Статті в журналах: "FAULTY NODE DETECTION"

1

Panda, Meenakshi, and P. M. Khilar. "Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks." Journal of Computer Networks and Communications 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/323754.

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A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.

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2

Bae, Jangsik, Meonghun Lee, and Changsun Shin. "A Data-Based Fault-Detection Model for Wireless Sensor Networks." Sustainability 11, no. 21 (November 5, 2019): 6171. http://dx.doi.org/10.3390/su11216171.

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With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.

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3

Mahapatro, Arunanshu, and Pabitra Mohan Khilar. "An Adaptive Approach to Discriminate the Persistence of Faults in Wireless Sensor Networks." ISRN Sensor Networks 2012 (October 14, 2012): 1–13. http://dx.doi.org/10.5402/2012/342461.

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This paper presents a parametric fault detection algorithm which can discriminate the persistence (permanent, intermittent, and transient) of faults in wireless sensor networks. The main characteristics of these faults are the amount the fault appears. We adopt this state-holding time to discriminate transient from intermittent faults. Neighbor-coordination-based approach is adopted, where faulty sensor nodes are detected based on comparisons between neighboring nodes and dissemination of the decision made at each node. Simulation results demonstrate the robustness of the work at varying transient fault rate.

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4

Xu, Xiaowei, Fangrong Zhou, Yongjie Nie, Wenhua Xu, Ke Wang, Jian OuYang, Kaihong Zhou, Shan Chen, and Yiming Han. "Fault Detection and Location of 35 kV Single-Ended Radial Distribution Network Based on Traveling Wave Detection Method." Processes 11, no. 8 (August 19, 2023): 2494. http://dx.doi.org/10.3390/pr11082494.

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With the progress of society and the iterative improvement of infrastructure construction, the power grid transmission lines have also entered an era of intelligence. The national distribution system has made ensuring the regular operation of the distribution network as well as prompting troubleshooting and detection its top priority. Research on fault diagnosis for 35 kV single-ended radial distribution networks is still in its infancy compared to other hot topics in the industry, such as short-circuit fault detection and fault node localization. This study adopts the 35 kV single-ended radial distribution network as a model, detects fault lines via the traveling wave method, and accurately locates fault nodes using the wavelet conversion method, hoping to quickly identify and locate fault nodes in distribution networks. The experimental results demonstrate that the research method can quickly identify the faulty line and carry out further fault node location detection. The final obtained fault distance is 1.19 km with an actual error of only 0.16 km; the maximum relative errors are only 0.33 km and 0.21 km when the initial phase angle and transition resistance parameters are changed, respectively; and the error amplitude fluctuations are essentially stable. The experimental results also demonstrate that the research method can quickly identify the faulty line and carry out further fault node location.

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5

Islampurkar, Mangesh, Kishanprasad Gunale, Sunil Somani, and Nikhil Bagade. "Multiple Stuck At Fault Diagnosis System For Digital Circuit On FPGA Using Vedic Multiplier and ANN." International Journal of Circuits, Systems and Signal Processing 16 (May 30, 2022): 985–92. http://dx.doi.org/10.46300/9106.2022.16.120.

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In an electronics circuit, the presence of a Fault leads to undesired or unexpected results. The output of many nodes on the circuit is changed due to the presence of the Fault at one node. So, it is necessary to detect the nature of the Fault present in a particular faulty node. To detect the fault present in the digital circuit, it is necessary to understand logical behavior using mathematical modeling. After the successful modeling, parameters are extracted, and the database is generated. The mathematical model uses Hebbian Artificial Neural Network algorithms [1] [2]. The database generated is used by the fault detection system to find the masked and multiple faults. A fault detection system monitors the faults present in the test circuit and finds the origin and nature of the Fault [3] [4]. The database generated for single stuck-at faults is used to find the multiple faults present in the faulty circuit. In this paper, Modified Vedic Multiplication [5] [4] method is used to optimize the utilization of the proposed system. In this proposed design multiplier of {N x N} bit input and {N} bit output is used, due to which device utilization is decreased, which is the expected outcome from the design. This system is designed using ISE Design Suite and implemented on Spartan-6 FPGA [6] [7].

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6

Sun, Yin Qiu, and Hai Lin Feng. "Intermittent Faults Diagnosis in Wireless Sensor Networks." Applied Mechanics and Materials 160 (March 2012): 318–22. http://dx.doi.org/10.4028/www.scientific.net/amm.160.318.

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Sensor node intermittent faults which sometimes behave as fault-free are common in wireless sensor networks. Intermittent faults also affect network performance and faults detection accuracy, so it is important to diagnose the intermittent faulty nodes accurately. This paper proposes a distributed clustering intermittent faults diagnosis method. First, the network is divided into several clusters with the cluster heads should be diagnosed as good. Then, the cluster members are diagnosed by their cluster head. In order to improve the validity of proposed diagnose method, a strategy which collect data for many times is adopted. Analysis of fault diagnosable is given, and simulation results indicate the proposed algorithm has high fault detection accuracy.

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7

Liu, Kezhong, Yang Zhuang, Zhibo Wang, and Jie Ma. "Spatiotemporal Correlation Based Fault-Tolerant Event Detection in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/643570.

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Reliable event detection is one of the most important objectives in wireless sensor networks (WSNs), especially in the presence of faulty nodes. Existing fault-tolerant event detection approaches usually take the probability of faulty nodes into account and fusion techniques to weaken the influence of faulty readings are usually developed. Through extensive experiments, we discover a phenomenon that event detection accuracy degrades quickly when the faulty sensors ratio reaches a critical value. This problem has not drawn enough attention and a solution to the problem is our concern. In this paper, a spatiotemporal correlation based fault-tolerant event detection scheme (STFTED) is proposed, which leverages a two-stage decision fusion and spatiotemporal correlation to improve the event detection quality. In the low-level local stage, a location-based weighted voting scheme (LWVS) is developed to make decision fusion locally on each sensor node, which is based on neighboring nodes and the geographical distributions of two decision quorums. In the high-level global stage, a Bayesian fusion algorithm is adopted to reach a consensus among individual detection decisions made by sensor nodes. Simulation results demonstrate that the proposed approach is highly effective and a better quality of event detection can be obtained compared with the optimal threshold decision schemes (OTDS).

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8

Saihi, Marwa, Ahmed Zouinkhi, Boumedyen Boussaid, Mohamed Naceur Abdelkarim, and Guillaume Andrieux. "Hidden Gaussian Markov model for distributed fault detection in wireless sensor networks." Transactions of the Institute of Measurement and Control 40, no. 6 (March 15, 2017): 1788–98. http://dx.doi.org/10.1177/0142331217691334.

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Wireless sensor networks are based on a large number of sensor nodes used to measure information like temperature, acceleration, displacement, or pressure. The measurements are used to estimate the state of the monitored system or area. However, the quality of the measurements must be guaranteed to ensure the reliability of the estimated state of the system. Actually, sensors can be used in a hostile environment such as, on a battle field in the presence of fires, floods, earthquakes. In these environments as well as in normal operation, sensors can fail. The failure of sensor nodes can also be caused by other factors like: the failure of a module (such as the sensing module) due to the fabrication process models, loss of battery power and so on. A wireless sensor network must be able to identify faulty nodes. Therefore, we propose a probabilistic approach based on the Hidden Markov Model to identify faulty sensor nodes. Our proposed approach predicts the future state of each node from its actual state, so the fault could be detected before it occurs. We use an aided judgment of neighbour sensor nodes in the network. The algorithm analyses the correlation of the sensors’ data with respect to its neighbourhood. A systematic approach to divide a network on cliques is proposed to fully draw the neighbourhood of each node in the network. After drawing the neighbourhood of each node (cliques), damaged cliques are identified using the Gaussian distribution theorem. Finally, we use the Hidden Markov Model to identify faulty nodes in the identified damaged cliques by calculating the probability of each node to stay in its normal state. Simulation results demonstrate our algorithm is efficient even for a huge wireless sensor network unlike previous approaches.

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9

Duche, R. N., and N. P. Sarwade. "Faulty Sensor Node Detection Using Round Trip Time and Discrete Paths in WSNs." ISRN Sensor Networks 2013 (September 23, 2013): 1–12. http://dx.doi.org/10.1155/2013/941489.

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Wireless sensor networks (WSNs) with efficient and accurate design to increase the quality of service (QoS) have become a hot area of research. Implementing the efficient and accurate WSNs requires deployment of the large numbers of portable sensor nodes in the field. The quality of service of such networks is affected by lifetime and failure of sensor node. In order to improve the quality of service, the data from faulty sensor nodes has to be ignored or discarded in the decision-making process. Hence, detection of faulty sensor node is of prime importance. In the proposed method, discrete round trip paths (RTPs) are compared on the basis of round trip delay (RTD) time to detect the faulty sensor node. RTD protocol is implemented in NS2 software. WSNs with circular topology are simulated to determine the RTD time of discrete RTPs. Scalability of the proposed method is verified by simulating the WSNs with various sensor nodes.

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10

V, Bindhu, and Ranganathan G. "Effective Automatic Fault Detection in Transmission Lines by Hybrid Model of Authorization and Distance Calculation through Impedance Variation." March 2021 3, no. 1 (March 27, 2021): 36–48. http://dx.doi.org/10.36548/jei.2021.1.004.

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Fault detection in the transmission is a challenging task when examining the accuracy of the system. This fault can be caused by a man-made force or by using concurrent overvoltage in the power distribution line. This research focuses on two sections to handle the power transmission line problem and can be rectified as previously stated. An intelligent approach is utilized for monitoring and controlling line faults in order to improve the accuracy of the equipment in transmission line fault detection. After several iterations of the procedure, the combination of line and master unit improves the system's accuracy and reliability. The master unit identifies faulty poles in the network based on the variation of current and voltage of each node and calculates the distance between the station and the faulty node to reduce manual effort. In the proposed work, many sensors are used to detect the line fault in a network by placing the appropriate point. The pure information can be transferred to an authorized person or unit after many iterations due to knowledgeable devices. The faulty status of the pole information is displayed in the control unit by a display unit comprised of an alarm unit to alert the corresponding section using ZigBee techniques. The GSM unit provides the faulty status of an authorized person to rectify the problems immediately which further improve the reliability of the system. When compared to existing methods, our hybrid proposed method achieves a higher accuracy of 90%. This method aids to reduce the labor costs gradually to visit all-pole points instead of faulty pole points and thereby increasing the reliability of the electrical consumers.

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11

Khavari, Saeid, Rahman Dashti, Hamid Reza Shaker, and Athila Santos. "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers." Energies 13, no. 9 (May 7, 2020): 2331. http://dx.doi.org/10.3390/en13092331.

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Power distribution networks are vulnerable to different faults, which compromise the grid performance and need to be managed effectively. Automatic and accurate fault detection and location are key components of effective fault management. This paper proposes a new framework for fault detection and location for smart distribution networks that are equipped with data loggers. The framework supports networks with mixed overhead lines and underground cables. The proposed framework consists of area detection, faulty section identification, and high impedance fault location. Firstly, the faulty zone and section are detected based on the operation of over-current relays and digital fault recorders. Then, by comparing the recorded traveling times at both ends of lines, which are related to the protection zone, the faulty line is identified. In the last step, the location of the fault is estimated based on discrete wavelet transform. The proposed method is tested on a 20 kV 13 node network, which is composed of overhead lines and underground cables. The method is tested in both balanced and unbalanced configurations. The obtained results confirm the advantages of the proposed method compared with the current state-of-the art.

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12

Amarasimha T. and V. Srinivasa Rao. "Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks." International Journal of Grid and High Performance Computing 13, no. 2 (April 2021): 1–20. http://dx.doi.org/10.4018/ijghpc.2021040101.

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Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.

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13

Owen, Donald E., Jithin Joseph, Jim Plusquellic, Tom J. Mannos, and Brian Dziki. "Node Monitoring as a Fault Detection Countermeasure against Information Leakage within a RISC-V Microprocessor." Cryptography 6, no. 3 (August 3, 2022): 38. http://dx.doi.org/10.3390/cryptography6030038.

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Advanced, superscalar microprocessors (μP) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the μP. Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The μP designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the μPs.

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14

Shao, Qing, Jia Lin Ma, and Yun Wei. "Study of Fault Detection Based on Benzene Ring in Wireless Sensor Networks." Advanced Engineering Forum 6-7 (September 2012): 1167–72. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1167.

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Large scale and limited resources of wireless sensor networks(WSNs) increase the need to cope with node failures. This paper proposes a novel mechanism for the detection of node failures in WSNs. We designed a structure of benzene ring with symmetry. A benzene ring consists of a center node and several child nodes. Reliable communication links among these nodes. Each node maintains a list of its neighbors. The center node delegates detection service and make decisions about faulty nodes based on its child nodes’ available resources. Compared with other solutions, our mechanism can reduce energy consumption, decrease the number of neighborhoods and extend network lifetime. Simulation experiments show the performance and viability of the mechanism.

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15

Ocak, Hasan, and Kenneth A. Loparo. "HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings." Journal of Vibration and Acoustics 127, no. 4 (September 23, 2004): 299–306. http://dx.doi.org/10.1115/1.1924636.

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In this paper, we introduce a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. Features extracted from amplitude demodulated vibration signals from both normal and faulty bearings were used to train HMMs to represent various bearing conditions. The features were based on the reflection coefficients of the polynomial transfer function of an autoregressive model of the vibration signals. Faults can be detected online by monitoring the probabilities of the pretrained HMM for the normal case given the features extracted from the vibration signals. The new technique also allows for diagnosis of the type of bearing fault by selecting the HMM with the highest probability. The new scheme was also adapted to diagnose multiple bearing faults. In this adapted scheme, features were based on the selected node energies of a wavelet packet decomposition of the vibration signal. For each fault, a different set of nodes, which correlates with the fault, is chosen. Both schemes were tested with experimental data collected from an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2 HP IQPreAlert) driven mechanical system and have proven to be very accurate.

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16

Karmakar, Koushik, Sohail Saif, Suparna Biswas, and Sarmistha Neogy. "A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN." International Journal of Biomedical and Clinical Engineering 10, no. 2 (July 2021): 44–65. http://dx.doi.org/10.4018/ijbce.2021070104.

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Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.

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17

Tariq, Rizwan, Ibrahim Alhamrouni, Ateeq Ur Rehman, Elsayed Tag Eldin, Muhammad Shafiq, Nivin A. Ghamry, and Habib Hamam. "An Optimized Solution for Fault Detection and Location in Underground Cables Based on Traveling Waves." Energies 15, no. 17 (September 5, 2022): 6468. http://dx.doi.org/10.3390/en15176468.

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Faults in the power system affect the reliability, safety, and stability. Power-distribution systems are familiar with the different faults that can damage the overall performance of the entire system, from which they need to be effectively cleared. Underground power systems are more complex and require extra accuracy in fault detection and location for optimum fault management. Slow processing and the unavailability of a protection zone for relay coordination are concerns in fault detection and location, as these reduce the performance of power-protection systems. In this regard, this article proposes an optimized solution for a fault detection and location framework for underground cables based on a discrete wavelet transform (DWT). The proposed model supports area detection, the identification of faulty sections, and fault location. To overcome the abovementioned facts, we optimize the relay coordination for the overcurrent and timing relays. The proposed protection zone has two sequential stages for the current and time at which it optimizes the current and time settings of the connected relays through Newton–Raphson analysis (NRA). Moreover, the traveling times for the DWT are modeled, which relate to the protection zone provided by the relay coordination, and the faulty line that is identified as the relay protection is not overlapped. The model was tested for 132 kV/11 kV and 16-node networks for underground cables, and the obtained results show that the proposed model can detect and locate the cable’s faults speedily, as it detects the fault in 0.01 s, and at the accurate location. MATLAB/Simulink (DigSILENT Toolbox) is used to establish the underground network for fault location and detection.

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18

Gu, Rentao, Jiawen Qin, Tao Dong, Jie Yin, and Zhihui Liu. "Recovery Routing Based on Q-Learning for Satellite Network Faults." Complexity 2020 (September 4, 2020): 1–13. http://dx.doi.org/10.1155/2020/8829897.

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With the fierce research on the space and terrestrial network, the satellite network as the main component has received increasing attention. Due to its special operating environment, there are temporary link failures caused by interference and permanent port failures caused by equipment problems. In this paper, we propose a new satellite network routing technology for fault recovery based on fault detection. Based on Bayesian decision, this technology judges the probability of each fault by a priori probability of the two faults to achieve the purpose of effectively distinguishing between two types of faults and locate faulty links and node ports. Then, corresponding to the previous two stages of the fault detection, different stages and different methods are updated for different types of fault. We also combine satellite network data from satellite simulation software to validate our study. The results show that the recovery strategy has good performance, and the effective resource utilization rate is improved significantly.

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19

Kaur, Jaspreet, and Amit Kumar Bindal. "Dynamic Reconfiguration of Network Partition Under the Constraints of Hostile Environment." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2653–57. http://dx.doi.org/10.1166/jctn.2020.8961.

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Node failure may interrupt the smooth network operations of a sensor network. A faulty node may choke the entire network. Fault detection and recovery provisions may extend the network life span. In this paper, a recovery mechanism is proposed to handle the partitioned network using instant reconfiguration method based on minimum spanning tree. Performance of the proposed method is analyzed using different routing protocols (AODV/LEACH/DSDV) under the constraints of partitioned network.

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20

Raju, Chitra, Sudarmani Rajagopal, Kanagaraj Venusamy, Kannadhasan Suriyan, and Manjunathan Alagarsamy. "SDSFLF: fault localization framework for optical communication using software digital switching network." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (March 1, 2023): 113. http://dx.doi.org/10.11591/ijres.v12.i1.pp113-124.

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Optical network is an emerging technology for data communication inworldwide. The information is transmitted from the source to destination through the fiber optics. All optical network (AON) provides good transmission transparency, good expandability, large bandwidth, lower bit error rate (BER), and high processing speed. Link failure and node failure haveconsistently occurred in the traditional methods. In order to overcome the above mentioned issues, this paper proposes a robust software defined switching enabled fault localization framework (SDSFLF) to monitor the node and link failure in an AON. In this work, a novel faulty node localization (FNL) algorithm is exploited to locate the faulty node. Then, the software defined faulty link detection (SDFLD) algorithm that addresses the problem of link failure. The failures are localized in multi traffic stream (MTS) and multi agent system (MAS). Thus, the throughput is improved in SDSFLF compared than other existing methods like traditional routing and wavelength assignment (RWA), simulated annealing (SA) algorithm, attackaware RWA (A-RWA) convex, longest path first (LPF) ordering, and biggest source-destination node degree (BND) ordering. The performance of the proposed algorithm is evaluated in terms of network load, wavelength utilization, packet loss rate, and burst loss rate. Hence, proposed SDSFLF assures that high performance is achieved than other traditional techniques.

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21

Misbahuddin, Syed. "Fault Detection and Tolerance in Cluster of Workstations using Message Passing Interface." Sir Syed University Research Journal of Engineering & Technology 1, no. 1 (December 20, 2011): 4. http://dx.doi.org/10.33317/ssurj.72.

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A Cluster of Workstations (COW) is network based multi-computer system aimed to replace supercomputers. A cluster of workstations works on Divisible Load Theory (DLT) according to which a job is divided into n subtasks and delegated to n workstations in the COW architecture. To get the job completed, all subtasks must be completed. Therefore, for satisfactory job completion, all workstations must be functional. However, a faulty node can suspend the overall job completion task until and unless some fault avoidance and correction measures are taken. This paper presents a fault detection and fault tolerant algorithm which will use Message Passing Interface (MPI) to identify faulty workstations and transfer the subtask being performed by them to a normally working workstation. The assigned workstations will continue their original subtasks in addition to assigned subtasks on time sharing basis.

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Misbahuddin, Syed. "1 Fault Detection and Tolerance in Cluster of Workstations using Message Passing Interface." Sir Syed Research Journal of Engineering & Technology 1, no. 1 (December 20, 2011): 4. http://dx.doi.org/10.33317/ssurj.v1i1.72.

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A Cluster of Workstations (COW) is network based multi-computer system aimed to replace supercomputers. A cluster of workstations works on Divisible Load Theory (DLT) according to which a job is divided into n subtasks and delegated to n workstations in the COW architecture. To get the job completed, all subtasks must be completed. Therefore, for satisfactory job completion, all workstations must be functional. However, a faulty node can suspend the overall job completion task until and unless some fault avoidance and correction measures are taken. This paper presents a fault detection and fault tolerant algorithm which will use Message Passing Interface (MPI) to identify faulty workstations and transfer the subtask being performed by them to a normally working workstation. The assigned workstations will continue their original subtasks in addition to assigned subtasks on time sharing basis.

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23

Gaykar, Reshma S., Velu Khanaa, and Shashank D. Joshi. "Faulty Node Detection in HDFS Using Machine Learning Techniques." Revue d'Intelligence Artificielle 36, no. 4 (August 31, 2022): 553–60. http://dx.doi.org/10.18280/ria.360406.

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24

Prasetiadi, Agi, and Dong-Seong Kim. "Faulty Node Detection in Distributed Systems Using BCH Code." IEEE Communications Letters 17, no. 3 (March 2013): 620–23. http://dx.doi.org/10.1109/lcomm.2013.020413.122463.

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25

Patel, R. K., and V. K. Giri. "Condition monitoring of induction motor bearing based on bearing damage index." Archives of Electrical Engineering 66, no. 1 (March 1, 2017): 105–19. http://dx.doi.org/10.1515/aee-2017-0008.

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Abstract The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become “transient” in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an ‘index’ representing the health condition of rotating machines.

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Ur Rahman, Hafiz, Guojun Wang, Md Zakirul Alam Bhuiyan, and Jianer Chen. "In-network generalized trustworthy data collection for event detection in cyber-physical systems." PeerJ Computer Science 7 (May 4, 2021): e504. http://dx.doi.org/10.7717/peerj-cs.504.

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Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

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Men, Shaoyang, Pascal Chargé, and Sébastien Pillement. "A Robust and Energy Efficient Cooperative Spectrum Sensing Scheme in Cognitive Wireless Sensor Networks." Network Protocols and Algorithms 7, no. 3 (November 30, 2015): 140. http://dx.doi.org/10.5296/npa.v7i3.8254.

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Cooperative spectrum sensing (CSS) is able to effectively solve the hidden terminal, depth attenuation, multipath shadows and other issues which are not addressed by the single-user sensing. Therefore, it has attracted a large amount of interest and several CSS algorithms have been proposed. However, they are not specifically tailored for cognitive wireless sensor networks (CWSNs) where transmission reliability, power management and interference avoidance are critical issues. In this paper, we propose a robust and energy efficient CSS scheme in CWSNs. Firstly, taking into account the limited energy of sensor node, especially the mobile node, we introduce the nodes of the network into multiple clusters for the CSS in order to save energy consumed in reporting results and exchanging information and extend the lifetime of the network. Secondly, we consider that some cognitive nodes may not work as expected. Hence, facing the problem of faulty nodes in clusters, we propose an evaluation method which considers simultaneously the node reliability and the mutually supportive degree among different nodes to support adapted decisions. Finally, after removing the node of low credibility, the energy efficiency and reliability of each cluster are improved significantly. Simulation results allow to validate that the proposed method outperforms the state of the art in energy efficiency and detection reliability, even in presence of faulty nodes.

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Cui, Chongyu, Zhaoxia Li, Hongmei Wu, and Xiaojia Sun. "Key Technology of Wide-Area Backup Protection of Intelligent DC Power Networks Based on Fault Identification." Mobile Information Systems 2022 (August 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/9523208.

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As the size of the grid continues to grow, traditional backup protection is no longer sufficient to meet the needs of the grid in complex environments. The key to wide-area protection is the concept of making full use of the wide-area information measured in the grid to identify faults. This paper first describes the architecture of a wide-area defense team and then proposes a fault line detection algorithm based on normal fault assembly wide-area backup protection and a positive matrix based on spectral analysis. In the assembly-based positive sequence error discovery algorithm, the difference in the amount of fault judgment between line zone faults and out-of-zone faults is illustrated, and fault line detection criteria are given. In the algorithm based on random matrix spectrum analysis, the current error sequence of each pin of the circuit is copied, translated, and superimposed with noise, then the original random matrix is matrix transformed according to random matrix theory to obtain the fault identification matrix, and finally the average spectrum radius is calculated. Through the simulation of the IEEE10 machine 39-node standard test model, it is proved that the two algorithmic methods proposed in our article can accurately identify faulty lines under various fault conditions.

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Faisal, Syed Mohd, and Taskeen Zaidi. "Implementation of ACO in Vanet with Detection of Faulty Node." Indian Journal of Science and Technology 14, no. 19 (May 22, 2021): 1598–614. http://dx.doi.org/10.17485/ijst/v14i19.76.

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Kishor, Shinde. "Survey on Faulty Node Detection and Recovery Algorithm for WSN." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1361–66. http://dx.doi.org/10.17762/ijritcc2321-8169.1503100.

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Saleh, Alaa, Pallavi Joshi, Rajkumar Singh Rathore, and Sandeep Singh Sengar . "Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks." Sensors 22, no. 20 (October 14, 2022): 7820. http://dx.doi.org/10.3390/s22207820.

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Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster’s trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity.

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Prakash, Shelly, Dr Vaibhav Vyas, and Dr Anup Bhola. "Proactive Fault Tolerance using Heartbeat Strategy for Fault Detection." International Journal of Engineering and Advanced Technology 9, no. 1 (October 30, 2019): 4927–32. http://dx.doi.org/10.35940/ijeat.a2079.109119.

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Failure is something which causes services on the cloud to go down for some time period. Most of the times instead of recovery and repair, we opt for virtual machine migration where failover of the failed service is done on some other running virtual server so that the service is revived. Virtual migrations and recovery mechanisms consume a lot of energy and many approaches are implemented to make them energy efficient. Failure Detection is a topic of equal importance and comes under fault tolerance. Failure detection if done properly can be more effective and energy/cost saving than fault recovery. Heartbeat strategy is one such failure detection approach where live processes send an “I am alive” message to the host device at some pre-defined fixed intervals which ensures that the process is running fine. In this paper, we propose to mark the nodes whose processes have failed to send the heartbeat message and prepare a count (confidence factor, α) for the same. In primary testing, if this confidence factor reaches a specific threshold then that particular node is sent for confidence testing (second level failure detection testing using a different time sequence of heartbeat message arrival) and later marked for failure recovery (if found faulty). Fault recovery techniques are then applied to it so that it can be corrected and reused and the current jobs can be migrated to the better node during the recovery period. If the confidence factor, α is below the threshold value then no action is taken and only network parameters and connections can be rechecked. This method will re-ensure the trust on heartbeat strategy for fault detection and save the device from failure.

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Sakir, Riesa Krisna Astuti, Sanjay Bhardwaj, and Dong-Seong Kim. "Enhanced faulty node detection with interval weighting factor for distributed systems." Journal of Communications and Networks 23, no. 1 (February 2021): 34–42. http://dx.doi.org/10.23919/jcn.2021.000002.

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Harshavardhan, A. "AN EFFECTIVE IMPLEMENTATION OF FAULTY NODE DETECTION IN MOBILE WIRELESS NETWORK." International Journal of Advanced Research in Computer Science 8, no. 8 (August 30, 2017): 705–8. http://dx.doi.org/10.26483/ijarcs.v8i8.4877.

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Li, Wenjie, Laura Galluccio, Francesca Bassi, and Michel Kieffer. "Distributed Faulty Node Detection in Delay Tolerant Networks: Design and Analysis." IEEE Transactions on Mobile Computing 17, no. 4 (April 1, 2018): 831–44. http://dx.doi.org/10.1109/tmc.2017.2743703.

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Jamali, Saeed, Syed Bukhari, Muhammad Khan, Khawaja Mehmood, Muhammad Mehdi, Chul-Ho Noh, and Chul-Hwan Kim. "A High-Speed Fault Detection, Identification, and Isolation Method for a Last Mile Radial LVDC Distribution Network." Energies 11, no. 11 (October 25, 2018): 2901. http://dx.doi.org/10.3390/en11112901.

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The day-by-day increase in digital loads draws attention towards the need for an efficient and compatible distribution network. An LVDC distribution network has the capability to fulfill such digital load demands. However, the major challenge of an LVDC distribution network is its vulnerability during a fault. The need for a high-speed fault detection method is inevitable before it can be widely adopted. This paper proposes a new fault detection method which extracts the features of the current during a fault. The proposed fault detection method uses the merits of overcurrent, the first and second derivative of current, and signal processing techniques. Three different features are extracted from a time domain current signal through a sliding window. The extracted features are based upon the root squared zero, second, and fourth order moments. The features are then set with individual thresholds to discriminate low-, high-, and very high-resistance faults. Furthermore, a fault is located through the superimposed power flow. Moreover, this study proposes a new method based on the vector sum of positive and negative pole currents to identify the faulty pole. The proposed scheme is verified by using a modified IEEE 13 node distribution network, which is implemented in Matlab/Simulink. The simulation results confirm the effectiveness of the proposed fault detection and identification method. The simulation results also confirm that a fault having a resistance of 1 m Ω is detected and interrupted within 250 μ s for the test system used in this study.

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Gutiérrez, Sebastián, and Hiram Ponce. "An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions." Sensors 19, no. 4 (February 19, 2019): 854. http://dx.doi.org/10.3390/s19040854.

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Wireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for the effective functioning of the sensor network. In this paper, we propose a supervised learning method that is named artificial hydrocarbon networks (AHN), to predict temperature in a remote location and detect failures in sensors. It allows predicting the temperature and detecting failure in sensor node of remote locations using information from a web service comparing it with field temperature sensors. For experimentation, we implemented a small WSN to test our sensor in order to measure failure detection, identification and accommodation proposal. In our experiments, 94.18% of the testing data were recovered and accommodated allowing of validation our proposed approach that is based on AHN, which detects, identify and accommodate sensor failures accurately.

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Gaykar, Reshma S., Velu Khanaa, and Shashank D. Joshi. "Mapping of Virtual Machines Using Machine Learning Algorithms for Detection of Faulty Nodes." International Journal of Safety and Security Engineering 12, no. 6 (December 31, 2022): 681–90. http://dx.doi.org/10.18280/ijsse.120603.

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A distributed system is characterized by a large number of nodes that are linked to a network and are mostly used for transaction processing. Large set of users are likely to communicate information over the network to the nodes, consistency and dependability remain a critical problem in the distributed environments. Independent failure of the component is one of the major problems in the distributed systems as it slowly impacts the performance of the other nodes in the system. The quality of service - QoS of a distributed network may be improved by a quick way of detecting problematic nodes. Sometime heavy nodes required high computation for transaction processing while idle nodes take low computation. In this paper, we proposed identification of straggler nodes in distributed environment with the help of hybrid machine learning algorithm. The work basically carried out to set up of large number of virtual machines and collect current log audits of each VM. According to the available parameters of audit files to each machine, algorithms decide that specific node is overheated or ideal condition. In expensive experimental analysis we demonstrate a accuracy of proposed hybrid machine learning algorithm. The proposed algorithm produces higher precision up to 4.5% than state-of-art methods. Key highlights of the VM mapping strategy were also investigated through a scrutiny of ongoing contracts. Main focus remains on machine learning (ML) to distinguish PM (Physical Machine) congestion, determining VMs from crowded PMs, and VM conditions as major exercises. This paper aims to review and characterize research on the planning and status of VMs that use ML using asset usage history. Energy productivity, VM migration, and quality of service were the main exhibition boundaries used to investigate cloud data center presentations.

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39

Palanikumar, R., and K. Ramasamy. "Software defined network based self-diagnosing faulty node detection scheme for surveillance applications." Computer Communications 152 (February 2020): 333–37. http://dx.doi.org/10.1016/j.comcom.2019.12.034.

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Das, Soumya, and Tamaghna Acharya. "Faulty Node Detection in HMM-Based Cooperative Spectrum Sensing For Cognitive Radio Networks." Computer Journal 61, no. 10 (January 6, 2018): 1468–78. http://dx.doi.org/10.1093/comjnl/bxx127.

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Malik, Tauqeer Safdar, Jawad Tanveer, Shahid Anwar, Muhammad Rafiq Mufti, Humaira Afzal, and Ajung Kim. "An Efficient and Secure Fog Based Routing Mechanism in IoT Network." Mathematics 11, no. 17 (August 24, 2023): 3652. http://dx.doi.org/10.3390/math11173652.

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The Internet of Things (IoT) networks are the most prone to internal as well as external attacks in addition to energy consumption problems. Conventional security solutions are not able to address these issues effectively due to the limited resources of sensor nodes participating in IoT communications. In this work, an Efficient and Secure Fog Based Routing Mechanism (ESFRM) is proposed to protect the network from faulty internal as well as external attacks. Every node participating in IoT communications calculates the comprehensive trust value of the next intermediate node which is the addition of direct trust, indirect trust and energy trust values before forwarding the data. This comprehensive trust value is then compared with the comprehensive threshold trust value to decide whether the particular node is a rogue node or a valid normal node. Further, an enhanced RSA (Rivest, Shamir, Adleman) algorithm is implemented to provide three levels of data security from Cluster Head (CH) to fog node, from fog node to cloud server and directly from CH to cloud server. For this purpose, an efficient CH selection technique is also proposed in this work. The proposed methodology is compared with the Secure Energy-efficient Fog-based Routing (SEFR) protocol and Trust-aware Secure Routing Protocol (TSRP). The evaluation results show that the proposed ESFRM outperforms the conventional schemes with respect to energy consumption, malicious node detection and transmission rate.

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Mirshekali, Hamid, Rahman Dashti, Ahmad Keshavarz, and Hamid Reza Shaker. "Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU." Sensors 22, no. 3 (January 26, 2022): 945. http://dx.doi.org/10.3390/s22030945.

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Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.

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Noh, Dong-Hee, and Dong-Seong Kim. "Real-time Faulty Node Detection scheme in Naval Distributed Control Networks using BCH codes." Journal of the Institute of Electronics and Information Engineers 51, no. 5 (May 25, 2014): 20–28. http://dx.doi.org/10.5573/ieie.2014.51.5.020.

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Feiyue, Yang, Tao Yang, Zhang Siqing, Dai Jianjian, Xu Juan, and Hou Yao. "A Faulty Node Detection Algorithm based on Spatial-temporal Cooperation in Wireless Sensor Networks*." Procedia Computer Science 131 (2018): 1089–94. http://dx.doi.org/10.1016/j.procs.2018.04.266.

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Masdari, Mohammad, and Suat Özdemir. "Towards Coverage-Aware Fuzzy Logic-Based Faulty Node Detection in Heterogeneous Wireless Sensor Networks." Wireless Personal Communications 111, no. 1 (October 30, 2019): 581–610. http://dx.doi.org/10.1007/s11277-019-06875-0.

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Noh, Dong-Hee, and Dong-Seong Kim. "Markov Model-Driven in Real-time Faulty Node Detection for Naval Distributed Control Networked Systems." Journal of Institute of Control, Robotics and Systems 20, no. 11 (November 1, 2014): 1131–35. http://dx.doi.org/10.5302/j.icros.2014.14.8019.

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47

Yarinezhad, Ramin, and Seyed Naser Hashemi. "Distributed faulty node detection and recovery scheme for wireless sensor networks using cellular learning automata." Wireless Networks 25, no. 5 (May 10, 2019): 2901–17. http://dx.doi.org/10.1007/s11276-019-02005-7.

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Artail, Hassan, Abdelkarim Ajami, Tania Saouma, and Malak Charaf. "A faulty node detection scheme for wireless sensor networks that use data aggregation for transport." Wireless Communications and Mobile Computing 16, no. 14 (January 12, 2016): 1956–71. http://dx.doi.org/10.1002/wcm.2661.

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Leela Rani, P., and G. A. Sathish Kumar. "Detecting Anonymous Target and Predicting Target Trajectories in Wireless Sensor Networks." Symmetry 13, no. 4 (April 19, 2021): 719. http://dx.doi.org/10.3390/sym13040719.

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Target Tracking (TT) is an application of Wireless Sensor Networks (WSNs) which necessitates constant assessment of the location of a target. Any change in position of a target and the distance from each intermediate sensor node to the target is passed on to base station and these factors play a crucial role in further processing. The drawback of WSN is that it is prone to numerous constraints like low power, faulty sensors, environmental noises, etc. The target should be detected first and its path should be tracked continuously as it moves around the sensing region. This problem of detecting and tracking a target should be conducted with maximum accuracy and minimum energy consumption in each sensor node. In this paper, we propose a Target Detection and Target Tracking (TDTT) model for continuously tracking the target. This model uses prelocalization-based Kalman Filter (KF) for target detection and clique-based estimation for tracking the target trajectories. We evaluated our model by calculating the probability of detecting a target based on distance, then estimating the trajectory. We analyzed the maximum error in position estimation based on density and sensing radius of the sensors. The results were found to be encouraging. The proposed KF-based target detection and clique-based target tracking reduce overall expenditure of energy, thereby increasing network lifetime. This approach is also compared with Dynamic Object Tracking (DOT) and face-based tracking approach. The experimental results prove that employing TDTT improves energy efficiency and extends the lifetime of the network, without compromising the accuracy of tracking.

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Talukdar, Md Ibrahim, Rosilah Hassan, Md Sharif Hossen, Khaleel Ahmad, Faizan Qamar, and Amjed Sid Ahmed. "Performance Improvements of AODV by Black Hole Attack Detection Using IDS and Digital Signature." Wireless Communications and Mobile Computing 2021 (March 2, 2021): 1–13. http://dx.doi.org/10.1155/2021/6693316.

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In mobile ad hoc networks (MANETs), mobile devices connect with other devices wirelessly, where there is no central administration. They are prone to different types of attacks such as the black hole, insider, gray hole, wormhole, faulty node, and packet drop, which considerably interrupt to perform secure communication. This paper has implemented the denial-of-service attacks like black hole attacks on general-purpose ad hoc on-demand distance vector (AODV) protocol. It uses three approaches: normal AODV, black hole AODV (BH_AODV), and detected black hole AODV (D_BH_AODV), wherein we observe that black holes acutely degrade the performance of networks. We have detected the black hole attacks within the networks using two techniques: (1) intrusion detection system (IDS) and (2) encryption technique (digital signature) with the concept of prevention. Moreover, normal AODV, BH_AODV, and D_BH_AODV protocols are investigated for various quality of service (QoS) parameters, i.e., packet delivery ratio (PDR), delay, and overhead with varying the number of nodes, packet sizes, and simulation times. The NS2 software has been used as a simulation tool to simulate existing network topologies, but it does not contain any mechanism to simulate malicious protocols by itself; therefore, we have developed and implemented a D_BH_AODV routing protocol. The outcomes show that the proposed D_BH_AODV approach for the PDR value delivers around 40 to 50% for varying nodes and packets. In contrast, the delay decreases from 300 to 100 ms and 150 to 50 ms with an increase in the number of nodes and packets, respectively. Furthermore, the overhead changes from 1 to 3 for various nodes and packet values. The outcome of this research proves that the black hole attack degrades the overall performance of the network, while the D_BH_AODV enhances the QoS performance since it detects the black hole nodes and avoids them to establish the communication between nodes.

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