Murad A. Rassam

Advancements of data anomaly detection research in wireless sensor networks: a survey and open issues.

Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.

An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications

Wireless sensor networks (WSNs) applications are growing rapidly in various fields such as environmental monitoring, health care management, and industry control. However, WSNs are characterized by constrained resources especially; energy which shortens their lifespan. One of the most important factors that cause a rapid drain of energy is radio communication of multivariate data between nodes and base station. Besides, the dynamic changes of environmental variables pose a need for an adaptive solution that cope with these changes over the time. In this paper, a new adaptive and efficient dimension reduction model (APCADR) is proposed for hierarchical sensor networks based on the candid covariance-free incremental PCA (CCIPCA). The performance of the model is evaluated using three real sensor networks datasets collected at Intel Berkeley Research Lab (IBRL), Great St. Bernard (GSB) area, and Lausanne Urban Canopy Experiments (LUCE). Experimental results show 33.33% and 50% reduction of multivariate data in dynamic and static environments, respectively. Results also show that 97-99% of original data is successfully approximated at cluster heads in both environment types. A comparison with the multivariate linear regression model (MLR) and simple linear regression model (SLR) shows the advantage of the proposed model in terms of efficiency, approximation accuracy, and adaptability with dynamic environmental changes.

Adaptive and online data anomaly detection for wireless sensor systems

Wireless sensor networks (WSNs) are increasingly used as platforms for collecting data from unattended environments and monitoring important events in phenomena. However, sensor data is affected by anomalies that occur due to various reasons, such as, node software or hardware failures, reading errors, unusual events, and malicious attacks. Therefore, effective, efficient, and real time detection of anomalous measurement is required to guarantee the quality of data collected by these networks. In this paper, two efficient and effective anomaly detection models PCCAD and APCCAD are proposed for static and dynamic environments, respectively. Both models utilize the One-Class Principal Component Classifier (OCPCC) to measure the dissimilarity between sensor measurements in the feature space. The proposed APCCAD model incorporates an incremental learning method that is able to track the dynamic normal changes of data streams in the monitored environment. The efficiency and effectiveness of the proposed models are demonstrated using real life datasets collected by real sensor network projects. Experimental results show that the proposed models have advantages over existing models in terms of efficient utilization of sensor limited resources. The results further reveal that the proposed models achieve better detection effectiveness in terms of high detection accuracy with low false alarms especially for dynamic environmental data streams compared to some existing models.

One-Class Principal Component Classifier for anomaly detection in wireless sensor network

To ensure the quality of data collected by sensor networks, misbehavior in measurements should be detected efficiently and accurately in each sensor node before relying the data to the base station. In this paper, a novel anomaly detection model is proposed based on the lightweight One Class Principal Component Classifier for detecting anomalies in sensor measurements collected by each node locally. The efficiency and accuracy of the proposed model are demonstrated using two real life wireless sensor networks datasets namely; labeled dataset (LD) and Intel Berkeley Research Lab dataset (IBRL). The simulation results show that our model achieves higher detection accuracy with relatively lower false alarms. Furthermore, the proposed model incurs less energy consumption by reducing the computational complexity in each node.

A sinkhole attack detection scheme in Mintroute wireless Sensor Networks

Wireless sensor networks (WSNs) have emerged as one of the hottest research areas in recent years. These networks use radio communications as a media for transmission which make them susceptible to different types of attacks. Sinkhole attack is considered as one of the severe attacks that is launched by a compromised node to attract the network traffic away from the intended route. Consequently, sinkhole attackers may run several types of attacks such as selective forwarding, denial of service (DoS), or even data fabrication attacks. In this paper, the vulnerabilities of Mintroute protocol to sinkhole attacks are discussed and the existing manual rules used for detection are investigated using different architecture. The initial experimental results with real WSN testbed show its ability in detecting sinkhole attacks for small size WSNs. Finally, the design of sinkhole detection scheme for Mintroute-based WSN is proposed.

Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter

Accurate positioning is a key factor for enabling innovative applications to properly perform their tasks in various areas including: Intelligent Transportation Systems (ITS) and Vehicular Ad Hoc Network (VANET). Vehicle positioning accuracy depends heavily on positioning techniques and the measurements condition in its surroundings. Several approaches which can be used for improving vehicle positioning accuracy have been reported in literature. Although some positioning techniques have achieved high accuracy in a controlled environment, they suffer from dynamic measurement noises in real environments leading to low accuracy and integrity for some VANET applications. To solve this issue, some existing positioning approaches assume the availability of prior knowledge concerning measurement noises, which is not practical for VANET. The aim of this paper is to propose an algorithm for improving accuracy and integrity of positioning information under dynamic and unstable measurement conditions. To do this, a positioning algorithm has been designed based on the Innovation-based Adaptive Estimation Kalman Filter (IAE_KF) by integrating the positioning measurements with vehicle kinematic information. Following that, the IAE_KF algorithm is enhanced in terms of positioning accuracy and integrity (EIAE_KF) in order to meet VANET applications requirements. This enhancement involves two stages which are: a switching strategy between dead reckoning and the Kalman Filter based on the innovation property of the optimal filter; and the estimation of the actual noise covariance based on the Yule–Walker method. An online error estimation model is then proposed to estimate the uncertainty of the EIAE_KF algorithm to enhance the integrity of the position information. Next Generation Simulation dataset (NGSIM) which contains real world vehicle trajectories is used as ground truth for the evaluation and testing procedure. The effectiveness of the proposed algorithm is demonstrated through a comprehensive simulation study. The results show that the EIAE_KF algorithm is more effective than existing solutions in terms of enhancing positioning information accuracy and integrity so as to meet VANET applications requirements.

Principal component analysis-based data reduction model for wireless sensor networks

Wireless sensor networks WSNs are widely used in monitoring environmental and physical conditions, such as temperature, vibration, humidity, light and voltage. However, the high dimension of sensed data, especially in multivariate sensor applications, increases the power consumption in transmitting this data to the base station and hence shortens the lifetime of sensors. Therefore, efficient data reduction methods are needed to minimise the power consumption in data transmission. In this paper, an efficient model for multivariate data reduction is proposed based on the principal component analysis PCA. The performance of the model was evaluated using Intel Berkeley Research Lab IBRL dataset. The experimental results show the advantages of the proposed model as it allows 50% reduction rate and 96% approximation accuracy after reduction. A comparison with an existing model shows the superiority of the proposed model in terms of approximation accuracy as the reconstruction error is always smaller for different datasets.

A novel intrusion detection framework for Wireless Sensor Networks

Wireless Sensor Networks (WSN) security issues are getting more attention by researchers due to deployment circumstances. They are usually deployed in unattended and harsh environments that make them susceptible for many kinds of attacks. Different security mechanisms have been proposed for WSN. Detection-based mechanisms are considered to be the second defense line against attacks when the traditional prevention based mechanisms failed to detect them. Different intrusion detection schemes have been introduced (e.g. rule based, statistical based…etc). Rule-based intrusion detection schemes are considered to be the fast and simple schemes that are suitable for the demand of WSN. However, these schemes are more specific to some kinds of attacks and cannot be generalized. In addition, these schemes cannot detect the unknown attacks that are not included in their rule base. In this paper, we highlight the limitations of the state-of-the-art rule based intrusion detection schemes and then introduce a novel framework based on rule based scheme that is able to overcome these limitations.

Mobility information estimation algorithm using Kalman-filter for vehicular ad hoc networks

In vehicular networks, the exchange of mobility information is considered as the basis for many applications. The quality of this information affects applications and networks performance. False mobility information leads applications, network services and security services to make wrong decisions. Accordingly, many verification approaches were proposed as a security countermeasure. However, most of these approaches assume accurate mobility information regardless of localisation errors occurrence. Therefore, estimating the mobility information is an important security procedure to enhance the verification performance. In this paper, mobility information estimation algorithm MIEA was proposed based on Kalman filter to estimate the actual mobility information from noisy measurements. To evaluate the algorithm effectiveness, the study used next generation simulation dataset NGSIM. White noises were injected into vehicle trajectories to simulate localisation errors. Results show that the root mean square error RMSE is reduced which implies the effectiveness of the proposed algorithm.