Achieving maximum reliability in fault tolerant network design for variable networks

Kaushik, Baijnath; Kaur, Navdeep; Kohli, Amit Kumar

doi:10.1016/j.asoc.2013.02.017

Cited by 9 publications

(3 citation statements)

References 51 publications

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“…In this section, common and related algorithms and methods to fault detection literature are reviewed [10][11][12][13][14]. These methods use majority vote but they can't detect common failure nodes.…”

Section: Related Workmentioning

confidence: 99%

FDMG: Fault detection method by using genetic algorithm in clustered wireless sensor networks

Ghaffari

Nobahary

2015

JAIDM

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Wireless sensor networks (WSNs) consist of a large number of sensor nodes which are capable of sensing different environmental phenomena and sending the collected data to the base station or Sink. Since sensor nodes are made of cheap components and are deployed in remote and uncontrolled environments, they are prone to failure. Thus, maintaining a network with its proper functions even when undesired events occur is necessary and is called fault tolerance. Hence, fault management is essential in these networks. In this paper, a new method has been proposed with particular attention to fault tolerance and fault detection in WSN. The performance of the proposed method was simulated in MATLAB. The proposed method was based on majority vote, which can permanently detect faulty sensor nodes accurately. High accuracy and low false alarm rate helped exclude them from the network. To investigate the efficiency of the new method, the researchers compared it with Chen, Lee, and hybrid algorithms. Simulation results indicated that the proposed method has better performance in parameters such as detection accuracy (DA) and a false alarm rate (FAR) even with a large set of faulty sensor nodes.

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Section: Related Workmentioning

confidence: 99%

FDMG: Fault detection method by using genetic algorithm in clustered wireless sensor networks

Ghaffari

Nobahary

2015

JAIDM

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“…Most of these approaches are simulation or analytical, requiring substantial computer work. For smaller networks, these strategies are straightforward and successful (Chakraborty and Goyal 2016;Jaime Silva 2015;Kaushik 2013). Because communicating with every network node is the primary goal of reliability, calculating precise reliability in networks of various sizes is fairly difficult.…”

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confidence: 99%

A comprehensive approach for assessing the reliability of complex networks using OANN approach

Taluja,

Solanki,

Kumar

2023

Multidiscip. Sci. J.

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The optimistic model of network reliability assumes the nodes to be trustworthy while assigning a failure probability to the network's connections. For calculating a network's reliability, the persistent condition is used to guarantee that no two edges have the same failure probability. Accurately estimating the network's reliability across all of its endpoints is an NP-hard problem. The study of network reliability centers on problems arising from topological isolation of individual data nodes. Analysis of network reliability spans the phases of building, deploying, and testing a network. Issues with connection, capacity, and trip time are all metrics used in analyses of computer network reliability. In this paper, an Optimal ANN strategy is proposed as a means of assessing the reliability of the network. In this research, we analyze the methods used in and findings from recent studies on trustworthiness. The term "Optimized Artificial Neural Network" (OANN) is used to describe a strategy that takes into account aspects of both neural networks and another way for measuring trustworthiness. Results are well tested on network of 2^8nodes (mesh network) and hyper-tree network of n nodes. A network's performance may be evaluated with the help of the suggested method, which also contributes to the calculation of its cost and reliability metrics.

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“…Association is especially applied to memorization tasks, such as by applying Hopfield networks [7], while optimization is a field of application for which most of the neural network types have not been specifically designed. However, neural networks can also be applied to optimize system design by learning the optimal structure of the network [39], [29].…”

Section: Introductionmentioning

confidence: 99%

Predicting component reliability and level of degradation with complex-valued neural networks

Fink

Zio

Weidmann

2014

Reliability Engineering & System Safety

102

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. Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering and System Safety, Elsevier, 2014, 121, pp.198-206 AbstractIn this paper, multilayer feedforward neural networks based on multi-valued neurons (MLMVN), a specific type of complex valued neural networks, are proposed to be applied to reliability and degradation prediction problems, formulated as time series. MLMVN have demonstrated their ability to extract complex dynamic patterns from time series data for mid-and long-term predictions in several applications and benchmark studies. To the authors' knowledge, it is the first time that MLMVN are applied for reliability and degradation prediction. MLMVN is applied to a case study of predicting the level of degradation of railway track turnouts using real data. The performance of the algorithms is first evaluated using benchmark study data. The results obtained in the reliability prediction study of the benchmark data show that MLMVN outperforms other machine learning algorithms in terms of prediction precision and is also able to perform multi-step ahead predictions, as opposed to the previously best performing benchmark studies which only performed up to two-step ahead predictions. For the railway turnout application, MLMVN confirm the good performance in the long-term prediction of degradation and does not show accumulating errors for multi-step ahead predictions.

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Achieving maximum reliability in fault tolerant network design for variable networks

Cited by 9 publications

References 51 publications

FDMG: Fault detection method by using genetic algorithm in clustered wireless sensor networks

FDMG: Fault detection method by using genetic algorithm in clustered wireless sensor networks

A comprehensive approach for assessing the reliability of complex networks using OANN approach

Predicting component reliability and level of degradation with complex-valued neural networks

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