La sécurité des IOT frameworks

International audienceLes objets connectés IOT sont des nouvelles technologies utilisées pour connecter les objets à internet et aux utilisateurs. L'IOT influence notre quotidien dans plusieurs domaines comme la logistique, la santé, l'énergie, les véhicules intelligents, la maison intelligente ou la ville intelligente... Par la suite, suite aux spécificités des caractéristiques des différents domaines d'usages, une multitude d'applications IOT ont été développées et déployés en utilisant des différents IOT frameworks. Un IOT framework est un ensemble de règles, protocoles et standards qui simplifient l'implémentation des applications IOT. Le bon fonctionnement et le succès de ces applications dépend des caractéristiques du framework, et aussi des mécanismes de sécurité utilisés.Dans cet article, nous allons discuter des frameworks utilisés en présentant l'architecture proposée de chaque framework, les hardwares et les softwares compatibles et les mécanismes de sécurité utilisés, ensuite une étude comparative de ces frameworks sera présentée. Enfin, nous allons proposer une architecture d'un IOT framework sécurisé

A New Practical Approach to Automatically Generate the Trending Topics in Morroccan Society using the Social Network Twitter

Social networks are taking an increasingly important place in the field of communication within our society. The most used are Twitter, Facebook, Instagram, Tumblr, Dribble, LinkedIn, and Google+. Twitter is a popular social network where connected users can publish short messages limited to 140 characters called “tweets” in which users can share thoughts, post links or images. Twitter has gained wide popularity in Arab world and especially Morocco due to its simplicity of use and services offered by its platform, this information revolution in our society leads to an accumulation of a vast quantity of data that may contain a lot of valuable information. Analyzing these tweets of Moroccan users come with challenges because Moroccan users use a variety of languages and dialects, such as Standard Arabic, Moroccan Arabic called “Darija”, Moroccan Amazigh dialect called “Tamazight”, French, English and more. In addition, the tweets of Moroccan users contain a lot of abbreviations, #hashtags, URLs, spelling mistakes, reduced syntactic structures, and many abbreviations. In this paper, we propose a new approach to determine, from the data sent on Twitter, the subjects that interest Moroccan society and then locate on the Moroccan map the areas from where come the tweets related to these topics. Our proposed approach is based on a distributed system, which contains four main components: the Hadoop framework, the natural language processing, the clustering algorithm k-means, and a tool for plotting tweets graphically on Moroccan map. The first task of this system is to automatically extract the tweets. Next, it stores them in a distributed file system using HDFS (Hadoop Distributed File System) of Apache Hadoop framework. Then we process this raw data and analyze it by using a distributed program using MapReduce of Hadoop framework, Python language, and Natural Language Processing (NLP) techniques. Afterward, we use a text mining technique, called TF-IDF (Term Frequency-Inverse Document Frequency), to convert the corpus generated by the previous step into a vector representation, where each dimension of the vector corresponds to a word, and then we implement the kmeans algorithm to cluster all words into topics. Finally, we graphically plot the topics on the Moroccan map by using the coordinates extracted from tweets, in order to discover the relation between the discovered topics and located Moroccan area

Etude et developpement d'un simulateur symbolique comportemental de circuits digitaux

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

Hierarchical Protocol Based on Recursive Clusters for Smart Parking Applications Using Internet of Things (IOT)

With the increasing number of vehicles, the management of parking spaces in cities is becoming increasingly important in improving the quality of life and combating air pollution. Indeed, finding a parking space at peak times and in congested areas of the population becomes a huge challenge for drivers. To remedy this problem, most modern cities have smart parking. The equipment of these smart parking is mainly based on the implementation of wireless sensor networks (WSN) to monitor, track, and collect real-time information on the occupancy status of each parking space. This information is then made available to drivers who are looking for an available parking space. However, sensor nodes have limitations in terms of energy and communication that affect the performance and quality of the wireless sensor network. Therefore, the design of a self-organization protocol for WSN that minimizes power consumption and maximizes the longevity of the WSN network must be taken into account when implementing and developing a sustainable and viable intelligent parking system. In this paper, we propose a protocol for self-organization of wireless sensor networks (WSN) for the management of parking spaces in outdoor and urban car parks. This protocol is based on building clusters using ZigBee transmission technology for multihop communication. Each sensor node will be installed in the ground of each parking space to monitor its availability by sending the empty or busy state of that space to the gateway using cluster head nodes (CHs). This approach has a robust and efficient self-organizing algorithm that minimizes energy dissipation and increases the lifetime of sensor nodes and the WSN network. The simulation results show that parking management systems in outdoor and urban car parks using the self-organization protocol presented are efficient and sustainable in terms of energy consumption, reliability of data transmission, and the longevity of the WSN network compared to other existing parking systems that use different self-organizing protocols for wireless sensor networks

Designing and Managing a Smart Parking System Using Wireless Sensor Networks

For several years, the population of cities has continued to multiply at a rapid pace. The main cause of this phenomenon in developing countries like Morocco is the rural exodus. In fact, rural youth are increasingly attracted by the modern way of life and the opportunities of employment offered by cities. This increase in population density has a large number of negative effects on the quality of life in the city. The most obvious is the intensity of the traffic, which has become an almost insurmountable problem and which causes a great deal of damage, such as the increase in the number of accidents that cause serious bodily harm to the road users, the pollution caused by the large amount of CO2 released by the vehicles, and the continuous stress of drivers who must drive in often narrow and very busy roads and who must look for a long time to find a space to park. Thus, to solve the parking problem, several modern technologies have been created to equip car parks with smart devices that help road users identify the nearest car park that has a free space. These technologies most often use wireless sensor networks and Internet of Things (IoT) technology. In this paper, we present the design and development of a smart parking system using the latest technologies based on wireless sensor networks (WSN). Our system uses an adaptable and hybrid self-organization algorithm for wireless sensor networks that adapts to all types of car parks existing in the city (linear and mass parking), and offers a better management of the energy consumption during the wireless communication to increase the lifetime of the sensor nodes and the longevity of the WSN. This system also offers innovative services which facilitate the task to the drivers when looking for an available parking space in the city near their destination, in a fast and efficient manner

Automated Real-Time Intelligent Traffic Control System for Smart Cities Using Wireless Sensor Networks

Over the years, the number of vehicles has increased dramatically, which has led to serious problems such as traffic jams, accidents, and many other problems, as cities turn into smart cities. In recent years, traffic jams have become one of the main challenges for engineers and designers to create an intelligent traffic management system capable of effectively detecting and reducing the overall density of traffic in most urban areas visited by motorists such as offices, downtown, and establishments based on several modern technologies, including wireless sensor networks (WSNs), surveillance camera, and IoT. In this article, we propose an intelligent traffic control system based on the design of a wireless sensor network (WSN) in order to collect data on road traffic and also on available parking spaces in a smart city. In addition, the proposed system has innovative services that allow drivers to view the traffic rate and the number of available parking spaces to their destination remotely using an Android mobile application to avoid traffic jams and to take another alternative route to avoid getting stuck and also to make it easier for drivers when looking for a free parking space to avoid unnecessary trips. Our system integrates three smart subsystems connected to each other (crossroad management, parking space management, and a mobile application) in order to connect citizens to a smart city

Nature, stockage et traitements des Big Data dans le domaine de la santé

International audienceActuellement, le traitement et l'analyse des big data dans les différents secteurs et domaines constitue un avantage majeur pour leurs développement et amélioration, notamment, le domaine de la santé pour lequel les études et recherches ont montré l'utilité et la grande valeur ajoutée des big data. En effet, l'exploitation des données massives du secteur médical ainsi que l'utilisation des algorithmes de machines Learning ont permis d'améliorer la qualité des services de soins et d'augmenter la précision des traitements médicaux et ils ont donné aussi la possibilité de prédire l'état de santé des patients. Dans notre article nous allons tout d'abord définir et préciser la notion des big data dans le domaine de santé, puis nous allons mettre le point sur les différents types de bases de données utilisées pour le stockage et le traitement des données tout en citant les avantages et inconvénients de chaque type. Nous allons par la suite étudier les techniques et algorithmes de classifications qui permettent le classement et la catégorisation des données médicales. Après, nous allons décrire les architectures big data qui ont été utilisés dans le secteur de santé, et nous allons conclure avec les challenges et les défis rencontrés

Using AraGPT and ensemble deep learning model for sentiment analysis on Arabic imbalanced dataset

With the fast growth of mobile technology, social media has become important for people to share their thoughts and feelings. Businesses and governments can make better strategic decisions when they know what the public thinks. Because of this, sentiment analysis is an important tool for figuring out how different people’s opinions are. This article presents a deeplearning ensemble model for sentiment analysis. The ensemble model proposed consists of three deep-learning models, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), as base classifiers. AraBERT is responsible for presenting the textual input data into representative embeddings. The stacking ensemble model then captures the long-range dependencies in the embedding for a given class. As a meta-classifier, Support Vector Machine (SVM) then combines the predictions made by the stacking deep learning model. In addition, data augmentation with AraGPT was implemented to address the imbalanced dataset issues. The experimental results demonstrate that the proposed model outperforms the state-of-the-art models with an accuracy of 88.89%, 90.88%, and 88.23% on the HARD, BRAD, and Twitter datasets, respectively

Using AraGPT and ensemble deep learning model for sentiment analysis on Arabic imbalanced dataset

With the fast growth of mobile technology, social media has become important for people to share their thoughts and feelings. Businesses and governments can make better strategic decisions when they know what the public thinks. Because of this, sentiment analysis is an important tool for figuring out how different people’s opinions are. This article presents a deeplearning ensemble model for sentiment analysis. The ensemble model proposed consists of three deep-learning models, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), as base classifiers. AraBERT is responsible for presenting the textual input data into representative embeddings. The stacking ensemble model then captures the long-range dependencies in the embedding for a given class. As a meta-classifier, Support Vector Machine (SVM) then combines the predictions made by the stacking deep learning model. In addition, data augmentation with AraGPT was implemented to address the imbalanced dataset issues. The experimental results demonstrate that the proposed model outperforms the state-of-the-art models with an accuracy of 88.89%, 90.88%, and 88.23% on the HARD, BRAD, and Twitter datasets, respectively