   

Scientific goals

- Extend LoRAWAN Architecture to support inter-operator roaming.

- Propose a DNS mechanism for home end-devices lookup.

- Support end-devices context migration from the home network to the visited network.

Contact

Mohamed HAMNACHE (mohamed.hamnache_at_irit.fr).

Rahim KACIMI (rahim.kacimi_at_irit.fr).

André-luc BEYLOT (andre-luc.beylot_at_irit.fr).

IRIT – Toulouse University                                         

Keywords

V2X Communication architectures, vulnerable road users safety, cooperative awareness messages.

With the evolution of Intelligent Transportation System (ITS), vehicles are capable of performing intelligent decisions and cooperative communications with other road users to exchange data and expand their environmental awareness. This communication is introduced as vehicle-to-everything (V2X) where vehicles exchange cooperative awareness messages « CAM » that includes important information (eg: position, speed, heading angle, vehicle type…). The generation rules of the CAM messages are defined by the ETSI standard and they are implemented in the facilities layer of each vehicle. The frequency of sending CAMs is between 10 and 1 Hz. However, we found out that following the standard, vehicles must generate a CAM if any change in their behavior is detected. This can lead to overloading the network if the number of road users is high, and might not be necessary if a vehicle has no.

Scientific goal

The aim of this work is to design a novel  neighboring mechanism to enhance the current version of the standard. Through a centralized server with a global vision of the network, the vehicles will be able to efficiently adapt the frequency of sending CAM messages using the information of surrounding neighbors received from the edge-server. Our mechanism is extended to support vulnerable road users (pedestrians, cyclists…) where reducing their transmission frequency undoubtedly helps in saving energy on their connected devices.

Contacts

Chaima.Zoghlami_at_irit.fr, Rahim.Kacimi_at_irit.fr, Riadh.Dhaou_at_irit.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

When a complex system requires the use of different components specified by different designers working on different domains, this greatly increases the number of virtual prototypes. These different components unfortunately tend to remain too independent of each other, thus preventing both different designers from collaborating and their systems from being interconnected to perform one or more tasks that could not be accomplished by one of these elements only. Co-simulation is the coupling of several simulation tools where each manages a part of a modular problem that allows each designer to interact with the complex system in order to maintain his business expertise and continue to use his own digital tools. For this co-simulation to work, the ability to exchange data between tools significantly, called interoperability, is required. We participate in the design of a co-simulation system that integrates di ff erent tools of simulation-trades based on the modeling of the behavior of devices like energy simulation and the simulation of wear of building materials within the same platform

Figure 1 : « Co-simulation architecture using dynamic data mediation »

Scientific Goals

- Take into account the concepts of architecture, communication (between simulators or with users) and visualization to define architectural models

- Architecture analysis managing interoperability

- Validation of this architecture and development of a tool for verifying certain properties of the architecture, such as coherence and semantics

Keywords

neOCampus, Interoperability, Mediation, Co-Simulation, Adaptive Multi-Agent Systems

Contacts

yassine.motie@irit.fralex@laas.frphilippe.truillet@irit.fr

Context Presentation

Anomaly detection in real fluid distribution applications is a difficult task, especially, when we seek to accurately detect different types of anomalies and possible sensor failures. Resolving this problem is increasingly important in building management and supervision applications for analysis and supervision. Our case study is based on a real context: sensor data from the SGE (Rangueil campus management and operation service in Toulouse).

We propose CoRP” Composition of Remarkable Points” a configurable approach based on pattern modelling, for the simultaneous detection of multiple anomalies. CoRP evaluates a set of patterns that are defined by users, in order to tag the remarkable points using labels, then detects among them the anomalies by composition of labels. CoRP is evaluated on real datasets of SGE and on state of the art datasets and is compared to classical approaches.

Figure 1: « Anomaly Detection in Sensor Networks »

Scientific Goals

- Detect different types of anomalies observed in real deployment

- Improve the supervision of sensor networks

- Use learning methods for anomaly detection on static and continuous data

Keywords

neOCampus, Sensor Data, Univariate Time Series, Anomaly Detection, Pattern-based Method

Contacts

Ines.ben-kraiem@irit.frOlivier.teste@irit.fr, andre.peninou@irit.fr

Context Presentation

The development of self-driving cars is increasing with 5G techniques. One of the biggest challenges posed by this domain is to protect pedestrians and to decrease accidents by detecting them before they occur. That’s why we need to decrease latency, improve reliability, optimize resource allocation and maintain connectivity… In this regard, we have proposed to preview vehicular and pedestrian traffic and send an alert message to warn them of collision risks. To achieve our goals, we started by proposing a network architecture based on an SDN approach, cell-less configuration, and decentralized computing nodes... Then we noticed that if all vehicles and pedestrians are going to communicate with the controller to send their position, the OpenFlow signaling is going to increase in the controller. So, we have simulated the up-link traffic and we have shown the interest of relieving the overload on the controller by sending position messages just in need. We developed an algorithm that estimate the time interval without future collision risks and decide the frequency of sending position messages in the up-link. Concerning the future work, we have to validate the obtained results with simulation.

Figure 1: Proposed SDN architecture

Scientific Goals

- Generate alert messages under low latency- Improve fiability and throughput- Optimize ressources allocation

Keywords

neOCampus, file, presentation, innovation, VANET, 5G, SDN …

Contacts

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

In an ever growing demand for connected objects (e.g SmartGardens, connected flowers, connected hives etc), neOCampus has extended its LoRaWAN infrastructure with the addition of a new industrial-grade LoRaWAN gateway. Bought by the Ecolab laboratory, it will get soon installed on its rooftop. This new gateway will address the downlink issue end-devices are facing. Actually, while the LoRa radio technology enables a 15km line-of-sight (LOS) range for data upload, an end-device will hardly get its downlink data from such a range! Hence, this additional gateway will greatly increases the downlink capability for most of our end-devices allowing a broader range of use-cases :) What a federated LoRaWAN infrastructure is useful for ? It means that neOCampus will be able to delegate end-devices management on a per-project basis to some local/remote managers. Through the https://lorawan.univ-tlse3.fr, these managers will be able to declare end-devices that will get recognized by all of our gateways. Moreover, it will also gives them the opportunity to finely tune their data flow through a broad range of data end-points like MQTT, HTTP sink etc

Contacts

neOCampus technical staff : neocampus-tech_at _irit.fr

Une architecture de réseau CCN est une approche alternative fondée sur un principe selon lequel un réseau de communication devrait permettre à un utilisateur de se concentrer sur les données dont il a besoin, plutôt que d’avoir à faire référence à un emplacement physique spécifique, d’où ces données doivent être récupérées. Elle permet la mise en cache des contenus pour réduire la congestion et améliorer le délai d’acheminement.

La durée de vie du contenu n’est pas prise en compte dans la dernière version de Content-Centric-Networking. Ainsi, dans ce travail, nous nous attachons à l’intégrer dans une architecture CCN pour les réseaux de capteurs et à montrer la pertinence de son exploitation.

Objectifs scientifiques

Nos objectifs de recherche sont multiples :

- Intégrer la notion de fraîcheur des données pour réaliser la satisfaction des utilisateurs.

- Comparer des approches réactives et proactives pour la mise à jour des contenus dans les caches.

- Réduire la consommation d’énergie des capteurs et maximiser la durée de vie du réseau.

- Optimiser le placement des contenus afin d’améliorer les performances du réseau.

Contacts

- Ghada JABER (IRIT-LAAS) : ghada.jaber@irit.fr

- Rahim KACIMI (IRIT) : kacimi@irit.fr

- Thierry GAYRAUD (LAAS) : gayraud@laas.fr