Faults In Wireless Sensor Networks Research Articles

Wireless Sensor Networks Fault Detection and Identification

We have developed and experimentally tested a set of models for the detection and identification of sensor faults that commonly occur in wireless sensor networks. Considered faults include outlier, spike, variance, high-frequency noise, offset, gain, and drift faults. These faults affect the system operations and can endanger operators, final users, and the general public. The fault detection models are divided into two classes: data-centric models, which only analyze a single data stream, and system-centric models, which consider the overall system. For data-centric models, we use the magnitude, the gradient, and the variance of raw sensor data to model faults. For system-centric models, we introduce variogram-based techniques that allow faults to be detected by comparing readings from multiple sensors that measure related phenomena. For data-centric and system-centric sensor fault detection, we show how a few model parameters affect the sensitivity of wireless sensor network fault models. We present simulation and experimental results that illustrate the fault detection and identification models. The system is intended for health monitoring applications of the NASA Stennis Space Center (SSC) test stands and widely distributed support systems, including pressurized gas lines, propellant delivery systems, and water coolant lines. The testbed consists of Coremicro® reconfigurable embedded smart sensor nodes [29] capable of wireless communication, a network-capable application processor, a wireless base station, the software that supports sensor and actuator health monitoring, a database server, and a smartphone running a health monitoring Android application.

Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

A hypergrid based adaptive learning method for detecting data faults in wireless sensor networks

In wireless sensor networks (WSNs), data anomalies/faults often occur due to the limited resources and unreliability of sensor nodes. Many traditional anomaly detection methods are designed in a batch manner, but for the nature of streaming data in WSNs, continuous anomaly detection method is preferred. Existing methods often detect only a single type of faults but cannot detect multiple types of faults that actually are more common in the sensor data. Therefore, this paper provides a Hypergrid based Adaptive Detection of Faults (HADF) method, which adopts hypergrid and statistical analysis to recognize three types of faults in the sensor data, including outliers, stuck-at faults, and noisy faults. HADF is a distributed method running on sensor nodes, which can reduce the influence of concept drift in unstable streaming data through combining both lazy leaning and continuous learning to adaptively update its normal profile. In the experimental study, we have manually inserted different types of faults into two real-world datasets, and the results demonstrate that HADF achieves higher accuracy with reasonable efficiency for detecting the data faults than four counterpart methods.

Software Engineering Based Fault Tolerance Method for Wireless Sensor Network

The management of faults in Wireless Sensor Networks (WSN) has been considered recently. The problem of tolerating the detected fault is solved by presenting different methods from numerous researchers. Moreover, the software engineering approaches have been adopted to introduce methods with high reliability. In this paper, a fault tolerance method is proposed for WSN based on the software engineering self-checking process to deal with the faults that affect energy consumption in the network and make it drop earlier. The proposed method detects the appeared fault at any sensor node and recovers the faulted readings by computing the average value of its neighbor nodes. In addition, this process is continued until the faulty sensor is fixed by the maintenance team. The proposed method is tested over different case studies and the obtained results prove the claim of the paper's idea.

Fault diagnosis based on extremely randomized trees in wireless sensor networks

Wireless Sensor Network (WSN) being highly diversified cyber–physical system makes it vulnerable to numerous failures, which can cause devastation towards safety, economy, and systems’ reliability. Precise detection and diagnosis of failures or faults in WSN is a challenging issue due to the diversity of deployment and the limitations in the sensors’ resources. In this paper, supervised machine learning-based technique is considered to scrutinize the behavior of sensors through their data for the detection and diagnosis of faults. Most of the faults that commonly occur in WSN are considered: hardover, drift, spike, erratic, data-loss, stuck, and random fault. A trusted dataset published online by the researchers at the University of North Carolina composed of temperature and humidity sensor measurements of multi-hop scenario was acquired and the aforementioned faults were simulated in non-faulty (normal) data. Events from fault occurrences were generated to replicate realistic scenarios of WSN. To detect and diagnose the faults in timely manner, we adopt an ensemble learning-based lightweight technique called Extremely Randomized Trees or Extra-Trees. The proposed Extra-Trees-based detection scheme has the ability of robustness towards signal noise and strong reduction of bias and variance error. The performances of the proposed scheme were compared with those of the state-of-the-art machine learning algorithms such as support vector machine, random forest, neural network, and decision tree. Performance evaluation shows the efficiency of the proposed scheme in terms of accuracy, precision, and F1-score. In addition, the proposed scheme has low training time compared to state-of-the-art approaches.

Fault autonomous model handling through integrated adaptive‐filters for eliminating deployment faults in wireless sensor networks

Wireless Sensor Networks (WSNs) are exposed to various data-deployment faults during the communication action. These faults may impact the behaviour of the sensors that degrade its performance and cuts its life. Therefore, we tend to implement the integration of two independent trends are self-awareness and self-adaptation capabilities along with two integrated adaptive filters, FIR and RLS. The proposed Autonomous Fault-Awareness and Adaptive (AFAA) model composed of three adaptive two-stage executed self-awareness approach to limit the impact of such faults during the propagation process. In this paper, we introduce the operational mechanism of AFAA that manages to identify the failure and aware of the lost signal values autonomously, then filter the perceptive-signals for eliminating the accompanied interference and gaining convergent values. It executed the incorporated autonomous model at the level of Cluster Head (CH) for independent fault-correction using an adaptive feedback model. Compared to the state-of-the-art methods, the proposed model achieved speed in fault diagnosis; also high-accuracy rate in the prediction of the lost signal values as much as 98.63%, thus improving the percentage of performance efficiency to 3:1 times along of duty cycle. Hence, it enhanced the overall network lifetime.

Lightweight approach to automated fault diagnosis in WSNs

Automated fault diagnosis in wireless sensor networks (WSNs) has always been an essential field for the networks research community. In resource-related constraints, the design of a lightweight method to automatically diagnose the fault in WSN is highly beneficial. In this study, the authors propose lightweight and less-overhead approaches to diagnose hard and soft faults in WSNs automatically. Correctly, a lightweight checksum method is implemented for hard or crash fault detection. This method is capable of detecting multiple hard faults within a single path with the help of a timeout mechanism. For diagnosis of soft faults such as permanent, intermittent, and transient faults, they implement the Anderson–Darling (AD) statistical method. The AD test analyses how the sensor readings are fitted in a specific distribution for a tested significance level. Several testbed experiments are conducted to validate their hypotheses and implementations. The performance parameters that are evaluated in this work include fault detection accuracy, false alarm rate, and false-positive rate. These parameters are evaluated about various fault probabilities in the network. They observe that the proposed lightweight schemes can diagnose both hard and soft faults in message complexity over the network, which makes it adaptable in practise.

Fault‐tolerant mechanism for wireless sensor network

The study aims to understand the various types of faults occur in a wireless sensor network (WSN). The primary objective of the fault-tolerant technique is to enhance the robustness and self-healing capacity of sensor network. The resource constraint environment, remote deployment and harsh monitoring conditions frequently invite different types of faults in WSN. The fault can be handled in two ways such as fault tolerance and fault prevention method. The novelty of this state-of-art is, the authors propose two new algorithms named Uniform Energy Clustering for fault tolerance and Population-Based Clustering for fault prevention. The result of the simulation is satisfactory in comparison to existing algorithms such as Low Energy Adaptive Clustering Hierarchy, Stable Election Protocol, Virtual Area Partition - Energy and Energy-Aware Multi-Hop Multi-Path Hierarchical protocols. In addition to these new algorithms, they also propose a unique taxonomy on fault which is based on sensitivity issues of specific applications.

Wireless Sensor Network Fault Sensor Recognition Algorithm Based on MM* Diagnostic Model

Wireless Sensor Network (WSN) as one of the representatives of the Internet of Things technology has also received much attention. To accurately diagnose fault sensor nodes, a fault diagnosis method based on fireworks algorithm optimization convolutional neural network algorithm is proposed. The weights and biases of the convolutional neural networks are optimized by using the self-regulating mechanism of global and local searching ability of fireworks algorithm. So the problem of convolution neural network in extreme judgment and limited convergence speed is solved, to effectively realize the fault diagnosis of the WSN. Simulation experiments show that this algorithm has higher fault diagnosis accuracy than other classic WSN fault diagnosis algorithms.

A mobile fault detection algorithm in heterogeneous wireless sensor networks: a bio-inspired approach

This paper puts forth a novel mobile fault detection algorithm for wireless sensor networks (WSNs) based on bacterial-inspired optimization. We introduce a bio-swarm intelligence approach to mobile fault detection in WSNs by using voltage values. At certain times, the sensor nodes in the clustered network send data packets containing health-fitness information to cluster heads (CHs) selected by the proposed CH selection algorithm. A mobile sink (MS) collects the health status via data from all the nodes as they reach the intersection point of the CHs. After this stage, the data packets are analyzed by the MS, and hardware or software faults are detected by assessing the fitness values of the nodes. The faulty nodes are eventually discarded from the network, and recovery of the rest of the nodes in the network is satisfied. Inspired by the interaction of bacteria for feed collection, their response to chemicals, and their interaction and communication with one another, we bring an innovative approach to finding node failures or software faults in WSNs, and these failures are removed from the network to help its operation and to take measures to maintain the electrical structures. In fact, we adapt our algorithm to low energy harvesting electrical components as an example. We compare our novel algorithm with existing studies through extensive simulations in NS 2 environment based on fault detection accuracy, false alarm rate, and false positive rate criteria versus fault probability, number of nodes, and sink speed. Considering detection accuracy, the simulation results validate that our algorithm shows better performance as compared with others.

Underlying and Persistence Fault Diagnosis in Wireless Sensor Networks Using Majority Neighbors Co-ordination Approach

The faults in wireless sensor network are classified according to the underlying causes, behavior, and persistence with respect to the observation time. Due to underlying causes, faults are classified as fail and stop, crash, omission, timing, and incorrect computation fault. Due to behavior, faults are classified as hard and soft fault. Due to persistence, faults are classified as permanent, intermittent, and transient fault. As the recent state-of-art fault diagnosis is a significant requirement for each application of wireless sensor network. In this research paper, we have proposed a fault diagnosis protocol using majority neighbors coordination based approach for wireless sensor network. Precisely, a multiple-hop data received technique, timeout period mechanism, timeout request and response message exchange, timeout early and delay message exchange, and degree of belongingness using Gaussian function mechanism are used for the detection of faults such as fail and stop, crash, omission, timing, and incorrect computation. The mean difference and standard error comparison with different threshold condition are used for soft (permanent, intermittent, and transient) fault detection, and timeout response mechanism with different threshold condition is used for hard (permanent, intermittent, and transient) fault detection. After fault detection, the actual fault status of the sensor node is confirmed by the one-hop majority neighbor sensor nodes. For validation of the proposed fault detection algorithms, simulation experiments are conducted by the network simulator NS-2.35. The experimental results show the substantially parameters performance such as fault detection accuracy, false alarm rate, false positive rate, and false classification rate with increasing the fault probability for different average degree of the sensor nodes in the network.

Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

Wireless sensor network (WSN) is a distributed intelligent network, which can independently achieve the information collection task of monitoring targets. However, the WSN is susceptible to faults due to various factors, such as sensor resources, network bandwidth, and work environment. The WSN fault prediction technology can estimate the fault trend of the WSN, which can provide the basis for the formulation and implementation of emergency strategies. In this paper, a new WSN fault prediction method is proposed based on evidential reasoning (ER) and belief rule base (BRB). First, the process of the WSN fault prediction is described, which mainly includes the fault assessment of the current WSN and the fault prediction of future WSN. Second, the WSN fault prediction model is constructed, including the ER-based fault assessment model and BRB-based fault prediction model. The projection covariance matrix adaptation evolutionary strategies (P-CMA-ESs) are used to optimize model parameters. Finally, a case study is constructed to verify the validity of the WSN fault prediction model. The experimental results show that the model can adequately estimate the fault state of the current WSN and then predict the fault status of future WSN.

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

With the wide applications of wireless sensor network (WSN), its reliability evaluation has been attracted more attention. The reliability of a WSN is affected mainly by internal and external factors, which include internal faults and external attacks. In this paper, a reliability evaluation method based on a hierarchical belief rule base (BRB) method is proposed for the reliability evaluation of the WSN. First, the factors affecting the reliability of a WSN are analysed, and the reliability evaluation process that considers the WSN fault evaluation and WSN security evaluation is described. Second, the reliability evaluation model is constructed based on the hierarchical BRB model. The qualitative knowledge is used by the BRB model to build initial belief rules, and the quantitative data are used to optimize the initial parameters of the BRB model, which can utilize various types of uncertainty information effectively. Therefore, the proposed method can be applied to the WSN reliability evaluation, which is a complex and uncertain problem. Finally, a simulation case study and an actual case study of wellhead blowout monitoring are conducted to verify the effectiveness of the proposed method. The reliability results of actual WSN are obtained by the standard testing method, where the loss and accuracy rates of the collected data are treated as the observation factors for obtaining the actual reliability values. The estimated results of hierarchical BRB model are very close to the actual reliability values, which shows that the proposed method can be used for evaluating the reliability of the actual WSN accurately.

WNBLI - A Multifarious Liability Examination for Wireless Sensor Networks

Wireless communications has completely revolutionized the living environment, just as personal computers forever altered the way of working in the 1980’s and the Internet dramatically changed the obtainment and accessing way of information in the 1990’s. Wireless sensor networks are evolving to a new level. The Failure liability is a very prominent problem in Wireless Sensor Network (WSN) research. Failure liable nodes behave in different form and this faulty behavior is referred to as multifarious faults or heterogeneous faults in WSN. This paper presents a Multifarious Failure liability Investigation protocol called Wireless Nodal Behavior and Liability Investigation (WNBLI) protocol for wireless sensor networks. The proposed WNBLI protocol consists of three vital phases, such as Association phase, Apprehension phase and Assortment phase. The performance of the proposed Wireless Nodal Behavior and Liability Investigation protocol is evaluated using network simulator.

Heterogeneous fault diagnosis for wireless sensor networks

Fault diagnosis has been considered as a very challenging problem in wireless sensor network (WSN) research. Faulty nodes having different behavior such as hard, soft, intermittent, and transient fault are called as heterogeneous faults in wireless sensor networks. This paper presents a heterogeneous fault diagnosis protocol for wireless sensor networks. The proposed protocol consists of three phases, such as clustering phase, fault detection phase, and fault classification phase to diagnose the heterogeneous faulty nodes in the wireless sensor networks. The protocol strategy is based on time out mechanism to detect the hard faulty nodes, and analysis of variance method (ANOVA test) to detect the soft, intermittent, and transient faulty nodes in the network. The feed forward probabilistic neural network (PNN) technique is used to classify the different types of faulty nodes in the network. The performance of the proposed heterogeneous fault diagnosis protocol is evaluated using network simulator NS-2.35. The evaluation of the proposed model is also carried out by the testbed experiment in an indoor laboratory environment and outdoor environment.

Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis

Wireless sensor networks (WSNs) have recently received increasing attention in the areas of defense and civil applications of sensor networks. Automatic WSN fault detection and diagnosis is essential to assure system's reliability. Proactive WSNs fault diagnosis approaches use embedded functions scanning sensor node periodically for monitoring the health condition of WSNs. But this approach may speed up the depletion of limited energy in each sensor node. Thus, there is an increasing interest in using passive diagnosis approach. In this paper, WSN anomaly detection model based on autoregressive (AR) model and Kuiper test-based passive diagnosis is proposed. First, AR model with optimal order is developed based on the normal working condition of WSNs using Akaike information criterion. The AR model then acts as a filter to process the future incoming signal from different unknown conditions. A health indicator based on Kuiper test, which is used to test the similarity between the training error of normal condition and residual of test conditions, is derived for indicating the health conditions of WSN. In this study, synthetic WSNs data under different cases/conditions were generated and used for validating the approach. Experimental results show that the proposed approach could differentiate WSNs normal conditions from faulty conditions. At last, the overall results presented in this paper demonstrate that our approach is effective for performing WSNs anomalies detection.

Failure detection in wireless sensor networks

Wireless Sensor Network (WSN) technology has recently moved out of controlled laboratory settings to real-world deployments. Many of these deployments experience high rates of failure. Common types of failure include node failure, link failure, and node reboot. Due to the resource constraints of sensor nodes, existing techniques for fault detection in enterprise networks are not applicable. Previously proposed WSN fault detection algorithms either rely on periodic transmission of node status data or inferring node status based on passive information collection. The former approach significantly reduces network lifetime, while the latter achieves poor accuracy in dynamic or large networks. Herein, we propose Sequence-Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs. The framework exploits in-network packet tagging using the Fletcher checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the sink. The sink monitors the extracted packet paths to detect persistent path changes which are indicative of network failures. When a failure is suspected, the sink uses control messages to check the status of the affected nodes. SBFD was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The method was found to achieve a fault detection accuracy of 90.7% to 95.0% for networks of 25 to 400 nodes at the cost of 0.164% to 0.239% additional control packets and a 0.5% reduction in node lifetime due to in-network packet tagging. Finally, a comparative study was conducted with existing solutions.

Wireless sensor network fault tolerant topology for lifetime optimization

Because of the limited energy of wireless sensor network, the optimization of network lifetime becomes a primary problem in the design of the network. Through analyzing the node residual energy and the load effect on the node lifetime, the fault tolerant topology evolution model in wireless sensor networks is put forward for extending lifetime. And the scope of load adjustment coefficient is obtained under the condition of network survival time. The simulation results show that the topological structure based on the evolution of the scale free network has the good fault tolerance characteristics. And it can also balance the network node energy consumption and the lifetime extension of the network.

IF: isolating fault nodes with mobile scanner

SUMMARYFault diagnosis plays an important role on stable applications maintaining in wireless sensor networks (WSNs). Most existing researchers are concerned about the statics nodes‐based diagnosis techniques, which are incompetent to cope with those dynamically occurring faults. In this paper, we propose a novel mechanism isolating fault (IF) nodes that employs the mobile node as a scanner to diagnose the faults in WSNs. The mobile scanner can detect the fault nodes within its communication region by using sniffer. It can also control the transmitting route of nodes by modifying their links. Starting from the base station, the mobile scanner periodically selects and visits all monitoring stations and consequently explores all static nodes in the monitoring area. After detecting a fault node, the mobile scanner will prevent it from connecting to the other normal nodes. We aim at finding the least number of monitoring stations and discovering an optimal route. We first formulate the two research issues to hitting set problem and travel salesperson problem. We then solve these two nondeterministic polynomial time hard problems by proposing an approximate algorithm, weighted, and hierarchical IF, which only uses local information. The mobile scanner makes a decision by weighing both the significance and the priority of nodes level by level. The proposed mechanism IF maintains the stabile service of WSNs in a transparent manner. The simulation results show that our algorithm can effectively isolate fault nodes, alleviate the damages of network corrupting, and hence increase the resilience of WSNs. Copyright © 2013 John Wiley & Sons, Ltd.

Wireless Sensor Network Fault Diagnosis Method of Optimization Research and Simulation

The traditional method of wireless sensor network fault diagnosis based on linear modeling rather than actually complicated and nod-linear relationship results in error data and leads to wrong decisions, therefore, this paper presents a sensor fault diagnosis method based on ARIMA and LSSVM integration control system which used to diagnosis sensor fault whose results are input LSSVM to fusion and get the final results of fault diagnosis. The simulation test results show that the proposed method improves the sensor node fault diagnosis accuracy, reduce the false negative rate and false positive rate.