Abbas Hijazi

Algorithm for data similarity measurements to reduce data redundancy in wireless sensor networks

Extending the lifetime of wireless sensor networks remains the most challenging and demanding requirement that impedes large-scale deployments. The basic operation in WSNs is the systematic gathering and transmission of sensed data to a base station for further processing. During data gathering, the amount of data can be large sometimes, due to redundant data combined from different sensing nodes in the neighborhood. Thus the data gathered need to be processed before being transmitted, in order to detect and remove redundancy, which can impact the communication traffic and energy consumption of the network in a negative way. In this paper, we propose an algorithm to measure similarity between the data collected toward the base station(relative to a specific event monitoring), so that an aggregator sensor sends a minimum amount of information to the base station in a way that the latter can deduce the source information of sensing neighbors nodes. Further, our experimental results demonstrate that the communication traffic and the number of bits transmitted can be minimized while preserving accuracy on the base station estimations.

Algorithm for temporal anomaly detection in WSNs

Knowledge discovery and data analysis in resource constrained wireless sensor networks faces different challenges. One of the main challenges is to identify misbehaviors or anomalies with high accuracy while minimizing energy consumption in the network. In this paper, we extend a previous work of us and we present an algorithm for temporal anomalies detection in wireless sensor networks. Our experiments results show that our algorithm can efficiently and accurately detect anomalies in sensor measurements. It also produces low false alarm rate for slow variation time series measurements without harvesting the source of energy.

Three-dimensional Monte Carlo simulations of the dynamics of macromolecular particles in solutions flowing in mesopores

Numerical simulations are developed to calculate the dynamic equilibrium probability distribution functions (PDF) for macromolecular rod-like particles suspended in a fluid under hydrodynamic flow inside mesopores. The simulations take into account the effects of Brownian and hydrodynamic forces acting on the particles, as well as diffusive collisions of the particles with the solid surface boundaries. An algorithm is developed for this purpose based on Jeffery’s equations for the dynamics of ellipsoidal objects in bulk fluids, and on a mechanism of restitution for the diffusive collisions. The results are presented with a focus on the depletion layer next to two types of solid boundaries, ideally flat and rough. They demonstrate the significance of numerical simulations in 3D compared to previous results based on a 2D approach. In particular, we are able to obtain a complete topography for the PDFs segmented as a hierarchy in the depletion layer.

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 networks 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.

End to End Mechanism to Protect Sip from Signaling Attacks

SIP is among the most popular Voice over IP signaling protocols. Its deployment in live scenarios showed vulnerability to attacks defined as signaling attacks. These attacks are used to tear down a session or manipulate its parameters. In this paper we present a security mechanism that protects SIP sessions against such attacks. The mechanism uses SIP fingerprint to authenticate messages, in order to prevent spoofing. We validate our mechanism using Openssl and Sipp and show that it is light and robust.

Test and diagnosis of wireless sensor networks applications

Safety critical applications, such as explosion prediction, require continuous and reliable operation of Wireless sensor networks (WSNs). However, validating that a WSN system will function correctly at run time is a hard problem. This is due to the numerous faults one may encounter in the resource-constrained nature of sensor platforms together with the unreliability of the wireless links networks. A holistic fault tolerance approach that addresses all fault issues does not exist. Existing fault tolerance work most likely misses some potential causes of system failures. In this paper, we propose an integrated fault tolerance framework (IFTF) that reduces the false negative by combining a network diagnosis service (component/element level monitoring) with an application testing service (system level monitoring). Thanks to these two complementary services, the maintenance operations will be more efficient leading to a more dependable WSN. From the design view, IFTF offers to the application many tunable parameters that make it suitable for various application needs. Simulation results show that the IFTF reduces the false negative rate of application level failures to 60% with an increase of 4% in power consumption (communication overhead) compared to using solely network diagnosis solutions.

SMART Service for Fault Diagnosis in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), performance and reliability depend on the fault tolerance scheme used in the system. Fault diagnosis is an important part of fault tolerance. An effective diagnosis tool helps network administrators clearly monitor, manage, and troubleshoot the performance of the network. However, the design of online fault diagnosis is crucial in WSNs since many faults can easily happen and propagate. Besides, fault diagnosis put extra burden on the sensor node and it will also consume extra resources of the sensor nodes. Thus, in order to guarantee the network quality of service, it is essential for WSNs to be able to diagnosis faults efficiently. In this paper, we propose an adaptive and efficient approach for fault diagnosis in WSN called (SMART). SMART is a layer independent fault diagnosis service for WSNs. The presented service focuses on diagnosis two types of failures that are likely to happen in WSN deployments which are the node failure due to energy depletion, and the link failure due to poor connectivity with neighbors. From the design view, SMART provides to the application many tunable parameters that make it suitable for various deployment needs: energy-robustness-detection latency tradeoffs, tolerable packet loss, reports frequency etc. Simulation results prove that SMART is resource efficient while providing satisfactory detection and diagnosis accuracy.

Online data fault detection for wireless sensor networks - case study

Sensor faults are the rule and not the exception in every WSN deployment. Sensors themselves may get stuck at a particular value or get partially disconnected and report noisy measurements. Sensor nodes may reboot unexpectedly or stop transmitting data. Software running on the sensor nodes may have bugs and may cause data loss. In this paper, we present an efficient approach for online data fault detection and its application to a case study from a real world dataset. Our approach exposes four types of data faults as they occur by locally applying five simple heuristic rules. By locally applying these rules, node will not need to exchange messages with neighboring ones and consequently to consume energy. Simulation results showed that around 19% of the total collected readings from a real world dataset were faulty.

Integrated fault tolerance framework for wireless sensor networks

Safety critical applications, such as fire detection or burglar alarm systems, require continuous and reliable operation of Wireless sensor networks (WSNs). However, validating that a WSN system will function correctly at run time is a hard problem. This is due to the numerous faults one may encounter in the resource-constrained nature of sensor platforms together with the unreliability of the wireless links networks. A holistic fault tolerance approach that addresses all fault issues does not exist. Existing fault tolerance work most likely misses some potential causes of system failures. In this paper, we propose an integrated fault tolerance framework (IFTF) that provides a complete picture of the system health with possibility to zoom in on the fault reasons of abnormal phenomena. IFTF diagnoses network failures, detects application level failures, identifies affected areas of the network and may determine the root causes of application malfunctioning. These goals are achieved efficiently through combining a network diagnosis service (component/element level monitoring) with an application testing service (system level monitoring). Thanks to these two complementary services, the maintenance operations will be more efficient leading to a more dependable WSN. From the design view, IFTF offers to the application many tunable parameters that make it suitable for various application needs. Simulation results show that the presented solution is efficient both in terms of memory use and power consumption. IFTF incurs a 4 %, on average, increase in power consumption (communication overhead) compared to using solely network diagnosis solutions.

A Self-Monitoring, Adaptive and Resource Efficient Approach for Improving QoS in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), performance and reliability depend on the fault tolerance scheme used in the system. Fault diagnosis is an important part of fault tolerance. An effective diagnosis tool helps network administrators clearly monitor, manage, and troubleshoot the performance of the network. However, the design of online fault diagnosis is crucial in WSNs since many faults can easily happen and propagate. Besides, fault diagnosis put extra burden on the sensor node and it will also consume extra resources of the sensor nodes. Thus, in order to guarantee the network quality of service, it is essential for WSNs to be able to diagnosis faults efficiently. In this paper, we propose an adaptive and efficient approach for fault diagnosis in WSN called (SMART). SMART is a layer independent fault diagnosis service for WSNs. The presented service focuses on diagnosis two types of failures that are likely to happen in WSN deployments which are the node failure due to energy depletion, and the link failure due to poor connectivity with neighbors. From the design view, SMART provides to the application many tunable parameters that make it suitable for various deployment needs: energy-robustness-detection latency tradeoffs, tolerable packet loss, reports frequency etc. Simulation results prove that SMART is resource efficient while providing satisfactory detection and diagnosis accuracy.