Summary of Topological Study of Chaotic CBC Mode of Operation

International audience—In cryptography, block ciphers are the most fundamental elements in many symmetric-key encryp-tion systems. The Cipher Block Chaining, denoted CBC, presents one of the most famous mode of operation that uses a block cipher to provide confidentiality or authenticity. In this research work, we intend to summarize our results that have been detailed in our previous series of articles. The goal of this series has been to obtain a complete topological study of the CBC block cipher mode of operation after proving his chaotic behavior according to the reputed definition of Devaney

Algorithmes pour l'estimation des données dans les réseaux de capteurs

International audienceLa collecte des données est un des enjeux majeurs dans les réseaux de capteurs. En effet, les communications induites par la transmission de données réduisent considérablement la durée de vie du réseau. Une des techniques utilisées pour réduire la quantité de données transférées est l'agrégation et selon le type des données étudiées, une des possibilités est l'utilisation de série temporelle ARMA. Dans cet article, nous proposons quatre algorithmes d'agrégation de données s'appuyant sur le modèle AR permettant ainsi la diminution de la consommation d'énergie dans les réseaux de capteurs et augmentant la durée de vie de ceux-ci

Investigating Data Similarity and Estimation Through Spatio-Temporal Correlation to Enhance Energy Efficiency in WSNs

International audienceWireless sensor networks are of energy-constrained nature, which calls for energy efficient protocols as a primary design goal. Thus, minimizing energy consumption is a main challenge.We are concerned in howcollected data by sensors, can be processed to increase the relevance of certain mass of data and reduce the overall data traffic. Since sensor nodes are often densely deployed, the data collected by nearby nodes are either redundant or correlated. One of the great challenges for the aforementioned problem is to exploit temporal and spatial correlation among the source nodes. Our work is composed of two main tasks: 1- A predictive modeling task that aims to capture the temporal correlation among collected data. 2- A data similarity detection task that measures the data similarity based on the spatial correlation

Une approche générique pour le développement des applications web

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Energy Efficient Data Collection in Periodic Sensor Networks Using Spatio-Temporal Node Correlation

International audienceIn wireless sensor networks (WSNs), the densely deployment and the dynamic phenomenon provide strong correlation between sensor nodes. This correlation is typically spatio-temporal. This paper proposes an efficient data collection technique, based onspatio-temporal correlation between sensor data, aiming to extend the network lifetime in periodic WSN applications. In the first step, our technique proposes a new model based on an adapted version of Euclidean distance which searches, in addition to the spatialcorrelation, the temporal correlation between neighboring nodes. Based on this correlation and in a second step, a subset of sensors are selected for collecting and transmitting data based on a sleep/active algorithm. Our proposed technique is validated via experiments on real sensor data readings. Compared to other existing techniques, the results show the effectiveness of our technique in terms of reducing energy consumption and extending network lifetime while maintaining the coverage of the monitored area

Comparison of Different Data Aggregation Techniques in Distributed Sensor Networks

International audienceWireless sensor networks (WSNs) are almost everywhere, they are exploited for thousands of applications in a densely distributed manner. Such deployment makes WSNs one of the highly anticipated key contributors of the big data nowadays. Hence, data aggregation is attracting much attention from researchers as efficient way to reduce the huge volume of data generated in WSNs by eliminating the redundancy among sensing data. In this paper, we propose an efficient data aggregation technique for clustering-based periodic wireless sensor networks. Further to a local aggregation at sensor node level, our technique allows cluster-head (CH) to eliminate redundant data sets generated by neighboring nodes by applying three data aggregation methods. These proposed methods are based on the sets similarity functions, the one-way Anova model with statistical tests and the distance functions respectively. Based on real sensor data, we have analyzed their performances according to the energy consumption and the data latency and accuracy, and we show how these methods can significantly improve the performance of sensor networks

Energy efficient filtering techniques for data aggregation in sensor networks

International audienceMinimizing latency is a major issue for data aggregation in wireless sensor networks (WSNs). Hence, the proposed algorithms must achieve the minimum delay in data delivery while decreasing the energy consumption. In this paper, we propose a new version of the prefix frequency filtering technique (PFF) proposed by [1], which aims to minimize aggregation latency. PFF finds similar sets of data generated by nodes in order to reduce redundancy in data over the network, thus, nodes consume less energy. While in the enhanced version of the PFF technique, called PPSFF, we propose a positional filtering that exploits the order of readings both in the prefix and the suffix of a set and leads to upper bound estimations of similarity scores. Experiments on real sensor data show that our enhancement can significantly improve the latency of the PFF technique without affecting its performance

A Complete Security Framework for Wireless Sensor Networks: Theory and Practice

International audienceWireless sensor networks are often deployed in public or otherwise untrusted and even hostile environments, which prompt a number of security issues. Although security is a necessity in other types of networks, it is much more so in sensor networks due to the resource-constraint, susceptibility to physical capture, and wireless nature. Till now, most of the security approaches proposed for sensor networks present single solution for particular and single problem. Therefore, to address the special security needs of sensor networks as a whole we introduce a security framework. In their framework, the authors emphasize the following areas: (1) secure communication infrastructure, (2) secure scheduling, and (3) a secure data aggregation algorithm. Due to resource constraints, specific strategies are often necessary to preserve the network's lifetime and its quality of service. For instance, to reduce communication costs, data can be aggregated through the network, or nodes can go to sleep mode periodically (nodes scheduling). These strategies must be proven as secure, but protocols used to guarantee this security must be compatible with the resource preservation requirement. To achieve this goal, secure communications in such networks will be defined, together with the notions of secure scheduling and secure aggregation. The concepts of indistinguability, nonmalleability, and message detection resistance will thus be adapted to communications in wireless sensor networks. Finally, some of these security properties will be evaluated in concrete case studies

Amélioration de la qualité d'agrégation par l'analyse de données dans les réseaux de capteurs

L'adoption des réseaux de capteurs sans fil (WSNs) dans divers secteurs continuent à croître, comme la médecine, la domotique, le contrôle de processus industriels, la localisation des objets, etc. Cela revient à l'émergence de capteurs de plus en plus petits et de plus en plus intelligents dans notre vie quotidienne. Ces dispositifs interagissent avec l'environnement ou d'autres périphériques, pour analyser les données et produire de l'information. En plus de créer de l'information, ils permettent, une intégration transparente de la technologie virtuelle autour de nous. En effet, ces objets sont de faible puissance et fonctionnent sur batterie. Ils sont souvent utilisé dans des zones géographiques dangereuse et peu accessible, tels que les volcans actifs, les champs de bataille, ou après une catastrophe naturelle etc. Ces zones critiques rendent le remplacement ou la recharge des batteries de chaque capteur difficile voire impossible. Ainsi, leur consommation énergétique devient le principale verrou technologique empêchant leur déploiement à grande échelle. Nous sommes intéressés à partie la plus consommatrice d'énergie dans les réseaux de capteurs: la communication ou l'envoi/la réception de données. Nous proposons des méthodes pour réduire les transmissions des nœuds en réduisant le volume de données à transmettre. Notre travail s'articule autour de trois axes fondamentaux: la prédiction des données, la détection de similarité des données et la détection des comportements anormaux.The promise and application domain of Wireless Sensor Networks (WSNs) continue to grow such as health care, home automation, industry process control, object tracking, etc. This is due to the emergence of embedded, small and intelligent sensor devices in our everyday life. These devices are getting smarter with their capability to interact with the environment or other devices, to analyze data and to make decisions. They have made it possible not only gather data from the environment, but also to bridge the physical and virtual worlds, assist people in their activities, while achieving transparent integration of the wireless technology around us. Along with this promising glory for WSNs, there are however, several challenges facing their deployments and functionality, especially for battery-operated sensor networks. For these networks, the power consumption is the most important challenge. In fact, most of WSNs are composed of low-power, battery-operated sensor nodes that are expected to replace human activities in many critical places, such as disaster relief terrains, active volcanoes, battlefields, difficult terrain border lands, etc. This makes their battery replacement or recharging a non-trivial task. We are concerned with the most energy consuming part of these networks, that is the communication. We propose methods to reduce the cost of transmission in energy-constrained sensor nodes. For this purpose, we observe the way data is collected and processed to save energy during transmission. Our work is build on three basic axis: data estimation, data similarity detection and abnormal behaviors detection.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

Tree-based data aggregation approach in periodic sensor networks using correlation matrix and polynomial regression

International audienceSensor networks are a collection of sensor nodes that co-operatively transmit sensed data to a base station. One of the well-known characteristics of Wireless Sensor Networks (WSNs) is its limited resources. Energy consumption of the network's nodes is considered one of the major challenges faced by researchers nowadays. On the other hand, data aggregation helps in reducing the redundant data transferred through the WSNs. This fact implies that aggregation of data is considered a very crucial technique for reducing the energy consumption across the WSN. Local aggregation and Prefix filtering are two methods used in which they utilize a tree based bi-level periodic data aggregation approach implemented on the source node and on the aggregator levels. In this paper an efficient model for multivariate data reduction is proposed based on periodic data aggregation on two sensor levels, in addition to polynomial regression functions. The performance of the model was evaluated using SensorScope network which is deployed at the Grand-St-Bernard located between Switzerland and Italy. The results show the advantages of the proposed model as it allows 84% reduction rate and 93% approximation accuracy after reduction. The simulations were done using the R software