Mohamed Aymen Labiod

Actuellement les communications véhiculaires sont devenues une réalité guidée par diverses applications. Notamment, la diffusion de vidéo de qualité élevée avec des contraintes de faible latence requises par les applications temps réel. Grâce au niveau de compression jamais atteint auparavant, l’encodeur H.265/HEVC est très prometteur pour la diffusion de vidéos en temps réel dans les réseaux ad hoc véhiculaire (VANET). Néanmoins, la qualité de la vidéo reçue est pénalisée par les mauvaises caractéristiques du canal de transmission (disponibilité, non stationnarité, rapport signal à bruit, etc.). Afin d’améliorer et d’assurer une qualité vidéo minimale à la réception nous proposons dans ce travail une optimisation conjointe source-canal-protocole de la transmission en tenant compte à la fois des paramètres de transmission et d’encodage vidéo. Dans un premier temps, nous montrons l’intérêt et le gain apporté par les solutions dites inter-couches « cross-layer ». Par la suite, nous développons deux approches l’une exploitant un « cross-layer » entre la couche application et la couche MAC et une seconde exploitant les protocoles de transports dans l’adaptation du flux vidéo. En ce qui concerne la première approche nous proposons une solution utilisant une gestion hiérarchique des trames au niveau des files d’attentes de la couche MAC, basée sur l’importance des images du flux vidéo. Dans une seconde solution, nous retenons le codage par descriptions multiples comme solution de protection à la source. Les résultats de simulations obtenus pour plusieurs types de scénarios véhiculaires réalistes montrent que les différents schémas de transmission véhiculaire proposés offrent des améliorations significatives en termes de qualité vidéo à la réception et de retard de bout en bout par rapport aux schémas classiques.

Text-to-image (T2I) synthesis, driven by advancements in deep learning and generative models, has seen significant improvements, enabling the creation of highly realistic images from textual descriptions. However, this rapid development brings challenges in distinguishing synthetic images from genuine ones, raising concerns in critical areas such as security, privacy, and digital forensics. To address these concerns and ensure the reliability and authenticity of data, this paper conducts a systematic study on detecting fake images generated by text-to-image synthesis models. Specifically, it evaluates the effectiveness of deep learning methods that leverage ensemble learning for detecting fake images. Additionally, it introduces a multi-classification technique to attribute fake images to their source models, thereby enabling accountability for model misuse. The effectiveness of these methods is assessed through extensive simulations and proof-of-concept experiments. The results reveal that these methods can effectively detect fake images and associate them with their respective generation models, achieving impressive accuracy rates ranging from 98.00% to 99.87% on our custom dataset, “DeepGuardDB”. These findings highlight the potential of the proposed techniques to mitigate synthetic media risks, ensuring a safer digital space with preserved authenticity across various domains, including journalism, legal forensics, and public safety.

Live Service Migration in Multi-Access Edge Computing (MEC) environments is essential to ensure seamless service continuity for users in dynamic network scenarios. While advances such as QUIC-based connection migration and CRIU-based container migration have improved reliability, they remain dependent on maintaining active connections with the source server, which is impractical in conditions of high mobility, unstable links, or server outages. Additionally, existing solutions lack a centralized mechanism to optimally manage migration paths. This study introduces the Software-Defined Service Migration (SDSM) paradigm, leveraging a centralized SDN controller, the QUIC protocol, and MEC technologies to enhance service migration. Our approach overcomes the limitations of traditional methods by preemptively coordinating container and connection migrations. Empirical evaluations on a 5G-based testbed and emulated environments demonstrate significant reductions in migration times and improved recovery in mobile scenarios. These results establish SDSM as a robust solution for next-generation network applications, ensuring consistent and adaptive service quality.

EHealth has played an essential role in improving human life since its inception. Thanks to the advancements in Wireless communication and Software-defined Networks, the most demanding requirements of modern eHealth applications are resolved in their convergence, the 5th Communication Technology Generation. Besides, the cutting-edge technology also renovated the eHealth network landscape by employing Network Function Virtualization and Network Slicing concepts. By allowing third-party actors to manage and operate predeployed network infrastructure, 5G presented a novel network perspective that outdated the classic network research point of view. Recognizing such outstanding phenomenon, we update the eHealth literature by addressing the 5G's network perspective transformation. More importantly, we take a further step and present our enhanced vision to the community. The given network perspective emphasizes flexibility and adaptability regarding network infrastructure, which later aims to complete the 5G's current landscape. As the final result, we provide the community with a next-generation vision towards a unified, fully automated, and adaptively self-manageable network infrastructure. Four distinguished experimental approaches that support Healthcare transport communication (Opportunistic Network Enhancement, Connection Recovery, Self-assisted Coverage Deployment, and Data Transportation, and Priority Orchestration in High-density Network) are also designed to ensure the efficiency of the proposed idea. Our result analysis indicates that the proposed approach allows better network metrics (throughput, latency, and packet loss), especially in vehicular ad-hoc network environment, after enhancing the existing network infrastructure.

Summary: Delivering health care services in isolated rural regions through vehicular communications has recently attracted more attention. Real‐time video streaming is one of the most commonly used applications in such services. Escorting this increasing interest, there is a high demand for high‐quality streaming video in vehicular environments. However, these services face challenging network characteristics such as high dynamic topology and mobility, leading to high packet loss and degraded visual quality. This paper presents a smart approach to multi‐access terrestrial and non‐terrestrial networks (NTNs) for high‐quality and real‐time video streaming in vehicular environments to enhance healthcare services in remote areas. The proposed approach integrates a mobile telemedicine unit connected to an expert site through different channels, including satellite communication, 5 Generation (5G), and long‐term evolution (LTE) networks. A modified multipath QUIC extension (MPQUIC) was introduced to stream data hybridly through the network. Furthermore, multiple description coding (MDC) was applied to split data into two different descriptions and prioritize one over the other. Important data would be transmitted through cellular networks, and non‐priority data would be transmitted through satellite links, enhancing the reliability of video transmission. We demonstrate the effectiveness of our approach through the use of a connected ambulance as a case study. The Mininet‐Wifi was used to emulate network conditions. The peak signal‐to‐noise ratio (PSNR), structural SIMilarity (SSIM), and video multimethod assessment fusion (VMAF) were measured to evaluate the received video quality; the received data rate and packet loss were also calculated. The obtained results show our proposed method's efficiency by reducing latency by up to 60% and improving the receiving data rate by up to 143% compared with the classical MPQUIC. The proposed system enhances video quality by up to 70%. It minimizes packet loss by up to 50% compared with the unreliable QUIC, showcasing the potential to improve m‐health services' efficiency and mobility in isolated rural regions.

There is nowadays a growing demand in vehicular communications for real-time applications requiring video assistance. The new state-of-the-art high-efficiency video coding (HEVC) standard is very promising for real-time video streaming. It offers high coding efficiency, as well as dedicated low delay coding structures. Among these, the all intra (AI) coding structure guarantees minimal coding time at the expense of higher video bitrates, which therefore penalizes transmission performances. In this work, we propose an original cross-layer system in order to enhance received video quality in vehicular communications. The system is low complex and relies on a multiple description coding (MDC) approach. It is based on an adaptive mapping mechanism applied at the IEEE 802.11p standard medium access control (MAC) layer. Simulation results in a realistic vehicular environment demonstrate that for low delay video communications, the proposed method provides significant video quality improvements on the receiver side.

Nowadays, real-time vehicle applications increasingly rely on video acquisition and processing to detect or even identify vehicles and obstacles in the driving environment. In this letter, we propose an algorithm that allows reinforcing these operations by improving end-to-end video transmission quality in a vehicular context. The proposed low complexity solution gives highest priority to the scene regions of interest (ROI) on which the perception of the driving environment is based on. This is done by applying an adaptive cross-layer mapping of the ROI visual data packets at the IEEE 802.11p MAC layer. Realistic VANET simulation results demonstrate that for HEVC compressed video communications, the proposed system offers PSNR gains up to 11dB on the ROI part.