Design and Evaluation of Online Fault Diagnosis Protocols forwireless Networks

Any node in a network, or a component of it may fail and show undesirable behavior due to physical defects, imperfections, or hardware and/or software related glitches. Presence of faulty hosts in the network affects the computational efficiency, and quality of service (QoS). This calls for the development of efficient fault diagnosis protocols to detect and handle faulty hosts. Fault diagnosis protocols designed for wired networks cannot directly be propagated to wireless networks, due to difference in characteristics, and requirements. This thesis work unravels system level fault diagnosis protocols for wireless networks, particularly for Mobile ad hoc Networks (MANETs), and Wireless Sensor Networks (WSNs), considering faults based on their persistence (permanent, intermittent, and transient), and node mobility. Based on the comparisons of outcomes of the same tasks (comparison model ), a distributed diagnosis protocol has been proposed for static topology MANETs, where a node requires to respond to only one test request from its neighbors, that reduces the communication complexity of the diagnosis process. A novel approach to handle more intractable intermittent faults in dynamic topology MANETs is also discussed.Based on the spatial correlation of sensor measurements, a distributed fault diagnosis protocol is developed to classify the nodes to be fault-free, permanently faulty, or intermittently faulty, in WSNs. The nodes affected by transient faults are often considered fault-free, and should not be isolated from the network. Keeping this objective in mind, we have developed a diagnosis algorithm for WSNs to discriminate transient faults from intermittent and permanent faults. After each node finds the status of all 1-hop neighbors (local diagnostic view), these views are disseminated among the fault-free nodes to deduce the fault status of all nodes in the network (global diagnostic view). A spanning tree based dissemination strategy is adopted, instead of conventional flooding, to have less communication complexity. Analytically, the proposed protocols are shown to be correct, and complete. The protocols are implemented using INET-20111118 (for MANETs) and Castalia-3.2 (forWSNs) on OMNeT++ 4.2 platform. The obtained simulation results for accuracy and false alarm rate vouch the feasibility and efficiency of the proposed algorithms over existing landmark protocols

A survey on fault diagnosis in wireless sensor networks

Wireless sensor networks (WSNs) often consist of hundreds of sensor nodes that may be deployed in relatively harsh and complex environments. In views of hardware cost, sensor nodes always adopt relatively cheap chips, which makes these nodes become error-prone or faulty in the course of their operation. Natural factors and electromagnetic interference could also influence the performance of the WSNs. When sensor nodes become faulty, they may have died which means they cannot communicate with other members in the wireless network, they may be still alive but produce incorrect data, they may be unstable jumping between normal state and faulty state. To improve data quality, shorten response time, strengthen network security, and prolong network lifespan, many studies have focused on fault diagnosis. This survey paper classifies fault diagnosis methods in recent five years into three categories based on decision centers and key attributes of employed algorithms: centralized approaches, distributed approaches, and hybrid approaches. As all these studies have specific goals and limitations, this paper tries to compare them, lists their merits and limits, and propose potential research directions based on established methods and theories

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%

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%

Cooperative fault detection and isolation in a surveillance sensor network: a case study

International audienceThis work focuses on Fault Detection and Isolation (FDI) among sensors of a surveillance network. A review of the main characteristics of faults in sensor networks and the associated diagnosis techniques is first proposed. An extensive study has then been performed on the case study of the persistent monitoring of an area by a sensor network which provides binary measurements of the occurrence of events to be detected (intrusions). The performance of a reference FDI method with and without simultaneous intrusions has been quantified through Monte Carlo simulations. The combination of static and mobile sensors has also been considered and shows a significant performance improvement for the detection of faults and intrusions in this context

An Adaptive Fault-Tolerant Event Detection Scheme for Wireless Sensor Networks

In this paper, we present an adaptive fault-tolerant event detection scheme for wireless sensor networks. Each sensor node detects an event locally in a distributed manner by using the sensor readings of its neighboring nodes. Confidence levels of sensor nodes are used to dynamically adjust the threshold for decision making, resulting in consistent performance even with increasing number of faulty nodes. In addition, the scheme employs a moving average filter to tolerate most transient faults in sensor readings, reducing the effective fault probability. Only three bits of data are exchanged to reduce the communication overhead in detecting events. Simulation results show that event detection accuracy and false alarm rate are kept very high and low, respectively, even in the case where 50% of the sensor nodes are faulty

A Survey on Fault Tolerance Techniques for Wireless Vehicular Networks

Future intelligent transportation systems (ITS) hold the promise of supporting the operation of safety-critical applications, such as cooperative self-driving cars. For that purpose, the communications among vehicles and with the road-side infrastructure will need to fulfil the strict real-time guarantees and challenging dependability requirements. These safety requisites are particularly important in wireless vehicular networks, where road traffic presents several threats to human life. This paper presents a systematic survey on fault tolerance techniques in the area of vehicular communications. The work provides a literature review of publications in journals and conferences proceedings, available through a set of different search databases (IEEE Xplore, Web of Science, Scopus and ScienceDirect). A systematic method, based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) Statement was conducted in order to identify the relevant papers for this survey. After that, the selected articles were analysed and categorised according to the type of redundancy, corresponding to three main groups (temporal, spatial and information redundancy). Finally, a comparison of the core features among the different solutions is presented, together with a brief discussion regarding the main drawbacks of the existing solutions, as well as the necessary steps to provide an integrated fault-tolerant approach to the future vehicular communications systems

Distributed Self Fault Diagnosis in Wireless Sensor Networks using Statistical Methods

Wireless sensor networks (WSNs) are widely used in various real life applications where the sensor nodes are randomly deployed in hostile, human inaccessible and adversarial environments. One major research focus in wireless sensor networks in the past decades has been to diagnose the sensor nodes to identify their fault status. This helps to provide continuous service of the network despite the occurrence of failure due to environmental conditions. Some of the burning issues related to fault diagnosis in wireless sensor networks have been addressed in this thesis mainly focusing on improvement of diagnostic accuracy, reduction of communication overhead and latency, and robustness to erroneous data by using statistical methods. All the proposed algorithms are evaluated analytically and implemented in standard network simulator NS3 (version 3.19). A distributed self fault diagnosis algorithm using neighbor coordination (DSFDNC) is proposed to identify both hard and soft faulty sensor nodes in wireless sensor networks. The algorithm is distributed (runs in each sensor node), self diagnosable (each node identifies its fault status) and can diagnose the most common faults like stuck at zero, stuck at one, random data and hard faults. In this algorithm, each sensor node gathered the observed data from the neighbors and computes the mean to check the presence of faulty sensor node. If a node diagnoses a faulty sensor node in the neighbors, then it compares observed data with the data of the neighbors and predicts its probable fault status. The final fault status is determined by diffusing the fault information obtained from the neighbors. The accuracy and completeness of the algorithm are verified based on the statistical analysis over sensors data. The performance parameters such as diagnosis accuracy, false alarm rate, false positive rate, total number of message exchanges, energy consumption, network life time, and diagnosis latency of the DSFDNC algorithm are determined for different fault probabilities and average degrees and compared with existing distributed fault diagnosis algorithms. To enhance the diagnosis accuracy, another self fault diagnosis algorithm is proposed based on hypothesis testing (DSFDHT) using the neighbor coordination approach. The Newman-Pearson hypothesis test is used to diagnose the soft fault status of each sensor node along with the neighbors. The algorithm can diagnose the faulty sensor node when the average degree of the network is less. The diagnosis accuracy, false alarm rate and false positive rate performance of the DSFDHT algorithm are improved over DSFDNC for sparse wireless sensor networks by keeping other performance parameters nearly same. The classical methods for fault finding using mean, median, majority voting and hypothesis testing are not suitable for large scale wireless sensor networks due to large devi- ation in transmitted data by faulty sensor nodes. Therefore, a modified three sigma edit test based self fault diagnosis algorithm (DSFD3SET) is proposed which diagnoses in an efficient manner over a large scale wireless sensor networks. The diagnosis accuracy, false alarm rate, and false positive rate of the proposed algorithm improve as compared to that of the DSFDNC and DSFDHT algorithms. The algorithm enhances the total number of message exchanges, energy consumption, network life time, and diagnosis latency, because the proposed algorithm needs less number of message exchanges over the algorithms such as DSFDNC and DSFDHT. In the DSFDNC, DSFDHT and DSFD3SET algorithms, the faulty sensor nodes are considered as soft faulty nodes which behave permanently. However in wireless sensor networks, the sensor nodes behave either fault free or faulty during different periods of time and are considered as intermittent faulty sensor nodes. Diagnosing intermittent faulty sensor nodes in wireless sensor networks is a challenging problem, because of inconsistent result patterns generated by the sensor nodes. The traditional distributed fault diagnosis (DIFD) algorithms consume more message exchanges to obtain the global fault status of the network. To optimize the number of message exchanges over the network, a self fault diagnosis algorithm is proposed here, which repeatedly conducts the self fault diagnosis procedure based on the modified three sigma edit test over a duration to identify the intermittent faulty sensor nodes. The algorithm needs less number of iterations to identify the intermittent faulty sensor nodes. The simulation results show that, the performance of the HISFD3SET algorithm improves in diagnosis accuracy, false alarm rate and false positive rate over the DIFD algorith

Distributed Clustering-based Sensor Fault Diagnosis for HVAC Systems

We design a Sensor Fault Detection and Isolation architecture for an IWSN monitoring an HVAC System, based on a clustering approach

GRU-based denoising autoencoder for detection and clustering of unknown single and concurrent faults during system integration testing of automotive software systems

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques