Relevant bibliographies by topics / WSN; Fault Model; BFRA

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'WSN; Fault Model; BFRA'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "WSN; Fault Model; BFRA"

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Vijay, Kumar. "SURVEY OF FAULT DETECTION ALGORITHM IN WSN." INTERNATIONAL JOURNAL OF RESEARCH- GRANTHAALAYAH 5, no. 5 (2017): 207–13. https://doi.org/10.5281/zenodo.583910.

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In recent years, applications of wireless sensor networks (WSNs) have been improved due to its vast potential to connect the physical world to the virtual world. Also, a progress in microelectronic fabrication technology reduces cost of developed portable wireless sensor nodes. Faults occurring to sensor nodes are familiar due to the sensor device itself and the harsh environment where the sensor nodes are deploy. WSNs is mainly affect by the crash of sensor nodes. Possibility of sensor node failure increases with increase number of sensors. Wireless sensor networks have been recognized, at an early stage in their development, to be a useful measurement technology for environmental monitoring applications. Based on their independence from accessible infrastructures, wireless sensor networks can be deploy in virtually any location and provide sensor samples in a spatial and temporal resolution
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Krishna, M. Sai Rama, Ch Jnana Gayathri, and K. Laxmi Pallavi Rao. "Building Fault Tolerance Within Wsn- A Topology Model." International Journal of Advances in Applied Sciences 7, no. 2 (2018): 135. http://dx.doi.org/10.11591/ijaas.v7.i2.pp135-142.

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<p>Wireless Sensor network plays a crucial role which helps in visualizing, processing, and analyzing the information wirelessly. WSN is a network which consists of huge amount of sensor devices which are of low cost and low powered also known as sensor nodes. These type of networks are generally used in real time applications such as monitoring of environmental conditions, militaries, industries etc.,.but the problem that exists in WSN is may be due to different failures such as node failure, link failure, sink failure, interference, power dissipation and collision. If these faults are unable to handle then the desired network criteria’s may not be reached properly which results in inefficiency of the network. So, the main idea behind the investigation is to form a different networking topology which works in the event of failure</p>
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M., Sai Rama Krishna, Jnana Gayathri Ch., and Laxmi Pallavi Rao K. "Building Fault Tolerance Within Wsn-A Topology Model." International Journal of Advances in Applied Sciences (IJAAS) 7, no. 2 (2018): 135–42. https://doi.org/10.11591/ijaas.v7.i2.pp135-142.

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Wireless Sensor network plays a crucial role which helps in visualizing, processing, and analyzing the information wirelessly. WSN is a network which consists of huge amount of sensor devices which are of low cost and low powered also known as sensor nodes. These type of networks are generally used in real time applications such as monitoring of environmental conditions, militaries, industries etc., .but the problem that exists in WSN is may be due to different failures such as node failure, link failure, sink failure, interference, power dissipation and collision. If these faults are unable to handle then the desired network criteria’s may not be reached properly which results in inefficiency of the network. So, the main idea behind the investigation is to form a different networking topology which works in the event of failure
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Mardenov, Yerik, Aigul Adamova, Tamara Zhukabayeva, and Mohamed Othman. "Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study." Journal of Robotics and Control (JRC) 4, no. 6 (2023): 868–77. http://dx.doi.org/10.18196/jrc.v4i6.20216.

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The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.
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Smara, Mounya, and Al-Sakib Khan Pathan. "An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks." Network 4, no. 2 (2024): 150–74. http://dx.doi.org/10.3390/network4020008.

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Fault tolerance is a critical aspect for any wireless sensor network (WSN), which can be defined in plain terms as the quality of being dependable or performing consistently well. In other words, it may be described as the effectiveness of fault tolerance in the event of crucial component failures in the network. As a WSN is composed of sensors with constrained energy resources, network disconnections and faults may occur because of a power failure or exhaustion of the battery. When such a network is used for precision agriculture, which needs periodic and timely readings from the agricultural field, necessary measures are needed to handle the effects of such faults in the network. As climate change is affecting many parts of the globe, WSN-based precision agriculture could provide timely and early warnings to the farmers about unpredictable weather events and they could take the necessary measures to save their crops or to lessen the potential damage. Considering this as a critical application area, in this paper, we propose a fault-tolerant scheme for WSNs deployed for precision agriculture. Along with the description of our mechanism, we provide a theoretical operational model, simulation, analysis, and a formal verification using the UPPAAL model checker.
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Hu, Jiamin, Xiaofan Yang, and Luxing Yang. "A Novel Diagnosis Scheme against Collusive False Data Injection Attack." Sensors 23, no. 13 (2023): 5943. http://dx.doi.org/10.3390/s23135943.

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The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in which false data are injected in a coordinated manner into some adjacent pairs of captured nodes of an attacked wireless sensor network (WSN). As a result, the defense of WSN against a CFDIA is much more difficult than defense against ordinary FDIA. This paper is devoted to identifying the compromised sensors of a well-behaved WSN under a CFDIA. By establishing a model for predicting the reading of a sensor and employing the principal component analysis (PCA) technique, we establish a criterion for judging whether an adjacent pair of sensors are consistent in terms of their readings. Inspired by the system-level fault diagnosis, we introduce a set of watchdogs into a WSN as comparators between adjacent pairs of sensors of the WSN, and we propose an algorithm for diagnosing the WSN based on the collection of the consistency outcomes. Simulation results show that the proposed diagnosis scheme achieves a higher probability of correct diagnosis.
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Xiao, Yang. "Dynamic Fault Tolerant Topology Control for Wireless Sensor Network Based on Node Cascading Failure." International Journal of Online Engineering (iJOE) 14, no. 05 (2018): 118. http://dx.doi.org/10.3991/ijoe.v14i05.8644.

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To address the node cascading failure (CF) of the wireless sensor networks (WSNs), considering such factors as node load and maximum capacity in scale-free topology, this paper establishes the WSN dynamic fault tolerant topology model based on node cascading failure, analyses the relationships between node load, topology and dynamic fault tolerance, and demonstrates the proposed model through simulation test. It studies the effects of topology parameter and load in case of random node failure in the network node cascading failure, and utilizes the theoretical derivation method to derive the structural feature of scale-free topology and the capacity limit for the WSNs large-scale cascading failure, effectively enhancing the cascading fault tolerance of traditional WSNs. The simulation test results show that, with the degree distribution parameter <em>C</em> increasing, the minimum network node degree will increase accordingly, and in highly intensive topology, the dynamic fault tolerance will be better; with the parameter<em> λ </em>increasing, the maximum degree of the network node will gradually decrease, and the degree distribution of topology structure tends to be uniform, so that the large-scale cascading failure caused by node failure will have less influence on the WSN, and further improve the dynamic fault tolerance performance of the system.
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Vasco Arone Mazibuco, Nguyen Phuong Nhung, and Nguyen Tuan Linh. "Fault detection in wireless sensor networks with deep neural networks." Journal of Military Science and Technology, CSCE7 (December 30, 2023): 27–36. http://dx.doi.org/10.54939/1859-1043.j.mst.csce7.2023.27-36.

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This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type. We propose the use of deep neural networks, particularly Recurrent Neural Networks (RNNs), for fault detection in WSNs, focusing on temperature and humidity data. The paper emphasizes the importance of careful model selection, tuning, and thorough evaluation to enhance the accuracy and robustness of fault detection in real-world WSN applications.
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Qin, Bo, Luyang Zhang, Heng Yin, and Yan Qin. "Multiple Feature Vectors Based Fault Classification for WSN Integrated Bearing of Rolling Mill." Journal of Control Science and Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3041591.

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For rolling mill machines, the operation status of bearing has a close relationship with process safety and production effectiveness. Therefore, reliable fault diagnosis and classification are indispensable. Traditional methods always characterize fault feature using a single fault vector, which may fail to reveal whole fault influences caused by complex process disturbances. Besides, it may also lead to poor fault classification accuracy. To solve the above-mentioned problems, a fault extraction method is put forward to extract multiple feature vectors and then a classification model is developed. First, to collect sufficient data, a data acquisition system based on wireless sensor network is constructed to replace the traditional wired system which may bring dangers during production. Second, the measured signal is filtered by a morphological average filtering algorithm to remove process noise and then the empirical mode decomposition method is applied to extract the intrinsic mode function (IMF) which contains the fault information. On the basis of the IMFs, a time domain index (energy) and a frequency index (singular values) are proposed through Hilbert envelope analysis. From the above analysis, the energy index and the singular value matrix are used for fault classification modeling based on the enhanced extreme learning machine (ELM), which is optimized by the bat algorithm to adjust the input weights and threshold of hidden layer node. In comparison with the fault classification methods based on SVM and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability.
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Shakya, Subarna. "Pollination Inspired Clustering Model for Wireless Sensor Network Optimization." September 2021 3, no. 3 (2021): 196–207. http://dx.doi.org/10.36548/jsws.2021.3.006.

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Remote and dangerous fields that are expensive, complex, and unreachable to reach human insights are examined with ease using the Wireless Sensor Network (WSN) applications. Due to the use of non-renewable sources of energy, challenges with respect to the network lifetime, fault tolerance and energy consumption are faced by the self-managed networks. An efficient fault tolerance technique has been provided in this paper as an effective management strategy. Using the network and communication nodes, revitalization and fault recognition techniques are used for handling diverse levels of faults in this framework. At the network nodes, the fault tolerance capability is increased by the proposed protocol model and management strategy. This enhances the corresponding data transmission in the network. When compared to the conventional techniques, the proposed model increases the network lifetime by five times. It is observed from the validation results that, with a 10% increase in the network lifetime, there is a 2% decrease in the fault tolerance proficiency of the network. The network lifetime and data transmission rate are improved while the network energy consumption is reduced significantly. The MATLAB environment is used for simulation purpose. In terms of energy consumption, network lifetime and fault tolerance, the proposed model offers optimal results.
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Dissertations / Theses on the topic "WSN; Fault Model; BFRA"

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Sridharan, Mukundan. "Design of Mobile and Static Sensor Fabrics." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306891061.

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Book chapters on the topic "WSN; Fault Model; BFRA"

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Sreedevi, Dr Pogula, Dr T. Santhi Sri, Dr E. Poornima, and Dr Farooq Sunar Mahammad. "IMPROVE FAULT DETECTION AND SENSOR ROUTING PROBLEMS WITH NAIVE BAYES CLASSIFIER AND DATA AGGREGATION APPROACH." In Futuristic Trends in IOT Volume 3 Book 6. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bbio6p2ch4.

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Wireless sensor networks are regularly located in hostile environments to observe changes in environmental factors. Broadening the importance of machine learning to WSN creates wonders and provides credibility to the system. Clustering in wireless sensor networks is a better technique to enhance energy utilization. This chapter uses an ensemble method for clustering and classification to develop machine-learning algorithms for fault detection in wireless sensor networks. We will propose a new routing protocol, Fault tolerance Backup cluster head, and Data Aggregation with Naïve Bayes Classifier to improve fault detection and sensor routing problems. We develop a statistical approach to detect and identify faults in a WSN. The analysis of our model is an accurate and effective fault management framework with minimum energy consumption, delay, and overhead
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Huang Hongwei and Zhang Dongming. "Quantitative Geotechnical Risk Management for Tunneling Projects in China." In Geotechnical Safety and Risk V. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-580-7-61.

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To date, the tunneling in China is experiencing an age of fast development for decades. The potential risks behind the huge amount of construction and operation works in China was first formally realized and managed after 2002. The transition of risk assessment from a qualitative manner to a quantitative manner is on the way from the research gradually to the practice. This paper tries to share some experiences in the quantitative risk management for tunneling in China by introducing novel techniques and associated practical applications. The fuzzy fault tree analysis is used for hazard identification, the conditional Markov chain for probability analysis of soil spatial uncertainty, the quantitative vulnerability analysis for consequence evaluation and the field data based statistics for environmental impact risk analysis. All these novel methods have been validated successfully by applying into real cases shown in the paper. The dynamic feature of risk management is appreciated due to the different stages and scenarios of a tunnel project. The real-time monitoring technique developed using the LEDs and MEMS coupled with WSN could visualize the risk to the worker on site timely. The resilience analysis model to incorporate the high-impact low-chance risk for tunnel lining structure is introduced in the end of paper, which could assist the engineers to make the decision on performance recovery strategies once the tunnel goes through a significant disruption.
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Huang Hongwei and Zhang Dongming. "Quantitative Geotechnical Risk Management for Tunneling Projects in China." In Geotechnical Safety and Risk V. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-580-7-60.

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To date, the tunneling in China is experiencing an age of fast development for decades. The potential risks behind the huge amount of construction and operation works in China was first formally realized and managed after 2002. The transition of risk assessment from a qualitative manner to a quantitative manner is on the way from the research gradually to the practice. This paper tries to share some experiences in the quantitative risk management for tunneling in China by introducing novel techniques and associated practical applications. The fuzzy fault tree analysis is used for hazard identification, the conditional Markov chain for probability analysis of soil spatial uncertainty, the quantitative vulnerability analysis for consequence evaluation and the field data based statistics for environmental impact risk analysis. All these novel methods have been validated successfully by applying into real cases shown in the paper. The dynamic feature of risk management is appreciated due to the different stages and scenarios of a tunnel project. The real-time monitoring technique developed using the LEDs and MEMS coupled with WSN could visualize the risk to the worker on site timely. The resilience analysis model to incorporate the high-impact low-chance risk for tunnel lining structure is introduced in the end of paper, which could assist the engineers to make the decision on performance recovery strategies once the tunnel goes through a significant disruption.
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Virendra, Mohit, Qi Duan, and Shambhu Upadhyaya. "Detecting Cheating Aggregators and Report Dropping Attacks in Wireless Sensor Networks." In Wireless Technologies. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-101-6.ch305.

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This chapter focuses on an important, challenging and yet largely unaddressed problem in Wireless Sensor Networks (WSN) data communication: detecting cheating aggregators and malicious/selfish discarding of data reports en route to the Base Stations (BSs). If undetected, such attacks can significantly affect the performance of applications. The goal is to make the aggregation process tamper-resistant so that the aggregator cannot report arbitrary values, and to ensure that silent discarding of data reports by intermediate en-route nodes is detected in a bounded fashion. In our model, individual node readings are aggregated into data reports by Aggregator Nodes or Cluster Heads and forwarded to the BS. BS performs a two-stage analysis on these reports: (a) Verification through attached proofs, (b) Comparison with Proxy Reports for ensuring arrival accuracy. Proofs are non-interactive verifiers sent with reports to attest correctness of reported values. Proxy Reports are periodically sent along alternate paths by non-aggregator nodes, piggybacked on data reports from other nodes. The model is intended as a guide for implementing security in real sensor network applications. It is simple and comprehensive, covering a variety of data formats and aggregation models: numeric and non-numeric data and aggregators located across one or multiple hops. Security analysis shows that the reports, both primary and proxy, cannot be forged by any outsiders and the contents of the reports are held confidential and the scheme is robust against collusion attacks. Lightweight design aims at minimal additional control and energy overhead. Simulation results show its fault tolerance against random and patterned node failures.
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Conference papers on the topic "WSN; Fault Model; BFRA"

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Begum, Beneyaz A., and N. V. Satyanarayana. "Composite interference mapping model for Interference Fault-Free Transmission in WSN." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275930.

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Lin, Lin. "An Intelligent Fault Diagnosis Model of WSN Based on Artificial Immune System." In 2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA). IEEE, 2020. http://dx.doi.org/10.1109/icsgea51094.2020.00093.

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Ren, Weizheng, Lianming Xu, and Zhongliang Deng. "Fault Diagnosis Model of WSN Based on Rough Set and Neural Network Ensemble." In 2008 Second International Symposium on Intelligent Information Technology Application. IEEE, 2008. http://dx.doi.org/10.1109/iita.2008.459.

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