Réseaux de capteurs ubiquitous dans l'environnement NGN

Ubiquités Sensor Network (USN) is a conceptual network built over existing physical networks. It makes use of sensed data and provides knowledge services to anyone, anywhere and at anytime, and where the information is generated by using context awareness. Smart wearable devices and USNs are emerging rapidly providing many reliable services facilitating people life. Those very useful small end terminals and devices require a global communication substrate to provide a comprehensive global end user service. In 2010, the ITU-T provided the requirements to support USN applications and services in the Next Génération Network (NGN) environment to exploit the advantages of the core network. One of the main promising markets for the USN application and services is the e-Health. It provides continuous patients’ monitoring and enables a great improvement in medical services. On the other hand, Vehicular Ad-Hoc NETwork (VANET) is an emerging technology, which provides intelligent communication between mobile vehicles. Integrating VANET with USN has a great potential to improve road safety and traffic efficiency. Most VANET applications are applied in real time and they are sensitive to delay, especially those related to safety and health. In this work, we propose to use IP Multimedia Subsystem (IMS) as a service controller sub-layer in the USN environment providing a global substrate for a comprehensive end-to-end service. Moreover, we propose to integrate VANETs with USN for more rich applications and facilities, which will ease the life of humans. We started studying the challenges on the road to achieve this goalUbiquitous Sensor Network (USN) est un réseau conceptuel construit sur des réseaux physiques existantes. Il se sert des données détectées et fournit des services de connaissances à quiconque, n'importe où et à tout moment, et où l'information est générée en utilisant la sensibilité au contexte. Dispositifs et USN portables intelligents émergent rapidement en offrant de nombreux services fiables facilitant la vie des gens. Ces petits terminaux et terminaux très utiles besoin d'un substrat de communication globale pour fournir un service complet de l'utilisateur final global. En 2010, ITU -T a fourni les exigences pour supporter des applications et services USN dans le Next Generation Network (NGN) de l'environnement d'exploiter les avantages du réseau de base. L'un des principaux marchés prometteurs pour l'application et les services USN est la e- santé. Il fournit le suivi des patients en continu et permet une grande amélioration dans les services médicaux. D'autre part, des Véhicules Ad-hoc NETwork (VANET) est une technologie émergente qui permet une communication intelligente entre les véhicules mobiles. Intégrer VANET avec USN a un grand potentiel pour améliorer la sécurité routière et la fluidité du trafic. La plupart des applications VANET sont appliqués en temps réel et ils sont sensibles à retarder, en particulier ceux liés à la sécurité et à la santé. Dans ce travail, nous proposons d'utiliser l'IP Multimédia Subsystem (IMS) comme une sous- couche de contrôle de service dans l'environnement USN fournir un substrat mondiale pour un service complet de bout en bout. De plus, nous vous proposons d'intégrer VANETs avec USN pour des applications et des installations riches plus, ce qui facilitera la vie des humains. Nous avons commencé à étudier les défis sur la route pour atteindre cet objecti

Assisting V2V failure recovery using Device-to-Device Communications

This paper aims to propose a new solution for failure recovery (dead-ends) in Vehicle to Vehicle (V2V) communications through LTE-assisted Device-to-Device communications (D2D). Based on the enhanced networking capabilities offered by Intelligent Transportation Systems (ITS) architecture, our solution can efficiently assist V2V communications in failure recovery situations. We also derive an analytical model to evaluate generic V2V routing recovery failures. Moreover, the proposed hybrid model is simulated and compared to the generic model under different constrains of worst and best cases of D2D discovery and communication. According to our comparison and simulation results, the hybrid model decreases the delay for alarm message propagation to the destination (typically the Traffic Control Center TCC) through the Road Side Unit (RSU)Comment: 3 page

A Hybrid Model to Extend Vehicular Intercommunication V2V through D2D Architecture

In the recent years, many solutions for Vehicle to Vehicle (V2V) communication were proposed to overcome failure problems (also known as dead ends). This paper proposes a novel framework for V2V failure recovery using Device-to-Device (D2D) communications. Based on the unified Intelligent Transportation Systems (ITS) architecture, LTE-based D2D mechanisms can improve V2V dead ends failure recovery delays. This new paradigm of hybrid V2V-D2D communications overcomes the limitations of traditional V2V routing techniques. According to NS2 simulation results, the proposed hybrid model decreases the end to end delay (E2E) of messages delivery. A complete comparison of different D2D use cases (best & worst scenarios) is presented to show the enhancements brought by our solution compared to traditional V2V techniques.Comment: 6 page

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Smart Electricity Meter Load Prediction in Dubai Using MLR, ANN, RF, and ARIMA

Load forecasting is one of the main concerns for power utility companies. It plays a significant role in planning decisions, scheduling, operations, pricing, customer satisfaction, and system security. This helps smart utility companies deliver services more efficiently and analyze their operations in a way that can help optimize performance. In this paper, we propose a study of different techniques: multiple linear regression (MLR), random forests (RF), artificial neural networks (ANNs), and automatic regression integrated moving average (ARIMA). This study used electricity consumption data from Dubai. The main objective was to determine the load demand for the next month in the whole country and different municipal areas in Dubai, as well as to assist a utility company in future system scaling by adding new power stations for high-demand regions. The results showed that the accuracy of using ARIMA was about 93% when working with only a single district, but both ANN and RF achieved excellent accuracy of about 97% in all cases. In addition, the mean absolute percentage errors improved from 2.77 and 2.17 to 0.31 and 0.157 for ANN and RF, respectively, after anomaly elimination and the use of our proposal. Therefore, the use of an ANN for such data types is recommended in most cases, particularly when working on a complete dataset. Additionally, both the ANN and RF models are good choices when working on a single-category region because they both attained the same accuracy of almost 91.02 percent

Optimal Mobile IRS Deployment for Empowered 6G Networks

The development of cellular networks is driving the rapid growth of wireless communications. With the advent of the 5th Generation (5G) towards the future of the 6th Generation (6G) dedicated to achieving strong growth in traffic while reducing energy consumption, there is a need to solve the problems facing leveraging of these networks’ advantages and support both operators and mobile users. The main challenges for wireless communications are power consumption, Quality of Service (QoS), and the blind areas of a Non-Line-Of-Sight (NLOS) between mobile users and the Base Station (BS). The Intelligent Reflective Surface (IRS) of a reconfigurable meta material is a promising solution for solving some of the challenges of wireless communications. Additionally, it enhances the QoS of the received signals without the need for a power source to operate. Hence, it does not constitute an additional burden as it consists of passive elements. From the other hand, it provides the perfect solution to cover mobile users in blind areas without the need to deploy extra expensive BSs. In this work, we propose to equip buses by IRS allowing them to act as mobile IRS. These buses will become a relay for the surrounding moving vehicles, represented as taxies in the performance Section of this paper. Practically speaking, not all buses have to be IRS equipped. We propose various approaches for selecting the best buses equipped with IRS. In the first optimization approach, we adapt the classical IRS selections methods used in static context to the mobile case. It uses a Multi Integer Linear Programming (MILP) which gives optimal results but with a very long processing time. Thus, we propose a neural-network to learn the result of the MILP. As an alternative solution, another approach is proposed using a Markov decision problem (MDP) relying on Long Short-Term Memory (LSTM) to predict the positions of the surrounding moving vehicles. It is used to solve the optimization problem with the performance criteria targeted for each session. The performance of the proposed approaches are validated based on bus and taxi dataset for the city of Rome in Italy

Distributed D2D architecture for ITS services in advanced 4G networks

International audienceThis paper aims at using the novel concept of LTE-based Device-to-Device communications (D2D) as a new alternative for Intelligent Transportation Systems (ITS) vehicular communications in advanced 4G networks and beyond. We propose the Cellular Vehicular Network (CVN) solution as a reliable and scalable operator-assisted opportunistic architecture that supports hyper-local ITS services as 3GPP Proximity Services (ProSe). A distributed D2D architecture is proposed based on a hybrid clustering approach to organize vehicles into dynamic clusters. The proposed solution includes a network setup phase based on enhanced LTE authorization and authentication procedures for ITS nodes, and an LTE direct discovery phase. A BCMP queuing network is used to model the CVN core network and to evaluate the impact of core network entities load on the network setup delay. The results are validated with Matlab and compared to the network setup delays of an existing solutio

Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients

International audienceThe aim of this study was to estimate the incidence of COVID-19 disease in the French national population of dialysis patients, their course of illness and to identify the risk factors associated with mortality. Our study included all patients on dialysis recorded in the French REIN Registry in April 2020. Clinical characteristics at last follow-up and the evolution of COVID-19 illness severity over time were recorded for diagnosed cases (either suspicious clinical symptoms, characteristic signs on the chest scan or a positive reverse transcription polymerase chain reaction) for SARS-CoV-2. A total of 1,621 infected patients were reported on the REIN registry from March 16th, 2020 to May 4th, 2020. Of these, 344 died. The prevalence of COVID-19 patients varied from less than 1% to 10% between regions. The probability of being a case was higher in males, patients with diabetes, those in need of assistance for transfer or treated at a self-care unit. Dialysis at home was associated with a lower probability of being infected as was being a smoker, a former smoker, having an active malignancy, or peripheral vascular disease. Mortality in diagnosed cases (21%) was associated with the same causes as in the general population. Higher age, hypoalbuminemia and the presence of an ischemic heart disease were statistically independently associated with a higher risk of death. Being treated at a selfcare unit was associated with a lower risk. Thus, our study showed a relatively low frequency of COVID-19 among dialysis patients contrary to what might have been assumed