Resource Allocation in Ad Hoc Networks with Two-Hop Interference Resolution

6International audienc

Performances des reseaux : un outil base sur la simulation

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Dissemination strategies in realistic V2V highway networks: The Madrid trace case

International audienc

Congestion Control in IoT Networks Using Artificial Intelligence Techniques

International audienceThe widespread deployment of Internet of Things ( IoT) devices promises substantial benefits, enabling applications ranging from smart homes to connected cities. However, to make IoT truly practical and efficient, several technical challenges must be addressed. These include the limited resources of IoT devices in terms of energy and storage, as well as the heterogeneous communication patterns of IoT applications. These limitations significantly impact data throughput and low-latency requirements imposed by IoT applications such as smart healthcare. Consequently, designing effective congestion control approaches proves to be complex.Our thesis, titled 'Smart Congestion Control Schemes for IoT Networks based on Artificial Intelligence Methods,' proposes leveraging Artificial Intelligence to design innovative congestion control approaches. In our research, we began with a comprehensive review of state-of-the-art congestion control approaches proposed in the literature for IoT networks, comparing them with simple methods based on static congestion control parameters. While straightforward, these approaches can severely impair network efficiency. Our critical analysis of these works highlights their limitations and weaknesses, primarily relying on estimations and network traffic regulations unsuited for IoT applications.In this research, we propose a novel congestion control approach based on Artificial Intelligence (AI). This approach primarily relies on Deep Learning (DL), which infers knowledge from massive and multi-scale data generated by IoT devices to predict network conditions, and Machine Learning (ML), which dynamically adjusts congestion controller parameters. The DL model we propose, based on time series data, predicts congestion based on loss rates. Additionally, we introduce an ML model that dynamically adjusts congestion control parameters based on the loss rates predicted by the DL model. These dynamically predicted congestion control parameters are then assigned to the congestion controller deployed in each IoT device, optimizing throughput, avoiding data loss, and ensuring fairness.In conclusion, our approach, implemented and evaluated using our simulator and the Contiki/Cooja environment, demonstrates that our proposed method outperforms traditional approaches. All quantitative results indicate that our algorithms achieve a better balance between throughput, latency, reliability, overhead, and fairness while meeting the requirements of IoT applications, particularly in patient monitoring scenarios within smart healthcare

On Coordinated Scheduling of Radio and Computing Resources in Cloud-RAN

International audienceCloud Radio Access Network is a promising mobile network architecture based on centralizing the baseband processing of many cellular base stations in a BBU (BaseBand Unit) pool. Such architecture has many advantages. However, computing resources are shared among the base stations connected to the BBU pool. It is challenging to schedule the processing of users' data, especially on overloaded BBU pools, while respecting the time constraints imposed by the Hybrid Automatic Repeat Request (HARQ) mechanism. Given that the processing time of users' data and the computing requirement depends on the radio parameters such as the Modulation and Coding Scheme (MCS), we propose to enable the coordination between radio and computing resources schedulers; such coordination makes the selection of MCS dependent on the availability of radio and computing resources and on the ability to process data while respecting the HARQ-deadline. In this context, we propose and evaluate three Integer Linear Programming (ILP)-based schemes and three low-complexity heuristics, demonstrating their ability to reduce the wasted transmission power. Moreover, we evaluate the performance of the coordination under a multiservices scenario consisting of two services having heterogeneous requirements, enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC)

A stochastic mobility model for traffic forecasting in urban environments

International audienc

Resource Allocation in Ad Hoc Networks with Two-Hop Interference Resolution

6International audienc

A new mobility-based clustering algorithm for Vehicular Ad Hoc Networks (VANETs)

International audienceClustering in vehicular ad hoc networks (VANETs) is a challenging issue due to the highly dynamic vehicle mobility and frequent communication disconnections problems. Recent years' research have proven that mobility-based clustering mechanisms considering speed, moving direction, position, destination and density, were more effective in improving cluster stability. In this paper, we propose a new mobility-based and stability-based clustering algorithm (MSCA) for urban city scenario, which makes use of vehicle's moving direction, relative position and link lifetime estimation. We evaluate the performance of our proposed algorithm in terms of changing maximum lane speed and traffic flow rate. Our proposed algorithm performs well in terms of average cluster head lifetime and average number of clusters