Efficient and Robust Secure Aggregation of Encrypted Data in Sensor Networks

International audienceWireless sensor networks are now in widespread use to monitor regions, detect events and acquire information. To reduce the amount of sending data, an aggregation approach can be applied along the path from sensors to the sink. However, usually the carried information contains confidential data. Therefore, an end-to-end secure aggregation approach is required to ensure a healthy data reception. End-to-end encryption schemes that support operations over cypher-text have been proved important for private party sensor network implementations. Unfortunately, nowadays these methods are very complex and not suitable for sensor nodes having limited resources. In this paper, we propose a secure end-to-end encrypted-data aggregation scheme. It is based on elliptic curve cryptography that exploits a smaller key size. Additionally, it allows the use of higher number of operations on cypher-texts and prevents the distinction between two identical texts from their cryptograms. These properties permit to our approach to achieve higher security levels than existing cryptosystems in sensor networks. Our experiments show that our proposed secure aggregation method significantly reduces computation and communication overhead and can be practically implemented in on-the-shelf sensor platforms. By using homomorphic encryption on elliptic curves, we thus have realized an efficient and secure data aggregation in sensor networks

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

Low Cost Monitoring and Intruders Detection using Wireless Video Sensor Networks

International audienceThere is a growing interest in the use of video sensor networks in surveillance applications in order to detect intruders with low cost. The essential concern of such networks is whether or not a specified target can pass or intrude the monitored region without being detected. This concern forms a serious challenge to wireless video sensor networks of weak computation and battery power. In this paper, our aim is to prolong the whole network lifetime while fulfilling the surveillance application needs. We present a novel scheduling algorithm where only a subset of video nodes contribute significantly to detect intruders and prevent malicious attacker to predict the behavior of the network prior to intrusion. Our approach is chaos-based, where every node based on its last detection, a hash value and some pseudo-random numbers easily computes a decision function to go to sleep or active mode. We validate the efficiency of our approach through theoretical analysis and demonstrate the benefits of our scheduling algorithm by simulations. Results show that in addition of being able to increase the whole network lifetime and to present comparable results against random attacks (low stealth time), our scheme is also able to withstand malicious attacks due to its fully unpredictable behavior

Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation

Interpreting the performance results of models that attempt to realize user behavior in platforms that employ recommenders is a big challenge that researchers and practitioners continue to face. Although current evaluation tools possess the capacity to provide solid general overview of a system's performance, they still lack consistency and effectiveness in their use as evident in most recent studies on the topic. Current traditional assessment techniques tend to fail to detect variations that could occur on smaller subsets of the data and lack the ability to explain how such variations affect the overall performance. In this article, we focus on the concept of data clustering for evaluation in recommenders and apply a neighborhood assessment method for the datasets of recommender system applications. This new method, named neighborhood-based evaluation, aids in better understanding critical performance variations in more compact subsets of the system to help spot weaknesses where such variations generally go unnoticed with conventional metrics and are typically averaged out. This new modular evaluation layer complements the existing assessment mechanisms and provides the possibility of several applications to the recommender ecosystem such as model evolution tests, fraud/attack detection and a possibility for hosting a hybrid model setup

Fault Tolerance Technique Using Bidirectional Hetero-Associative Memory for Self-Reconfigurable Programmable Matter

International audienceProgrammable Matter (PM) based on modular robots is a material which can be reprogrammed to have different shapes and to change its physical properties on demand. It can be deployed in several domains and has a variety of applications in construction, surgery, environmental science, space exploration, etc. PM is composed of a big number of limited resources connected robots called modules or particles to form its shape. These modules communicate with each other and move around each other dynamically in order to switch from one configuration to another. Due to the limited resources of modules and the high number of packets that transit within the system, it is very challenging to ensure packet delivery with high reliability. In this paper, we are using a Bidirectional Hetero-Associative Memory (BHAM) networks to improve the reliability and fault tolerance in PM. The idea is to let modules sending packets with smaller size without loosing any information. Furthermore, this model is also capable to remove noise from received packets. The proposed approach is tested on a real programmable matter blinky blocks platform as well as via simulations. We studied two versions of artificial neural networks based on storage capacity. The experimental results show that the studied approach is efficient in reducing the size of packets that transit in the system thus reducing energy consumption and it is capable to detect and remove noise and correct noisy packets

Energy Efficient Sensor Data Collection Approach for Industrial Process Monitoring

International audienceThe use of wireless sensor network (WSN) for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, while the second one is based on the sets similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixedrate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing approaches

Adaptive data collection approach for periodic sensor networks

International audienceData collection from unreachable terrain and then transmit the information to the sink is a fundamental task in periodic sensor networks. Energy is a major constraint for this network as the only source of energy is a battery with limited lifetime. Therefore, in order to keep the networks operating for long time, adaptive sampling approach to periodic data collection constitutes a fundamental mechanism for energy optimization. The key idea behind this approach is to allow each sensor node to adapt its sampling rates to the physical changing dynamics. In this way, over-sampling can be minimised and power efficiency of the overall network system can be further improved. In this paper, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. The proposed method was successfully tested in a real sensor data set