Modélisation analytique et contrôle d'admission dans les réseaux 802.11e pour une maîtrise de la Qualité de Service.

The QoS control in 802.11e EDCA (Enhanced Distributed Coordination Function) cannot be assured without an admission control mechanism which is capable of stopping the network from reaching a high saturation state and therefore guarantee the QoS requirements for voice and video applications.This admission control mechanism needs to predict the performance metrics that can be achieved by the network before deciding to admit any new flow. In order to obtain accurate decisions, we chose to use a prediction method based on an analytical model. The later must 1) grant the best accuracy of the prediction and 2) have a low computational complexity. Knowing that the current literatures' major analytical models do not satisfy these two conditions, we therefore develop a new analytical model for EDCA capable to predict the achievable performance metrics of different Access Categories (ACs) of EDCA such as the throughput and access delay.Hence, after the analytical modeling of the transmission time of different ACs while taking into account the TXOPLimit differentiation parameter, we develop an analytical model for EDCA based on a four dimensional Markov Chain. This model is developed first in the saturation conditions and then extended to general traffic conditions. Finally, we propose the admission control algorithm to be implemented within the QoS Access Point (QAP) that uses the analytical model proposed. As a final point, we propose an abacus solution to optimize the configuration of EDCA access parameters. The objective is to enhance the performance of the admission control algorithm by the optimal use of network resources.La maîtrise de la QoS dans 802.11e EDCA (Enhanced Distributed Coordination Function) ne peut être assurée que par un mécanisme de contrôle d'admission qui empêche le réseau d'atteindre un état de saturation critique et par la même garantit les besoins de QoS des applications voix/vidéo.Ce mécanisme de contrôle d'admission a besoin pour sa prise de décision de prédire les métriques de performances si un nouveau flux est admis. Dans le but de rendre les décisions efficaces, nous choisissons d'utiliser une méthode de prédiction basée sur un modèle analytique. Ce dernier doit remplir deux conditions : 1) fournir une bonne précision de prédiction et 2) avoir une complexité numérique faible et un temps de réponse limité. Vu que la majorité des modèles analytiques de la littérature ne satisfont pas à ces deux conditions, nous développons un nouveau modèle analytique pour EDCA qui est capable de prédire le débit et le délai d'accès des différentes Access Category (AC) d'EDCA.Ainsi, après la modélisation analytique du temps de transmission des ACs en prenant en compte le paramètre de différentiation TXOPLimit, nous développons un modèle analytique pour EDCA sous la forme d'une chaîne de Markov à quatre dimensions. Celui-ci est développé d'abord dans les conditions de saturation puis étendu aux conditions générales de trafic.Pour finir, nous proposons un algorithme de contrôle d'admission à implémenter au sein du point d'accès et qui utilise le modèle analytique proposé. Nous proposons un abaque de solution d'optimisation des paramètres d'accès d'EDCA. Le but étant d'améliorer les performances du mécanisme de contrôle d'admission par l'utilisation optimale des ressources du réseau

Robust self-organized wireless sensor network: A gene regulatory network bio-inspired approach

International audienceMinimal energy consumption and maximal event detection rate are among the main objectives in Wireless Sensor Networks (WSN). Sensor nodes are constrained units that have limited energy and low processing capabilities. Some challenging applications aim to spread a large number of nodes randomly in a geographical location to monitor it. Since it is difficult to access frequently and physically these sensors, an independent, failures resistant and distributed control, that is non-assisted by humans is mandatory. However, any intelligent strategy in WSN should have minimal requirements and low overhead. In this paper, we exploit the cell/node analogy to introduce a bio-inspired controller based on the principles of Gene Regulatory Network (GRN). This controller is adapted by the Genetic Algorithm. By implementing this controller in each node, the emergent network is characterized by an auto-organized, robust and adaptive behavior similar to a biological system. We compare the approach to a classical approach that uses redundancy as a failure resistance strategy, and found a significant increase in lifetime and event detection rates of the entire network

Towards using blockchain technology for IoT data access protection

International audienceIn the past few years, the number of wireless devices connected to the Internet has increased to a number that could reach billions in the next few years. While cloud computing is being seen as the solution to process this data, security challenges could not be addressed solely with this technology. Security problems will continue to increase with such a model, especially for private and sensitive data such as data personal and medical data collected with more and more sophisticated connected devices (forming the IoT). Thus the need for a fully decentralized peer to peer and secure technology to overcome these problems. The blockchain Technology is a promising approach giving the properties it brings to the field. This paper illustrates an architecture based on blockchain technology, and a protocol for data access, using smart contracts and a publisher-subscriber mechanism

Towards an Efficient Service Provisioning in Cloud of Things (CoT)

International audienceThe Cloud offers virtually unlimited resources and the ability to scale up or down applications as needed on the fly. Hence, the Cloud emerged as a suitable solution for large-scale IoT applications to cope with the rapidly increasing devices and data volume. Furthermore, IoT broadened the scope of the Cloud to the real world and enabled new service models such as the Sensing as a Service model. The convergence of both technologies stimulated innovations in both fields, we refer to this convergence as the Cloud of Things. The Cloud of Things enables users to request a complex IoT service (IoT application composed of several interconnected micro services) and deploy it seamlessly. However, deploying a complex IoT service in the Cloud of Things infrastructure is a difficult process due to the different types of physical nodes (Cloud data centers, IoT devices, gateways, etc.) and multiple architectures to collect and process data. Furthermore, network usage largely depends on the placement of different services across the network. In this paper, we present an efficient provisioning model of IoT services formulated as a Mixed Integer Problem. The objective is to minimize the cost of the deployment of IoT services in Cloud of Things infrastructure, through optimizing resources usage across physical nodes and bandwidth consumption over the network

Towards using blockchain technology for eHealth data access management

International audienceeHealth is a technology that is growing in importance over time, varying from remote access to Medical Records, such as Electronic Health Records (EHR), or Electronic Medical Records (EMR), to real-time data exchange from different on-body sensors coming from different patients. With this huge amount of critical data being exchanged, problems and challenges arise. Privacy and confidentiality of this critical medical data are of high concern to the patients and authorized persons to use this data. On the other hand, scalability and interoperability are also important problems that should be considered in the final solution. This paper illustrates the specific problems and highlights the benefits of the blockchain technology for the deployment of a secure and a scalable solution for medical data exchange in order to have the best performance possible

Blockchain Technology: Is It a Good Candidate for Securing IoT Sensitive Medical Data?

In the past few years, the number of wireless devices connected to the Internet has increased to a number that could reach billions in the next few years. While cloud computing is being seen as the solution to process this data, security challenges could not be addressed solely with this technology. Security problems will continue to increase with such a model, especially for private and sensitive data such as personal data and medical data collected with more and more smarter connected devices constituting the so called Internet of Things. As a consequence, there is an urgent need for a fully decentralized peer-to-peer and secure technology solution to overcome these problems. The blockchain technology is a promising just-in-time solution that brings the required properties to the field. However, there are still challenges to address before using it in the context of IoT. This paper discusses these challenges and proposes a secure IoT architecture for medical data based on blockchain technology. The solution introduces a protocol for data access, smart contracts and a publisher-subscriber mechanism for notification. A simple analytical model is also presented to highlight the performance of the system. An implementation of the solution as a proof of concept is also presented

An IoT-Cloud Based Solution for Real-Time and Batch Processing of Big Data: Application in Healthcare

International audienceWith the large use of Internet of Things (IoT) today everything around us seems to generate data. The ever increasing number of connected things or objects (IoT) is coupled with a growing volume of data generated at a continually increasing rate. Especially where data is big or there is a need to process it cloud infrastructures with their scalability and easy access are becoming the solution of choice for storage and processing. In the context of healthcare applications where medical sensors collect health data from patients and send it to the cloud two issues frequently appear in relation to 'Big Data'. The first issue is related to real-time analysis introduced by the increasing velocity at which data is generated especially from connected devices (IoT). This data should be analyzed continuously in real-time in order to take appropriate actions regarding the patient's care plan. Moreover medical data accumulated from different patients over time constitutes an important training dataset that can be used to train machine learning models in order to perform smarter disease prediction and treatment. This gives rise to another issue regarding long-term batch processing of often huge volumes of stored data. To deal with these issues we propose an IoT-Cloud based framework for real-time and batch processing of Big Data in the healthcare domain. We implement the proposed solution on Amazon Cloud operator known as Amazon Web Services (AWS) and use a Raspberry pi as an IoT device to generate data in real time. We test the solution with the specific application of ECG monitoring and abnormality reporting. We analyze the performance of the implemented system in terms of response time by varying the velocity and volume of the analyzed data. We also discuss how the cloud resources should be provisioned in order to guarantee processing performance for both long-term and real-time scenarios. To ensure a good tradeoff between cost and processing performance resources provision should be adapted to the exact needs and characteristics of the considered application

Performance/cost analysis of a cloud based solution for big data analytic: Application in intrusion detection

International audienceThe essential target of ‘Big Data’ technology is to provide new techniques and tools to assimilate and store large amount of generated data in a way to analyze and process it to get insights and predictions that can offer new opportunities towards the improvement of our life in different domains. In this context, ‘Big Data’ treats two essential issues: the real-time analysis issue introduced by the increasing velocity at which data is generated, and the long-term analysis issue introduced by the huge volume of stored data. To deal with these two issues, we propose in this paper a Cloud-based solution for big data analytic on Amazon Cloud operator. Our objective is to evaluate the performance of Big Data services offered regarding the volume/velocity of the processed data. The dataset we use contains information about”network connections” in approximately 5 million records with 41 features; the solution works as a network intrusion detector. It receives data records in real time from a raspberry pi node and predicts if the connection is bad (malicious intrusion or attack) or good (normal connection). The prediction model was made using a logistic regression network. We evaluate the cloud resources needed to train the machine learning model (batch processing), and to predict the new streaming data with the trained network in real time (real time processing). The solution worked very well with high accuracy and the results show that when working with Big Data in the cloud, we are mainly dealing with a cost/performance trade-off, the processing performance in term of response time for both long-term and real-time analysis can be always guaranteed once the cloud resources are well provisioned according to the needs

Towards voice/video application support in 802.11e WLANs: A model-based admission control algorithm

International audienceSupporting emergent voice/video applications in all wireless technologies is a requirement in the Next Generation Network (NGN) where Wireless Local Area Networks (WLANs) is a main component. For this type of applications, QoS needs to be fully maintained in order to assure user satisfaction. Actually, QoS control in 802.11e WLANs to support real time voice/video services remains an open problem. All the solutions that only aim to enhance the performance of the Enhanced Distributed Channel Access (EDCA) mechanism cannot resolve the performance degradation problem once the channel becomes saturated. Hence, an efficient admission control scheme in EDCA is the key to guarantee the QoS required by voice/video services in WLANs. In this paper, we propose a model-based admission control algorithm that is located within the QoS Access Point (QAP). An accurate analytical model is used to predict the QoS metrics that can be achieved once a new flow is introduced in the WLAN. Based on this prediction and on the QoS constraints of already admitted (active) flows as well as of the new flow, the QAP takes the appropriate decision for the new flow. The proposed admission control scheme is fully compatible with the legacy 802.11e EDCA MAC protocol. It is validated numerically and through simulations using several realistic usage scenarios