Contents
Academic literature on the topic 'FAULTY NODE DETECTION'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'FAULTY NODE DETECTION.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
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.
Full textAbstract:
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.
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.
Full textAbstract:
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.
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.
Full textAbstract:
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.
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.
Full textAbstract:
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.
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.
Full textAbstract:
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].
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.
Full textAbstract:
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.
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.
Full textAbstract:
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).
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.
Full textAbstract:
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.
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.
Full textAbstract:
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.
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.
Full textAbstract:
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.