Abdallah Makhoul

Performance study of multiple cover-set strategies for mission-critical video surveillance with wireless video sensors

A Wireless Video Sensor Network (WVSN) consists of a set of sensor nodes equipped with miniaturized video cameras. Unlike omni-directional sensors, the sensing region of a video node is limited to the field of view of its camera. In this paper, we study the problem of coverage by video sensors in randomly deployed WVSN. We focus on the performance of various fast cover set construction strategies for enabling efficient scheduling of nodes in mission-critical surveillance applications. Simulation results shows the performance of the various strategies in terms of percentage of coverage, network lifetime, intrusion stealth time and number of intrusion detection.

Hilbert mobile beacon for localisation and coverage in sensor networks

Wireless sensor networks (WSN) constitute a major area of research developing at a very fast pace. Target localisation and coverage are core issues in the field of WSN and represent constraints that affect the effectiveness of WSN. This article focuses on localisation and coverage and identifies a relationship of dependence between the two issues. Throughout this article, a localisation algorithm is proposed and also an energy efficient approach that aims to preserve coverage. The use of a mobile beacon is suggested to divide the region of interest into unit squares following the same method used in the Hilbert space filling curve. A proper choice of the order of the Hilbert curve (i.e. the region subdivisions) is studied to guarantee the localisation of all nodes as well as the total area of coverage. The mobile beacon assists to determine the physical location of undetected nodes by sending beacon packets while traversing the region of interest. It also locally derives an activity scheduling between nodes with a relatively low cost in terms of energy spent by nodes compared to other approaches. In order to validate the effectiveness of the above proposed approach, a series of experiments have been conducted and will be mentioned throughout this article.

Localization and coverage for high density sensor networks

This paper tackles the problems of localization and coverage in randomly deployed high density sensor networks. In particular, it presents a novel and integrated approach that performs at once localization and coverage. We introduce here an approach based on a single mobile beacon aware of its position. Sensor nodes receiving beacon packets will be able to locate themselves. On the other hand, We exploit the localization phase to construct sets of active nodes that ensure as much as possible the zone coverage. In our approach the mobile beacon follows a Hilbert curve. The results of experiments conducted using the discrete event simulator Omnet++, are discussed, they allow us to justify our approach and to compare it to existing ones.

Energy-efficient data aggregation and transfer in periodic sensor networks

Limited battery power and high transmission energy consumption in wireless sensor networks make in-network aggregation and prediction a challenging area for researchers. The most energy consumable operation is transmitting data by a sensor node, comparing it with the energy consumption of in-network computation which is negligible. The energy trade-off between communication and computation provides applications benefit when processing the data at the network side rather than simply transmitting sensor data. In this study, the authors consider a cluster-based technique with which data is sent periodically from sensor nodes to their appropriate cluster-heads (CH). The proposed technique manages energy efficiency in periodic sensor network and it consists of two phases: ‘aggregation phase and adaptation phase’. The aggregation phase is used to find similarities between data (measurements captured during a period p) in order to eliminate redundancy from raw data, thus reducing the amount of data-sets sent to the CH. The adaptation phase provides sensors the ability to identify duplicate data-sets captured among successive periods, using the sets-similarity joins functions. To evaluate the performance of the proposed technique, experiments on real sensor data have been conducted. Results show that the proposed technique is effective in term of energy consumption and quality of data.

Data aggregation for periodic sensor networks using sets similarity functions

Energy 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.

Efficient and Robust Secure Aggregation of Encrypted Data in Sensor Networks

Wireless 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.

Residual energy-based adaptive data collection approach for periodic sensor networks

Due to its potential applications and the density of the deployed sensors, distributed wireless sensor networks are one of the highly anticipated key contributors of the big data in the future. Consequently, massive data collected by the sensors beside the limited battery power are the main limitations imposed by such networks. In this paper, we consider a periodic sensor networks (PSNs) where sensors transmit their data to the sink on a periodic basis. We propose an efficient adaptive model of data collection dedicated to PSN, in order to increase the network lifetime and to reduce the huge amount of the collected data. The main idea behind this approach is to allow each sensor node to adapt its sampling rate to the physical changing dynamics. In this way, the oversampling can be minimized and the power efficiency of the overall network system can be further improved. The proposed method is based on the dependence of measurements variance while taking into account the residual energy that varies over time. We study three well known statistical tests based on One-Way Anova model. 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. Experiments on real sensors data show that our approach can be effectively used to minimize the amount of data retrieved by the network and conserve energy of the sensors, without loss of fidelity/accuracy.

An optimized in-network aggregation scheme for data collection in periodic sensor networks

In-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 outperforms existing filtering techniques.

Dynamic scheduling of cover-sets in randomly deployed Wireless Video Sensor Networks for surveillance applications

A Wireless Video Sensor Network (WVSN) consists of a set of sensor nodes equipped with miniaturized video cameras. Unlike omni-directional sensors, the sensing region of a video node is limited to the field of view of its camera. Power conservation and coverage is an important issue in such wireless video networks, especially in the context of surveillance applications which is the focus of the article. In this paper, we address the area coverage problem of scheduling the activity of randomly deployed nodes to extend the network lifetime. We present a distributed algorithm for area coverage (no known targets). Moreover, we show that our approach reduces inherent ambiguities when it is necessary. Simulation results are also presented to verify the performance of the proposed approach.

K-means based clustering approach for data aggregation in periodic sensor networks.

In-network data aggregation becomes an important technique to achieve efficient data transmission in wireless sensor networks (WSN). Energy efficiency, data latency and data accuracy are the major key elements evaluating the performance of an in-network data aggregation technique. The trade-offs among them largely depends on the specific application. For instance, prefix frequency filtering (PFF) is a good recently example for an in-network data aggregation technique that optimizing energy consumption and data accuracy. The objective of PFF is to find similar data sets generated by neighboring nodes in order to reduce redundancy of the data over the network and thus to preserve the nodes energy. Unfortunately, this technique has a heavy computational load. In this paper, we propose an enhanced new version of the PFF technique called KPFF technique. In this new technique, we propose to integrate a K-means clustering algorithm on data before applying the PFF on the generated clusters. By this way we minimize the number of comparisons to find similar data sets and thus we decrease the data latency. Experiments on real sensors data show that our new technique can significantly reduce the computational time without affecting the data aggregation performance of the PFF technique.