Frequency Filtering Approach for Data Aggregation in Periodic Sensor Networks

International audienceThis paper presents an energy-efficient technique for data aggregation in periodic sensor networks. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We provide a frequency filtering approach to solve this problem. Our experiments demonstrate that our algorithm outperforms existing prefix filtering methods in reducing energy consumption

An Optimized In-Network Aggregation Scheme for Data Collection in Periodic Sensor Networks

International audienceIn-network data aggregation is considered an effective technique for conserving energy communication in wireless sensor networks. It consists in eliminating the inherent redundancy in raw data collected from the sensor nodes. Prior works on data aggregation protocols have focused on the measurement data redundancy. In this paper, our goal in addition of reducing measures redundancy is to identify near duplicate nodes that generate similar data sets. We consider a tree based bi-level periodic data aggregation approach implemented on the source node and on the aggregator levels. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We propose a new frequency filtering approach and several optimizations using sets similarity functions to solve this problem. To evaluate the performance of the proposed filtering method, experiments on real sensor data have been conducted. The obtained results show that our approach offers significant data reduction by eliminating in network redundancy and out-performs existing filtering techniques

Novel Order preserving encryption Scheme for Wireless Sensor Networks

International audienceAn Order-Preserving Encryption (OPE) scheme is a deterministic cipher scheme, whose encryption algorithm produces cipher texts that preserve the numerical ordering of the plain-texts. It is based on strictly increasing functions. It is a kind of homomorphic encryption where the homomorphic operation is order comparison. This means that comparing encrypted data provides the exact result than comparing the original data. It is attractive to be used in databases, especially in cloud ones as a method to enhance security, since it allows applications to perform order queries over encrypted data efficiently (without the need of decrypting the data). Wireless sensor network is another potential domain in which order preserving encryption can be adopted and used with high impact. It can be integrated with secure data aggregation protocols that use comparison operations to aggregate data (MAX, MIN, etc.) in a way that no decryption is being performed on the sensor nodes, which means directly less power consumption. In this paper, we will review many existing order-preserving encryption schemes with their related brief explanation, efficiency level, and security. Then, and based on the comparative table generated, we will propose a novel order-preserving encryption scheme that has a good efficiency level and less complexity, in order to be used in a wireless sensor network with an enhanced level of security

Gestion de données volumineuses dans les réseaux de capteurs périodiques

This thesis proposes novel big data management techniques for periodic sensor networksembracing the limitations imposed by wsn and the nature of sensor data. First, we proposed anadaptive sampling approach for periodic data collection allowing each sensor node to adapt itssampling rates to the physical changing dynamics. It is based on the dependence of conditionalvariance of measurements over time. Then, we propose a multiple level activity model that usesbehavioral functions modeled by modified Bezier curves to define application classes and allowfor sampling adaptive rate. Moving forward, we shift gears to address the periodic dataaggregation on the level of sensor node data. For this purpose, we introduced two tree-based bilevelperiodic data aggregation techniques for periodic sensor networks. The first one look on aperiodic basis at each data measured at the first tier then, clean it periodically while conservingthe number of occurrences of each measure captured. Secondly, data aggregation is performedbetween groups of nodes on the level of the aggregator while preserving the quality of theinformation. We proposed a new data aggregation approach aiming to identify near duplicatenodes that generate similar sets of collected data in periodic applications. We suggested the prefixfiltering approach to optimize the computation of similarity values and we defined a new filteringtechnique based on the quality of information to overcome the data latency challenge. Last butnot least, we propose a new data mining method depending on the existing K-means clusteringalgorithm to mine the aggregated data and overcome the high computational cost. We developeda new multilevel optimized version of « k-means » based on prefix filtering technique. At the end,all the proposed approaches for data management in periodic sensor networks are validatedthrough simulation results based on real data generated by periodic wireless sensor network.Les recherches présentées dans ce mémoire s’inscrivent dans le cadre des réseaux decapteurs périodiques. Elles portent sur l’étude et la mise en oeuvre d’algorithmes et de protocolesdistribués dédiés à la gestion de données volumineuses, en particulier : la collecte, l’agrégation etla fouille de données. L’approche de la collecte de données permet à chaque noeud d’adapter sontaux d’échantillonnage à l’évolution dynamique de l’environnement. Par ce modèle le suréchantillonnageest réduit et par conséquent la quantité d’énergie consommée. Elle est basée surl’étude de la dépendance de la variance de mesures captées pendant une même période voirpendant plusieurs périodes différentes. Ensuite, pour sauvegarder plus de l’énergie, un modèled’adpatation de vitesse de collecte de données est étudié. Ce modèle est basé sur les courbes debézier en tenant compte des exigences des applications. Dans un second lieu, nous étudions unetechnique pour la réduction de la taille de données massive qui est l’agrégation de données. Lebut est d’identifier tous les noeuds voisins qui génèrent des séries de données similaires. Cetteméthode est basée sur les fonctions de similarité entre les ensembles de mesures et un modèle defiltrage par fréquence. La troisième partie est consacrée à la fouille de données. Nous proposonsune adaptation de l’approche k-means clustering pour classifier les données en clusters similaires,d’une manière à l’appliquer juste sur les préfixes des séries de mesures au lieu de l’appliquer auxséries complètes. Enfin, toutes les approches proposées ont fait l’objet d’études de performancesapprofondies au travers de simulation (OMNeT++) et comparées aux approches existantes dans lalittérature

Data Aggregation for Periodic Sensor Networks Using Sets Similarity Functions

International audienceEnergy is a major constraint in wireless sensor networks. Data Aggregation constitutes a fundamental mechanism for energy optimization. The idea is to minimize redundancy from the raw data captured by the sensors, minimizing the number of transmissions to the sink and thus saving energy. Since the data is often captured on a periodic basis, and sensor nodes detect common phenomena, a periodic based protocol that manages collected data sets can help to preserve the scarce energy. This paper proposes a new filtering technique for identifying duplicate sets of periodically captured data. We suggest a data aggregation model based on set joins similarity functions that conserves data integration while eliminating inherited redundancy. We show through the result that our approach offers significant data reduction by eliminating in-network redundancy and sending only necessary information to the sink

A Two Tiers Data Aggregation Scheme for Periodic Sensor Networks

International audienceThe expected lifetime of any wireless sensor network is a critical issue since sensor nodes are powered by small batteries. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is regarded as an effective technique to reduce energy consumption and prevent congestion. This paper objective is to identify near duplicate nodes that generate similar sets of collected data in periodic applications. We propose a new prefix filtering approach that avoids computing similarity values for all possible pairs of sets. We define a new filtering technique based on the quality of information. To the best of our knowledge, the proposed algorithm is a pioneer in using "sets similarity functions" for data aggregation in sensor networks. To evaluate the performance of the proposed method, experiments on real and synthetic sensor data have been conducted. The analysis and the results show the effectiveness of our method dedicated to sensor networks

Energy Efficient in-Sensor Data Cleaning for Mining Frequent Itemsets

International audienceLimited energy, storage, computational power represent the main constraint of sensor networks. Development of algorithms that take into consideration this extremely demanding and constrained environment of sensor networks became a major challenge. Communicating messages over a sensor network consume far more energy than processing it and mining sensors data should respect the characteristics of sensor networks in terms of energy and computation constraints, network dynamics, and faults. This lead us to think of a data cleaning pre processing phase to reduce the packet size transmitted and prepare the data for an efficient and scalable data mining. This paper introduces a tree-based bi-level periodic data cleaning approach implemented on both the source node and the aggregator levels. Our contribution in this paper is two folds. First we look on a periodic basis at eachdata measured and periodically clean it while taking into consideration the number of occurrences of the measures captured which we shall call weight. Then, a data cleaning is performed between groups of nodes on the level of the aggregator, which contains lists of measures along with their weights. The quality of the information should be preserved during the in-network transmission through the weight of each measure captured by the sensors. This weight will constitute the key optimization of the frequent pattern tree. The result set will constitute a perfect training set to mine without higher CPU consumption allowing us to send only the useful information to the sink. The experimental results show the effectiveness of this technique in terms of energy efficiency and quality of the information by focusing on a periodical data cleaning while taking into consideration the weight of the data captured

An Aggregation and Transmission Protocol for Conserving Energy in Periodic Sensor Networks

International audienceIn wireless sensor networks (WSNs), redundant collected measures and the resulting redundant packets to sendto the sink are likely to happen repeatedly. As transmission is an expensive issue in term of energy, eliminating data redundancy and reducing communication load can minimize energy consumption and extend the whole network lifetime. In this paper, we propose an adaptive protocol composed of two phases, called aggregation and transmission protocol (ATP), that operates on each sensor node separately in order to reduce its data transmission and to save energy. We consider a cluster-based scheme in which data is sent periodically from sensor nodes to their appropriate Cluster-Heads (CHs). The proposed protocol searches, during aggregation phase, similarities between data captured during a period p in order to eliminate redundancy from raw data. While during transmission phase, sensor node searches periodic correlation of data, using one way ANOVA model and Fisher test. The proposed protocol was successfully tested on real sensor data. The obtained results show that ATP can significantly minimize energy consumption, comparing to other existing data aggregation techniques, without affecting the quality of data

Tree-based data aggregation approach in wireless sensor network using fitting functions

Sensor 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 (WSN) 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 WSN. 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 our goal is to apply data aggregation on two nodes levels. We worked on sending fewer data from aggregator to the sink, along with the equation that expresses all data. We applied Bayesian belief network algorithm to measure the accuracy of this method