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Relevant bibliographies by topics / FAULTY NODE DETECTION

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Journal articles

Academic literature on the topic 'FAULTY NODE DETECTION'

Author: Grafiati

Published: 16 January 2024

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Journal articles on the topic "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 s

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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 (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” optim

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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 trans

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4

Xu, Xiaowei, Fangrong Zhou, Yongjie Nie, et al. "Fault Detection and Location of 35 kV Single-Ended Radial Distribution Network Based on Traveling Wave Detection Method." Processes 11, no. 8 (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 radia

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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 generate

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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 collec

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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 p

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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 (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 b

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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 ro

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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 (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 t

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