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International audience; Anomaly in Online Social Network can be designated as an unusual or illegal activity of an individual. It can also be considered as an outlier or a surprising truth. Due to the emergence of social networking sites such as Facebook, Instagram, etc., the number of negative impacts of aggressive and bullying phenomena has increased exponentially. Anomaly detection is a problem of crucial importance which has attracted researchers since the 2000s. This problem is often carried out, thanks to deep learning, artificial intelligence and statistics. Several methods have been devoted to solving the problem of detecting abnormal behavior on social media, which are kept under three different types: structural methods which are based on the analysis of graphs of social networks, behavioral methods which are based on the extraction and analysis of user activities and hybrid methods which combine the two types of methods mentioned above. This survey reviews various methods of data mining for the detection of anomalies to provide a better assessment that can facilitate the understanding of this area.

International audience; Nowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal.

International audience; This paper proposes a novel approach based on adaptive dimensionality reduction (ADR) and a semi-supervised 3-D convolutional neural network (3-D CNN) for the spectro-spatial classification of hyperspectral images (HSIs). It tackles the problem of curse of dimensionality and the limited number of training samples by selecting the most relevant spectral bands. The selected bands should be informative, discriminative and distinctive. They are fed into a semi-supervised 3-D CNN feature extractor, then a linear regression classifier to produce the classification map. In fact, the proposed semi-supervised 3-D CNN model seeks to extract the deep spectral and spatial features based on convolutional encoder-decoder to enhance the HSI classification. It uses several 3-D convolution and max-pooling layers to extract these features from the selected relevant bands. The main advantage of the proposed approach is to reduce the high dimensionality of HSI, preserve the relevant spectro-spatial information and enhance the classification using few labeled training samples. Experimental studies are carried out on three real HSI data sets: Indian Pines, Pavia University, and Salinas. The obtained results show that the proposed approach performs better than other deep learning-based methods including CNN-based methods, and significantly improves the classification accuracy of HSIs.

Internet of Mobile Things (IoMT) is a new paradigm of the Internet of Things (IoT) where devices are inherently mobile. While mobility enables innovative applications and allows new services, it remains a challenging issue as it causes disconnection of nodes and intermittent connectivity, which negatively impacts the network operation and performance. In addition, energy saving presents a real challenge for networks with limited resources. In this context, a new energy efficient and mobility aware routing protocol called EMA-RPL is proposed based on the well-known Routing Protocol for Low power and Lossy Networks (the RPL standard). Unlike the RPL which is basically designed for static devices, EMA-RPL enables better sustaining of connectivity of mobile nodes and conserving energy. The proposed protocol integrates an enhanced mobility detection method through a continuous control of the distance between the mobile node and its attachment, a novel point of attachment prediction based on the new location of the mobile node, and an efficient replacement strategy preserving the mobile node energy. EMA-RPL overcomes and mitigates problems caused by the mobility of nodes. Simulations using Cooja/Contiki show that EMA-RPL outperforms both the RPL and its mobility aware variant (MRPL) in terms of handover delay, data loss rate, signaling cost and energy consumption.

International audience; Internet of Things (IoT) is increasingly deployed in different domains and environments including smart homes, smart cities, healthcare, industry 4.0, and smart agriculture, by connecting a large number of physical objects to deliver a new class of applications. The rising number of these connected objects and their heterogeneity have raised new research directions and challenges regarding their communications, scalability and the large amount of data that generate. As a result, new communication technologies have been proposed to be applied in these environments and applications, mainly to consider their main inherent properties which are the information that generate and handle, and the content that disseminate. Among the adopted techniques and recently integrated into the IoT is the ICN (Information Centric Networking) paradigm. The choice of integration of ICN in the context of IoT is mainly motivated by all the advantages it represents, in particular content caching and decoupling senders and receivers. In this paper, we provide a detailed analysis of the motivations behind using ICN in IoT environments and a survey of existing research work that have already applied ICN as a communication support for IoT applications.

International audience; Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods.

International audience

Drought disasters significantly affected human life and water resources. Therefore, forecasting methods like statistical models, machine learning, and deep learning architectures help scientists to take effective decisions to decrease the effects of natural disasters by providing decision-making plans. Droughts can be forecasted using meteoro-logical indices like the standardized precipitation evapotranspiration index (SPEI), which aid governments in taking drought-prevention steps. In this paper, we present a big drought architecture for drought modeling and forecasting. The proposed architecture is composed of 5 layers: Data collection, data preprocessing, data storage, data processing and interpretation, and decision making. Besides, we present a comparative study between three different methods ARIMA, PROPHET, and LSTM for drought forecasting. Three different metrics are used for the performance evaluation Root Mean Squared Error (RMSE), coefficient of determination (R2), and Mean Squared Error (MAE). Experiments are carried out using data from the province of Jiangsu. Results revealed that LSTM outperformed the other models, and ARIMA outperformed the PROPHET model.

International audience; Low-power Internet of Things (IoT) networks are widely deployed in various environments with resource constrained devices, making their states monitoring particularly challenging. In this paper, we propose an adaptive monitoring mechanism for low-power IoT devices, by using a reinforcement learning (RL) method to automatically adapt the polling frequencies of the collected attributes. Our goal is to minimize the number of monitoring packets while keeping accurate and timely detection of threshold crossings associated to supervised attributes. We study the various RL parameter settings under different monitoring attribute behaviors using OpenAi Gym simulator. We implement the RL based adaptive polling in Contiki OS and we evaluate its performance using Cooja simulator. Our results show that our approach converges to optimal polling frequencies and outperforms static periodic notification-based methods by reducing the number of monitoring packets, with a percentage of correctly detected threshold crossings exceeding 80%.

International audience; Surrogate modeling is an increasingly popular tool for engineering design as it enables to model the performance of very complex systems with a limited computational cost. A large number of techniques exists for the surrogate modeling of continuous functions, however, only a very few methods for the surrogate modeling of mixed continuous/discrete functions have been developed. In this chapter, existing adaptations and variants of Gaussian process-based surrogate modeling techniques for mixed continuous/discrete variables are described, discussed and compared on several analytical test-cases and aerospace design problems.

International audience; In just a few years, social networking sites have become the most popular landmarks on the Internet. They revolutionized the way we communicate, and socialized the Web. However, while it is now impossible to deny their impact, it can take a variety of forms, not all of them are positive. As a result, the detection of anomalies on social networks is a topic of current research that has attracted researchers since the 2000s. This problem is of crucial importance to prevent abnormal activities. So far, all existing works have been devoted to one-dimensional networks. Our approach attempts to provide a new anomaly detection method based on examining relationships between OSN users using multidimensional networks.

With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However, image classification and annotation remain a challenging problem due to many related issues like the problem of ambiguity between classes. This problem can affect the quality of both classification and annotation results. In this paper, we propose an ontology-based image classification and annotation approach. Our contributions consist of the following: (1) exploiting ontological relationships between classes during both image classification and annotation processes; (2) combining the outputs of hypernym–hyponym classifiers to lead to a better discrimination between classes; and (3) annotating images by combining hypernym and hyponym classification results in order to improve image annotation and to reduce the ambiguous and inconsistent annotations. The aim is to improve image classification and annotation by using ontologies. Several strategies have been experimented, and the obtained results have shown that our proposal improves image classification and annotation.

Today, the existence of several air pollution sources, such as household, transportation and industrial activities, increases the levels of air criteria pollutants in the urban areas and thus causes premature deaths, cardiovascular diseases and acute respiratory infections. To control the pollution levels, wireless technologies, like Wireless Sensor Networks (WSNs), Vehicular Sensor Networks (VSNs) and cellular networks, have become intensively adopted to collect real world data and to transmit decisions to people and interested governments in real time. Within this framework, we developed a new dynamic air pollution monitoring protocol using a communication model dedicated to enable wireless access for mobile objects. Based on Network Simulator 3 (NS-3), the obtained experimental results show that our solution can be applied to control the air data with better performance either in low or high density of vehicles in the regions of interest.

International audience; The Internet of Things (IoT) ecosystem includes a plethora of devices equipped with heterogeneous communication interfaces. They exhibit different mobility patterns and hardware constraints as memory, battery and processing power. On the other hand, IoT applications are overlayed on top of these constrained-devices imposing stringent requirements in term of data availability, data coherence, and response latency. To cope with these challenges, Named Data Networking (NDN) architecture is positioned in the middle layer to act as the networking layer. By providing easy data access thanks to the unique and location-independent content names, in-network caching and name-based routing, NDN expects to hide from IoT applications the complexity and diversity of the underlying Things by adapting the network operation to their features. In this paper, we focus on data availability requirements threatened by high IoT network dynamics related to sensors mobility. We address the producer mobility issue in NDN/IoT networks using the routing-based approach. We propose a novel and efficient forwarding algorithm named AFIRM in order to support producer mobility, and compare its performances to those of other relevant solutions based on the routing approach.

International audience; In the context of software product line engineering, collaborative configuration is a decision-making process where multiple stakeholders contribute in building a single product specification. Several approaches addressing collaboration during configuration have already been proposed, but we still have little hard evidence about their effectiveness and little understanding about how collaborative configuration process should be carried out. This paper presents a classification framework to help understand existing collaborative configuration approaches. To elaborate it, a systematic mapping study was conducted guided by three research questions and 41 primary studies was selected out of 238 identified ones. The proposed framework is composed of four dimensions capturing main aspects related to configuration approaches: purpose, collaboration, process and tool. Each dimension is itself multi-faceted and a set of attributes is associated to each facet. Using this framework, we position and classify existing approaches, structure the representation of each approach characteristics, highlight their strengths and weaknesses, compare them to each other, and identify open issues. This study gives a solid foundation for classifying existing and fu-* Corresponding author ture approaches for product lines collaborative configuration. Researchers and practitioners can use our framework for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones.

International audience; The Information-Centric Networking (ICN) paradigm is shaping the foreseen future Internet architecture by focusing on the data itself rather than its hosting location. It is a shift from a host-centric communication model to a content-centric model supporting among others unique and location-independent content names, in-network caching and name-based routing. By leveraging the easy data access, and reducing both the retrieval delay and the load on the data producer, the ICN can be a viable framework to support the Internet of Things (IoT), interconnecting billions of heterogeneous constrained objects. Among several ICN architectures, the Named Data Networking (NDN) is considered as a suitable ICN architecture for IoT systems. However, its default caching approach lacks a data freshness mechanism, while IoT data are transient and frequently updated by the producer which imposes stringent requirements in terms of information freshness. Furthermore, IoT devices are usually resource-constrained with harsh limitations on energy, memory and processing power. We propose in this paper a caching strategy and a novel cache freshness mechanism to monitor the validity of cached contents in an IoT environment while minimizing the caching process cost. We compared our solution to several relevant schemes using the ccnSim simulator. Our solution exhibits the best system performances in terms of hop reduction ratio, server hit reduction ratio and response latency, yet it provides the lowest cache cost and significantly improves the content validity.

In recent years, the diffusion of electric vehicles(EVs) is of great importance in the road transport and automotive sectors. Reducing electricity consumption and increasing battery autonomy are actually motivating vehicle manufacturers to focus their attention on the EV concept. The evaluation of EV performance through realistic scenarios in traffic simulations is still a key issue. In fact, the road traffic Simulator Simulation of Urban Mobility (SUMO) integrates an energy model for calculating the EV energy consumption. However, this model underestimates the energy consumption values in the real world. In this paper, we intend to investigate the energy consumption models existing in the literature. Then, we assess the performance of the existing energy model in SUMO through a set of simulation scenarios. Finally, we present the main requirements for a realistic, accurate and scalable energy model to estimate the instantaneous energy consumption for EVs.

A new hyperspectral texture descriptor, Relative Spectral Difference Occurrence Matrix (RSDOM) is proposed. Developed in a metrological framework, it simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. It is generic and adapted for any number of spectral band or range. As validation, a texture classification scheme is applied on HyTexiLa dataset using RSDOM. The obtained accuracy is excellent (95.6%), comparable to Opponent Band Local Binary Pattern (OBLBP) but at a much-reduced feature size (0.1% of OBLBP's). © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Data as a Service (DaaS) is seen as a promising cloud offering for wrangling the overload of information and making it available across cloud platforms anytime and anywhere. While there exist a large number of DaaS providers in the market, each one has a different way to describe its provided services as well as supplied datasets. The lack of a well-defined machine-readable model strongly hinders the automatic selection and composition of DaaSs. This paper presents MoDaaS, a model-driven framework for the modeling and the description of DaaS services. MoDaaS enables DaaS providers to describe their services capabilities and concerns according to a shared ontology, thereafter it enables them to automatically generate service views in order to assist the integration and data exchange between heterogeneous services.

International audience; Spaceborne and airborne sensors deliver a huge number of Earth Observation Data every day. In this context, we can easily observe the whole earth from its different sides. Therefore, this big data is important in remote sensing and could be exploited in several domains requiring image classification, natural hazard monitoring, global climate change, agriculture, urban planning. Over the last five years, Convolutional Neural Networks (CNN) emerged as the most successful technique for the image classification task, as well as a number of other computer vision tasks. However, to train millions of parameters in CNN one requires a huge amount of annotated data. This requirement leads to a significant challenge if the available training data is limited for a target task at hand. To address this challenge, in the recent literature, researchers proposed various ways to apply a technique called Transfer Learning to transfer the knowledge gained by training CNNs parameters on some large annotated dataset to the target task with limited availability of training data. Most of our work in this paper was dedicated to proposing a hybrid classification of remote sensing images. This architecture combines Spark RDD image coding to consider image's local regions, pre-trained VGGNET-16 and UNET for image segmentation and SVM (Support Vector Machines) from spark Machine Learning to achieve labeling task.

International audience; Unmanned Aerial Vehicles (UAVs) have known much popularity for dangerous missions for human operators or for applications which do not need human intervention (such as monitoring and surveillance of physical infrastructures and interest areas). They operate in frequency bands (IEEE L-Band, IEEE S-Band, and ISM band) shared with other users. Accordingly, these frequency bands have become overcrowded and UAVs may face the issue of spectrum scarcity. Furthermore, there are particular difficulties associated with aeronautical communication links. Cognitive radio (CR) has emerged as a promising strategy for resolving the problems caused by scarce spectrum. It checks the spectrum availability and allows the adjustment of the transmission parameters. The aim is to opportunistically use spectral bands with minimum interference to applications or other users. In this paper, we present a centralized CR based frequency allocation scheme for UAV-Ground Control Station (GCS) communication in surveillance applications within an urban environment. In the proposed model, the GCS monitors and allocates available WiMAX frequencies using CR and Software Defined Radio (SDR). If no WiMAX frequency is available at a given time, the Wi-Fi will be used. Therefore in the worst case, our approach will have the same performance as when the Wi-Fi is only used for UAV-GCS communication.

International audience; In recent years, a large number of researchers investigate the conception of systems that use a unique Unmanned Ariel Vehicles (UAV) or multiple independent UAVs to conduct civil or military missions, with minimal human intervention. In this paper we focus on using multiple UAVs to cooperatively monitor a crowded area. Communication in such UAVs network is an ongoing project. Due to the lack of proper communication standards and rules, designing a reliable communication model is essential for: (i) multi-UAV coordination, (ii) efficient bandwidth sharing according to data priority and urgency and (iii) avoiding useless transmission of the same data by multiple UAVs. To address the above challenges, we propose a centralized data-oriented communication architecture for crowd surveillance allocations using an UAV fleet. The Ground Control Station (GCS) is used as a central coordinator to manage bandwidth usage for the UAV fleet in its coverage area. To allow UAVs to send priority messages urgently to the GCS, we define two classes of urgent messages: critical state and important result. The class of the data as well as other relevant information about the detected event will be used by the GCS to authorize or not UAV data transmission and hence to optimize the bandwidth usage efficiency.

International audience; To fulfill its complex business demands, an organization may require the integration of many SaaS applications. A main challenge in this regard is to select the suitable SaaS offerings that satisfy all the business requirements. Due to the diversity of QoS needs and the large number of SaaS providers, selecting the suitable set of SaaS applications to be integrated may result in QoS degradation and higher costs. In this paper, we propose to use a System-of-Systems (SoS)-based approach for the selection and integration of SaaS. We believe that the use of SoS and its dynamic properties could improve the service selection at SaaS level. To do so, we propose three Mixed Integer Programming models that optimize the global SaaS behavior by minimizing the overall delivery costs, and maximizing the global functional and QoS utilities. The implementation of the approach and the experimental results highlight the efficiency of our proposed solution.

Nowadays, Wireless Body Area Networks (WBANs) are revolutionizing the field of healthcare applications which is deeply related to human life and requires a noteworthy attention. In this medical context, reliability, energy consumption and real time monitoring are the main Quality of Service (QoS) challenges to satisfy. This kind of networks relies on wireless channel medium which is sensitive to several factors such as body shadowing, interference, human postures and body signal absorption. In this paper, our main focus is to exploit the knowledge related to the link quality to select the most appropriate Body Node Coordinator (BNC) in order to improve the overall network performance. Our experimental study which is based on real testbeds revealed that the position of the BNC is sensitive to human morphology and practiced activity.

International audience; The increasing amount of remote sensing data has opened the door to new challenging research topics. Nowadays, significant efforts are devoted to pixel and object based classification in case of massive data. This paper addresses the problem of semantic segmentation of big remote sensing images. To do this, we proposed a top-down approach based on two main steps. The first step aims to compute features at the object-level. These features constitute the input of a multi-layer feed-forward network to generate a structure for classifying remote sensing objects. The goal of the second step is to use this structure to label every pixel in new images. Several experiments are conducted based on real datasets and results show good classification accuracy of the proposed approach. In addition, the comparison with existing classification techniques proves the effectiveness of the proposed approach especially for big remote sensing data.

International audience; In-network caching in Named Data Networking (NDN) based Internet of Things (IoT) plays a central role for efficient data dissemination. Data cached throughout the network may quickly become obsolete as they are transient and frequently updated by their producers. As such, NDN-based IoT networks impose stringent requirement in terms of data freshness. While various cache replacement policies were proposed, none has considered the cache freshness requirement. In this paper, we introduce a novel cache replacement policy called Least Fresh First (LFF) integrating the cache freshness requirement. LFF evicts invalid cached contents based on time series forecasting of sensors future events. Extensive simulations are performed to evaluate the performance of LFF and to compare it to the different well-known cache replacement policies in ICN-based IoT networks. The obtained results show that LFF significantly improves data freshness compared to other policies, while enhancing the server hit reduction ratio, the hop reduction ratio and the response latency.

International audience; Business intelligence (BI) is a set of technologies and strategies allowing analyzing data and providing information to help in the decision-making process. Today, BI is used in many sectors and shows marvelous results. In this paper, we are interested in supporting decision-making in higher education. BI concepts when integrated to the process of academic affairs can make significant improvement. The present paper aims at describing a BI solution to support academic affairs at Taibah University. Using BI, we can take advantages of a set of analytical tools that support decision-making for different types of users (students, faculty members, administrators and decision makers). The proposed BI solution consists essentially of three mains tasks: (1) collecting data from different sources using the three operations (Extract, Transform and Load), (2) proposing a multidimensional solution that describes the academic processes, and (3) visualizing results through a set of dashboards and reports. Experiments are made using the SQL Server Data Tools. Three steps are detailed which are: SQL Server Integration Services, SQL Server Analysis Services and SQL Server Reporting Services. The proposed solution provides many statistical and predictive indicators needed in academic tasks.

International audience; The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

International audience; In this paper, we design and experiment ODYSSE (Opportunistic Duty cYcle based routing protocol for wirelesS Sensor nEtworks) protocol. It combines three main mechanisms: (i) duty cycle, where nodes alternate between active and sleep states, (ii) opportunistic routing where routing tables do not exist and the next hop is elected once the packet arrives, and (iii) source coding with LDPC (Low-Density Parity-Check) codes in order to face packet losses while minimizing energy consumption. We focus on two heterogeneous scenarios: bulk image transmission and infrequent events reporting. Modeling the average waiting delay of forwarders, we show that simple relay selection strategies are effective. We used 45 Arduino nodes communicating with IEEE 802.15.4 (XBee) within the large platform FIT IoT-LAB (IoT-LAB is part of the large platform FIT: Future Internet of Things). We implement and extensively study the behavior and performance of our proposal ODYSSE. We show that the three techniques fit perfectly, yielding a robust low complexity protocol for highly constrained nodes in typical IoT applications.

International audience; This paper addresses the problem of human action recognition from sequences of 3D skeleton data. For this purpose, we combine a deep learning network with geometric features extracted from data lie on a non-Euclidean space, which have been recently shown to be very effective to capture the geometric structure of the human pose. In particular, our approach claims to incorporate the intrinsic nature of the data characterized by Lie Group into deep neural networks and to learn more adequate geometric features for 3D action recognition problem. First, geometric features are extracted from 3D joints of skeleton sequences using the Lie group representation. Then, the network model is built from stacked units of 1-dimensional CNN across the temporal domain. Finally, CNN-features are then used to train an LSTM layer to model dependencies in the temporal domain, and to perform the action recognition. The experimental evaluation is performed on three public datasets containing various challenges: UT-Kinect, Florence 3D-Action and MSR-Action 3D. Results reveal that our approach achieves most of the state-of-the-art performance.

International audience; In this paper we tackle the problem of data analysis on high dimensional space of hyperspectral images. The remote sensing data analysis is a complex task due to many factors such as the large spectral and spatial variability. In fact, several approaches suffer from the pixel-mixed problem since mixed pixels are often sources of uncertainty and inaccuracy. Although spectral un-mixing techniques can provide abundance fractions for each class in mixed pixels, spatial distribution of these classes remains still unknown. Sub-pixel mapping techniques provide any solutions to the above-mentioned problem. Nevertheless, most of the proposed methods treat one particular type of objects because they assume that mixed pixels in a single image are identical. Sub-pixel mapping methods employing zonal objects are based on the spatial dependence assumption; the same assumption is used for linear objects while considering objects direction. Whereas, in case of encapsulated objects no spatial correlation is applied. As a result, each type of object requires special treatment, hence, the need for object recognition at sub-pixel scales. In this paper to improve sub-pixel object recognition accuracy in coarse spatial resolution image, we develop in this paper a spectro-spatial method based on a graph model, which discriminates between each type of sub-pixel object. The latter is classified into three categories: zonal, linear and small encapsulated objects based on their spatial information in neighboring pixels and their spectral information. In order to determine the suitability and the effectiveness of the proposed approach, experiments are performed with a simulated and a real hyperspectral image.

Wireless Body Area Networks are nowadays attracting both academic and industrial worlds. Combining collected data related to patient context with original health measurement can enhance the general health state monitoring and help to better understand the patient disease evolution. Daily activity is one of the important features that may influence the patient health state. Thus, recognizing the user activity can be a useful way for improving quality of health services. Relying on supervised learning, we study the feasibility of extracting and classifying the human activities from channel gain measures, which is an important feature that characterizes the WBAN channel links.

The attempts to reduce vehicle’s CO2 emissions and fuel consumption are actually motivating the manufacturers in the transport and automotive sectors to focus their interests on Electric Vehicle (EV) concept. Battery autonomy and charging process are still a big issue for that kind of vehicles. The evaluation of EV’s instantaneous energy consumption through realistic scenarios and based on consistent energy models are needed to take step forward on these issues. In this paper, we provide a deep insight on EV consumption dynamics and make a review of the energy model implemented in SUMO, one of the most popular traffic simulation framework used to evaluate vehicular technologies and strategies. We propose a set of energy consumption estimation improvements and show through simulation the achieved improvements in terms of accuracy compared to the sumo implementation.

International audience; The standard IEEE 802.11p was proposed to support the rapid exchange of data between the vehicles themselves and between the vehicles and the RSU (Road Side Unit). Besides, the Medium Access Control (MAC) protocols in Vehicular Ad-hoc Network (VANET), presented in the literature, have many weaknesses in supporting real time traffic. Indeed, some of them combine the advantage of TDMA with CSMA/CA to organize the channel access, therefore collisions may occur when safety messages are transmitted. In the context of hard real time, meeting deadlines requires prior knowledge of the possible communications between vehicles. Thus, we introduce an extension of the Enhanced Distributed Channel Access (EDCA) defined in IEEE 802.11p combined with TDMA, called Qos-aware Centralized Hybrid MAC protocol (QCHMAC). To evaluate our proposed protocol, we implemented it in ns-3 and we used the simulator SUMO to generate vehicular mobility traces. The simulation results reveal that QCH-MAC significantly outperforms pure TDMA in terms of transmission delay, packet loss rate and network throughput

International audience; Hyperspectral imagery is widely used for the identification and monitoring of earth surface, which in turn need good classification performances. However, the high spectral dimensionality of hyperspectral images degrades classification accuracy and increases computational complexity. To overcome these issues, dimensionality reduction has become an essential preprocessing step in order to enhance classifiers performances using hyperspectral images. Dimensionality reduction tackles the problem of the high dimensionality, but also the high correlation between the spectral bands of hyperspectral images. In this paper, we first review the main dimensionality reduction approaches and compare their performances when used for the classification task using the Support Vector Machines classifier. We also propose a combination of feature extraction and band selection for classification. We report the performances of all these methods using real hyperspectral images and show their efficiency for hyperspectral image classification.

International audience; Today's sensors are like eyes in the sky, thanks to the growth of satellite remote sensing technologies. Therefore, we see a steady evolution of the usage of different types of sensor, from airborne and satellites platforms which are generating large quantities of remote sensing image for divers applications such as; smart city, disaster management, military intelligence and others. As a result, the rate of growth in the amount of data by satellite is increasing dramatically. The velocity has exceeded 1TB per day and it will certainly increase in the future. However, it becomes crucial for these huge volume data to be stored. So, how to store and manage it efficiently becomes a real challenge because traditional ways have intensive issues; they are expensive and difficult to extend. Therefore, we need some scalable and parallel models for remote sensing data storage and processing. In this paper, we describe a scalable and distributed architecture for massive remote sensing data storage based on three No SQL databases (Apache Cassandra, Apache HBase, MongoBD). Also, a Hadoop-based framework is proposed to manage the big remote sensing data in a distributed and parallel manner.

International audience; Hyperspectral imagery (HSI) is widely used for several fields of remote sensing such as agriculture, land cover monitoring, and deforestation. However, the HSI classification is a challenge task due to the large number of spectral bands, unavailability of training samples, and the high correlation inter-bands. To address these challenges, we propose in this work a semantic reduction dimensionality approach based on the principal component analysis (PCA) and mutual information-based band selection (MI). Firstly, we project the original HSI using PCA to obtain a novel subspace with lower dimensions. Using the obtained components, a set of rules can be generated to find the relevant spectral bands based on score contribution coefficient. Moreover, the mutual information (MI) is used to select the spectral bands that contain a higher information based on the entropy criterion. We propose then to exploit the selected bands for HSI classification using SVM technique. Experiment results demonstrate that our proposed approach is effective and perform for HSI classification compared to other dimensionality reduction approaches.

International audience; Hybrid clouds are increasingly used to outsource non-critical applications to public clouds. However, the main challenge within such environments, is to ensure a cost-efficient distribution of the systems between the resources that are on/off premises. For Multi Agent Systems (MAS), this challenge is deepened due to irregular workload progress and intensive communication between the agents, which may result in high computing and data transfer costs. Thus, in this paper we propose a generic framework for adaptive cost-efficient deployment of MAS with a special focus on hybrid clouds. The framework is based mainly on the use of a performance evaluation process that consists of simulating various partitioning options to estimate and optimize the overall deployment costs. Further, to cope with the irregular workload changes within a MAS and dynamically adapt its initial deployment, we propose an extended version of the Fiduccia-Mattheyses algorithm (E-FM). The experimental results highlight the efficiency of E-FM and show that an efficient MAS deployment to hybrid clouds depends on various factors such as the cloud providers and their different cost-models, the network state, the used partitioning algorithm, and the initial deployment.

International audience; In this paper, we define a cluster based scheduling algorithm for Cognitive Radio Sensor Networks (CRSNs). To avoid inter-clusters collision, we assign fixed channels only to nodes having one-hop neighbors out of their clusters. We denote these nodes as specific nodes. Previous studies assign distinct channels to whole neighbor clusters to avoid inter-clusters collision. Our objective is to optimize the spatial reuse and to increase the network throughput while saving sensors energy. We start by assigning channels only to the specific nodes. Once the problem of inter-clusters collision is solved, each cluster head (CH) schedules the transmissions in its cluster independently. For the cluster members that are specific nodes, the CH assigns only time slots because the channel assignment is already done. For other cluster members (CMs) (not specific nodes), the CH assigns the pair (channel, slot). Two solutions are proposed in this paper to schedule the CMs: The Frame Intra Cluster Multichannel Scheduling algorithm denoted Frame-ICMS and the Slot Intra Cluster Multichannel Scheduling algorithm denoted Slot-ICMS. We evaluate the performance of these algorithms in case of accurate PUs activity detection and in case of bad PUs activity estimation. We prove that our proposals outperform an existing one especially in terms of energy saving.

International audience; Safety applications in Vehicular Ad-hoc Network (VANET) are used to reduce the number of accidents, which necessitates good Quality of Service (QoS), especially in some urgent cases and critical traffic. In fact, several Medium Access Control (MAC) protocols were proposed to provide high-performance for vehicular networks. For instance, the Contention-free MAC allows several vehicles to share the same communication channel. However, contention-based MAC protocols do not require any predefined schedule where each node competes for channel access when it needs to transmit data without any guarantee of success transmission. Hence, a hybrid MAC protocol that adopts Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA/CA) is required to enhance the network performance. Nevertheless, hybrid MAC protocols cannot ensure services differentiation so that critical messages and nonsecurity messages have the same priority. In this context and to overcome such limitation, this paper focuses on the design of a novel access method integrating TDMA and Enhanced Distributed Channel Access (EDCA) in unique architecture called Qos-aware Centralized Hybrid MAC protocol (QCH-MAC). To prove the efficiency of our proposal, the latter was implemented in ns-3 and the simulator SUMO to generate vehicular mobility traces. Therefore, experiments were performed to show the high performance of our system

Background The aim of this study was to review, systematically, evidence concerning the link between the ACTN3 R577X polymorphism and the rates and severity of non-contact injuries and exercise-induced muscle damage in athletes and individuals enrolled in exercise training programs. Methods A computerized literature search was performed in the electronic databases PubMed, Web of Science, and SPORTDiscus, from inception until November 2020. All included studies compared the epidemiological characteristics of non-contact injury between the different genotypes of the ACTN3 R577X polymorphism. Results Our search identified 492 records. After the screening of titles, abstracts, and full texts, 13 studies examining the association between the ACTN3 genotypes and the rate and severity of non-contact injury were included in the analysis. These studies were performed in 6 different countries (Spain, Japan, Brazil, China, Republic of Korea, and Italy) and involved a total participant pool of 1093 participants. Of the studies, 2 studies involved only women, 5 studies involved only men, and 6 studies involved both men and women. All the studies included were classified as high-quality studies (≥6 points in the Physiotherapy Evidence Database (PEDro) scale score). Overall, evidence suggests there is an association between the ACTN3 R577X genotype and non-contact injury in 12 investigations. Six studies observed a significant association between ACTN3 R577X polymorphism and exercise induced muscle damage: two with non-contact ankle injury, three with non-contact muscle injury, and one with overall non-contact injury. Conclusion The present findings support the premise that possessing the ACTN3 XX genotype may predispose athletes to a higher probability of some non-contact injuries, such as muscle injury, ankle sprains, and higher levels of exercise-induced muscle damage.

International audience; Named Data Networking (NDN) is an Information Centric Networks (ICN) solution that has recently attracted significant attention. NDN changes the Internet communication paradigm from the host-to-host IP model, to a name based communication model. In NDN, the requested Data can be retrieved from different sources and through multiple paths. This distinguished feature of NDN makes the traditional end-to-end congestion control schemes flagging with this new architecture. In this paper, we present a Hop-by-hop congestion control mechanism to regulate the Interest rate between the consumer and the congested router. Each NDN router continuously monitors its outgoing queues occupancy. When the queue size exceeds or falls below a specified threshold, an explicit notification is sent to downstream routers and consumers. The consumer and routers along the path then react by adjusting their Interest sending rates according to the available rate specified in the received notification. We prove the efficiency of the proposed solution and its ability to reduce the congestion impact and to maintain fairness per consumer. We highlight the advantages of our solution using different scenarios implemented in ndnSIM.

International audience; Information-Centric Networking (ICN) is an emerging network paradigm based on name-identified data objects and in-network caching. Therefore, ICN contents are distributed in a scalable and cost-efficient manner. With the rapid growth of IoT traffic, ICN is intended to be a suitable architecture to support IoT networks. In fact, ICN provides unique persistent naming, in-network caching and multicast communications which reduce the data producer load and the response latency. Using ICN in an IoT environment requires a study of caching policies in terms of cache placement strategies and cache replacement policies. To this end, we address, in this paper, caching challenges with the aim to identify which caching policies are suitable for IoT networks. Simulation findings show that the combination of the consumer-cache caching strategy and the RR cache replacement policy is the most convenient in IoT environments in terms of hop reduction ratio, server hit reduction and response latency.

This paper focuses on the topic identification for the Arabic language based on topic models. We study the Latent Dirichlet Allocation (LDA) as an unsupervised method for the Arabic topic identification. Thus, a deep study of LDA is carried out at two levels: Stemming process and the choice of LDA hyper-parameters. For the first level, we study the effect of different Arabic stemmers on LDA. For the second level, we focus on LDA hyper-parameters α and β and their impact on the topic identification. This study shows that LDA is an efficient method for Arabic topic identification especially with the right choice of hyper-parameters. Another important result is the high impact of the stemming algorithm on topic identification. Cet article met l'accent sur l'identification thématique pour la langue arabe basée sur les topic models. Nous étudions l'Allocation de Dirichlet Latente (LDA) comme une méthode non supervisée pour l'identification thématique. Ainsi, une étude approfondie de LDA a été effectuée à deux niveaux: le processus de lemmatisation et le choix des hyper-paramètres. Pour le premier niveau, nous étudions l'effet des différents lemmatiseurs sur LDA. Pour le deuxième niveau, nous nous focalisons sur les hyper-paramètres α et β de LDA et leurs impacts sur l'identification. Cette étude montre que LDA est une méthode efficace pour l'identification thématique Arabe surtout avec le bon choix des hyper-paramètres. Un autre résultat important est l'impact élevé de l'algorithme de lemmatisation sur l'identification thématique.

International audience; Traffic is one of the urban phenomena that have been attracting substantial interest in different scientific and industrial communities since many decades. Indeed, traffic congestions can have severe negative effects on people's safety, daily activities and quality of life, resulting into economical, environmental and health burden for both governments and organizations. Traffic monitoring has become a hot multidisciplinary research topic that aims to minimize traffic's negative effects by developing intelligent techniques for accurate traffic states' estimation, control and prediction. In this paper, we propose a novel algorithm for traffic state estimation from GPS data and using fuzzy switching linear models. The use of fuzzy switches allows the representation of intermediate traffic states, which provides more accurate traffic estimation compared to the traditional hard switching models, and consequently enables making better proactive and in-time decisions. The proposed algorithm has been tested on open traffic datasets collected in England, 2014. The results of the experiments are promising, with a maximum absolute relative error equal to 9.04%.

International audience; With the growing number of users and the huge amount of information in dynamic social networks, contextualizing community detection has been a challenging task. Thus, modeling these social networks is a key issue for the process of contextualized community detection. In this work, we propose a temporal multiplex information graph-based model to represent dynamic social networks: we consider simultaneously the social network dynamicity, its structure (different social connections) and various members’ profiles so as to calculate similarities between “nodes” in each specific context. Finally a comparative study on a real social network shows the efficiency of our approach and illustrates practical uses.

Workflows management systems (WfMS) are aimed for designing, scheduling, executing, reusing, and sharing workflows in distributed environments like the Cloud computing. With the emergence of e-science workflows, which are used in different domains like astronomy, life science, and physics, to model and execute vast series of dependents functionalities and a large amount of manipulated data, the workflow management systems are required to provide customizable programming environments to ease the programming effort required by scientists to orchestrate a computational science experiment. A key issue for e-science WfMS is how to deal with the change of the execution environment constraints and the variability and confliction of end users and cloud providers objectives for the execution of the same workflow or sub-workflow. They have to customize their management processes to insure the adaptability of the execution environment to the scientific workflows specificities, especially when dealing with large-scale (data, computing, I/O)-intensive workflows. In this paper, we propose a dynamically re-configurable framework for the deployment of scientific workflows in the Cloud (called DR-SWDF) that allows customizing the workflow deployment process according to a set of objectives and constraints of end users or cloud providers defined differently for the tasks or partitions of the same workflow. The DR-SWDF framework offers a K-means based algorithm that allows dynamically clustering the input workflows or sub-workflows in order to identify the most convenient techniques or algorithms to be applied for their scheduling and deployment in the cloud. The simulations results run on three examples large-scale scientific workflows show that our proposed framework can achieve better results than the use of a generic purpose approach.

In the Big Data era, companies are moving away from traditional data-warehouse solutions whereby expensive and time-consuming ETL (Extract-Transform-Load) processes are used, towards data lakes, which can be viewed as storage repositories holding a vast amount of raw data. In this paper, we position ourselves in the recurrent context where a user has a local dataset that is not sufficient for processing the queries that are of interest to him. In this context, we show how the data lake, or more specifically the service lake since we are focusing on data providing services, can be leveraged to enrich the local dataset with concepts that cater for the processing of user queries. Furthermore, we present the algorithms we have developed for this purpose and showcase the working of our solution using a study case.

International audience; Power conservation mechanisms allow the operator to meet the QoS requirements of its customers while offering mobility service for longer duration. Therefore, it is essential to maximize the battery life of WiMAX equipments. In this paper, we study the mechanisms of power conservation already proposed for the WiMAX network. We then propose an improved mechanism for energy conservation in the IEEE802.16e standard, taking into account the characteristics of the WiMAX network. Finally, we evaluate the various mechanisms of energy conservation in terms of energy consumption, mean delivery time and management throughput. We show through extensive simulations that our proposed mechanism retains more energy while providing a better mean delivery time and decreasing the management throughput.

International audience; Annotation of images is largely studied in the literature and used in many application fields such as in image interpretation, indexation and retrieval. Manually annotating images gives valuable information on the semantic content of images, but is no longer acceptable when dealing with real corpora of images, especially in the era of big data. Content-based approaches had known great success to deal with large datasets, using low-level features such as color, texture, and shape, which are easy to compute automatically. Nonetheless, they suffer from the well known semantic gap problem, since they produce semantically very limited representations of images. In this paper, we propose a semantic image annotation approach that simultaneously handles contextual, spatial and spectral information of the image. We consider a predefined remotely sensed ontology and develop an annotation process that produces semantically rich hypergraphs representing objects in scenes, as well as their spatial and spectral attributes. We apply our approach to build a hypergraph corresponding to the Jasper Ridge AVIRIS image, showing the promising use of such representation in remote sensing.