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Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications

Technology plays a significant role in our daily lives as real-time applications and services such as video surveillance systems and the Internet of Things (IoT) are rapidly developing. With the introduction of fog computing, a large amount of processing has been done by fog devices for IoT applicat...

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Autores principales: H, Sabireen, Venkataraman, Neelanarayanan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054027/
https://www.ncbi.nlm.nih.gov/pubmed/36991624
http://dx.doi.org/10.3390/s23062913
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author H, Sabireen
Venkataraman, Neelanarayanan
author_facet H, Sabireen
Venkataraman, Neelanarayanan
author_sort H, Sabireen
collection PubMed
description Technology plays a significant role in our daily lives as real-time applications and services such as video surveillance systems and the Internet of Things (IoT) are rapidly developing. With the introduction of fog computing, a large amount of processing has been done by fog devices for IoT applications. However, a fog device’s reliability may be affected by insufficient resources at fog nodes, which may fail to process the IoT applications. There are obvious maintenance challenges associated with many read-write operations and hazardous edge environments. To increase reliability, scalable fault-predictive proactive methods are needed that predict the failure of inadequate resources of fog devices. In this paper, a Recurrent Neural Network (RNN)-based method to predict proactive faults in the event of insufficient resources in fog devices based on a conceptual Long Short-Term Memory (LSTM) and novel Computation Memory and Power (CRP) rule-based network policy is proposed. To identify the precise cause of failure due to inadequate resources, the proposed CRP is built upon the LSTM network. As part of the conceptual framework proposed, fault detectors and fault monitors prevent the outage of fog nodes while providing services to IoT applications. The results show that the LSTM along with the CRP network policy method achieves a prediction accuracy of 95.16% on the training data and a 98.69% accuracy on the testing data, which significantly outperforms the performance of existing machine learning and deep learning techniques. Furthermore, the presented method predicts proactive faults with a normalized root mean square error of 0.017, providing an accurate prediction of fog node failure. The proposed framework experiments show a significant improvement in the prediction of inaccurate resources of fog nodes by having a minimum delay, low processing time, improved accuracy, and the failure rate of prediction was faster in comparison to traditional LSTM, Support Vector Machines (SVM), and Logistic Regression.
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spelling pubmed-100540272023-03-30 Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications H, Sabireen Venkataraman, Neelanarayanan Sensors (Basel) Article Technology plays a significant role in our daily lives as real-time applications and services such as video surveillance systems and the Internet of Things (IoT) are rapidly developing. With the introduction of fog computing, a large amount of processing has been done by fog devices for IoT applications. However, a fog device’s reliability may be affected by insufficient resources at fog nodes, which may fail to process the IoT applications. There are obvious maintenance challenges associated with many read-write operations and hazardous edge environments. To increase reliability, scalable fault-predictive proactive methods are needed that predict the failure of inadequate resources of fog devices. In this paper, a Recurrent Neural Network (RNN)-based method to predict proactive faults in the event of insufficient resources in fog devices based on a conceptual Long Short-Term Memory (LSTM) and novel Computation Memory and Power (CRP) rule-based network policy is proposed. To identify the precise cause of failure due to inadequate resources, the proposed CRP is built upon the LSTM network. As part of the conceptual framework proposed, fault detectors and fault monitors prevent the outage of fog nodes while providing services to IoT applications. The results show that the LSTM along with the CRP network policy method achieves a prediction accuracy of 95.16% on the training data and a 98.69% accuracy on the testing data, which significantly outperforms the performance of existing machine learning and deep learning techniques. Furthermore, the presented method predicts proactive faults with a normalized root mean square error of 0.017, providing an accurate prediction of fog node failure. The proposed framework experiments show a significant improvement in the prediction of inaccurate resources of fog nodes by having a minimum delay, low processing time, improved accuracy, and the failure rate of prediction was faster in comparison to traditional LSTM, Support Vector Machines (SVM), and Logistic Regression. MDPI 2023-03-08 /pmc/articles/PMC10054027/ /pubmed/36991624 http://dx.doi.org/10.3390/s23062913 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
H, Sabireen
Venkataraman, Neelanarayanan
Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications
title Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications
title_full Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications
title_fullStr Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications
title_full_unstemmed Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications
title_short Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications
title_sort proactive fault prediction of fog devices using lstm-crp conceptual framework for iot applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054027/
https://www.ncbi.nlm.nih.gov/pubmed/36991624
http://dx.doi.org/10.3390/s23062913
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