A New Method for Node Fault Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD) scheme checks out the failed nodes by exchanging data and mutually testing among neighbor nodes in this network., but the fault detection accuracy of a DFD scheme would decrease rapidly when the number of neighbor nodes to be diagnosed is small and the node's failure ratio is high. In this paper, an improved DFD scheme is proposed by defining new detection criteria. Simulation results demonstrate that the improved DFD scheme performs well in the above situation and can increase the fault detection accuracy greatly

Wireless sensor network modeling using modified recurrent neural network: Application to fault detection

Wireless Sensor Networks (WSNs) consist of a large number of sensors, which in turn have their own dynamics. They interact with each other and the base station, which controls the network. In multi-hop wireless sensor networks, information hops from one node to another and finally to the network gateway or base station. Dynamic Recurrent Neural Networks (RNNs) consist of a set of dynamic nodes that provide internal feedback to their own inputs. They can be used to simulate and model dynamic systems such as a network of sensors. In this dissertation, a dynamic model of wireless sensor networks and its application to sensor node fault detection are presented. RNNs are used to model a sensor node, the node\u27s dynamics, and the interconnections with other sensor network nodes. A neural network modeling approach is used for sensor node identification and fault detection in WSNs. The input to the neural network is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type neural network. The input to the neural network (NN) and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme. The simulation with comparison to the Kalman filtering technique was carried out on a network with 15 sensor nodes. A fault such as drift was introduced and successfully detected with the modified recurrent neural net model with no early false alarm that could have resulted when using the Kalman filtering approach. In this dissertation, we also present the real-time implementation of a neural network-based fault detection for WSNs. The method is implemented on a TinyOS operating system. A collection tree network is formed, and multi-hoping data is sent to the base station root. Nodes take environmental measurements every N seconds while neighboring nodes overhear the measurement as it is being forwarded to the base station for recording it. After nodes complete M and receive/store M measurements from each neighboring node, recurrent neural networks are used to model the sensor node, the node\u27s dynamics, and the interconnections with neighboring nodes. The physical measurement is compared to the predicted value and to a given threshold of error to determine a sensor fault. The process of neural network training can be repeated indefinitely to maintain self-aware network fault detection. By simply overhearing network traffic, this implementation uses no extra bandwidth or radio broadcast power. The only costs of the approach are the battery power required to power the receiver for overhearing packets and the processor time to train the RNN

FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

Sensor networks aim at monitoring their surroundings for event detection and object tracking. But, due to failure, or death of sensors, false signal can be transmitted. In this paper, we consider the problems of distributed fault detection in wireless sensor network (WSN). In particular, we consider how to take decision regarding fault detection in a noisy environment as a result of false detection or false response of event by some sensors, where the sensors are placed at the center of regular hexagons and the event can occur at only one hexagon. We propose fault detection schemes that explicitly introduce the error probabilities into the optimal event detection process. We introduce two types of detection probabilities, one for the center node, where the event occurs and the other one for the adjacent nodes. This second type of detection probability is new in sensor network literature. We develop schemes under the model selection procedure, multiple model selection procedure and use the concept of Bayesian model averaging to identify a set of likely fault sensors and obtain an average predictive error.Comment: 14 page

Fault Diagnosis Algorithms for Wireless Sensor Networks

The sensor nodes in wireless sensor networks (WSNs) are deployed in unattended and hostile environments. The ill-disposed environment affects the monitoring infrastructure that includes the sensor nodes and the links. In addition, node failures and environmental hazards cause frequent topology change, communication failure, and network partition. This in turn adds a new dimension to the fragility of the WSN topology. Such perturbations are far more common in WSNs than those found in conventional wireless networks. These perturbations demand efficient techniques for discovering disruptive behavior in WSNs. Traditional fault diagnosis techniques devised for wired interconnected networks, and conventional wireless networks are not directly applicable to WSNs due to its specific requirements and limitations. System-level diagnosis is a technique to identify faults in distributed networks such as multiprocessor systems, wired interconnected networks, and conventional wireless networks. Recently, this has been applied on ad hoc networks and WSNs. This is performed by deduction, based on information in the form of results of tests applied to the sensor nodes. Neighbor coordination-based system-level diagnosis is a variation of this method, which exploits the spatio-temporal correlation between sensor measurements. In this thesis, we present a new approach to diagnose faulty sensor nodes in a WSN, which works in conjunction with the underlying clustering protocol and exploits spatio-temporal correlation between sensor measurements. An advantage of this method is that the diagnostic operation constitutes real work performed by the system, rather than a specialized diagnostic task. In this way, the normal operation of the network can be used for the diagnosis and resulting less time and message overhead. In this thesis, we have devised and evaluated fault diagnosis algorithms for WSNs considering persistence of the faults (transient, intermittent, and permanent), faults in communication channels and in one of the approaches, we attempt to solve the issue of node mobility in diagnosis. A cluster based distributed fault diagnosis (CDFD) algorithm is proposed where the diagnostic local view is obtained by exploiting the spatially correlated sensor measurements. We derived an optimal threshold for effective fault diagnosis in sparse networks. The message complexity of CDFD is O(n) and the number of bits exchanged to diagnose the network are O(n log2 n). The intermittent fault diagnosis is formulated as a multiobjective optimization problem based on the inter-test interval and number of test repetitions required to diagnose the intermittent faults. The two objectives such as detection latency and energy overhead are taken into consideration with a constraint of detection errors. A high level (> 95%) of detection accuracy is achieved while keeping the false alarm rate low (< 1%) for sparse networks. The proposed cluster based distributed intermittent fault diagnosis (CDIFD) algorithm is energy efficient because in CDIFD, diagnostic messages are sent as the output of the routine tasks of the WSNs. A count and threshold-based mechanism is used to discriminate the persistence of faults. The main characteristics of these faults are the amounts of time the fault disappears. We adopt this state-holding time to discriminate transient from intermittent or permanent faults. The proposed cluster based distributed fault diagnosis and discrimination (CDFDD) algorithm is energy efficient due to the improved network lifetime which is greater than 1150 data-gathering rounds with transient fault rates as high as 20%. A mobility aware hierarchal architecture is proposed which is to detect hard and soft faults in dynamic WSN topology assuming random movements of nodes in the WSN. A test pattern that ensures error checking of each functional block of a sensor node is employed to diagnose the network. The proposed mobility aware cluster based distributed fault diagnosis (MCDFD) algorithm assures a better packet delivery ratio (> 80%) in highly dynamic networks with a fault rate as high as 30%. The network lifetime is more than 900 data-gathering rounds in a highly dynamic network with a fault rate as high as 20%

A Novel Method to Improve the Resolution of Envelope Spectrum for Bearing Fault Diagnosis Based on a Wireless Sensor Node

In this paper, an accurate envelope analysis algorithm is developed for a wireless sensor node. Since envelope signals employed in condition monitoring often have narrow frequency bandwidth, the proposed algorithm down-samples and cascades the analyzed envelope signals to construct a relatively long one. Thus, a relatively higher frequency resolution can be obtained by calculating the spectrum of the cascaded signal. In addition, a 50 % overlapping scheme is applied to avoid the distortions caused by Hilbert transform based envelope calculation. The proposed method is implemented on a wireless sensor node and tested successfully for detecting an outer race fault of a rolling bearing. The results show that the frequency resolution of the envelope spectrum is improved by 8 times while the data transmission remains at a low rate

An adaptive envelope analysis in a wireless sensor network for bearing fault diagnosis using fast kurtogram algorithm

This paper proposes a scheme to improve the performance of applying envelope analysis in a wireless sensor network for bearing fault diagnosis. The fast kurtogram is realized on the host computer for determining an optimum band-pass filter for the envelope analysis that is implemented on the wireless sensor node to extract the low frequency fault information. Therefore, the vibration signal can be monitored over the bandwidth limited wireless sensor network with both intelligence and real-time performance. Test results have proved that the diagnostic information for different bearing faults can be successfully extracted using the optimum band-pass filter