Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault...

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Main Authors: Thavavel Vaiyapuri, K. Shankar, Surendran Rajendran, Sachin Kumar, Srijana Acharya, Hyunil Kim
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10138178/
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author Thavavel Vaiyapuri
K. Shankar
Surendran Rajendran
Sachin Kumar
Srijana Acharya
Hyunil Kim
author_facet Thavavel Vaiyapuri
K. Shankar
Surendran Rajendran
Sachin Kumar
Srijana Acharya
Hyunil Kim
author_sort Thavavel Vaiyapuri
collection DOAJ
description Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.
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spelling doaj.art-e03de033f4ab4631948a3f6a8e7e99412023-06-12T23:00:54ZengIEEEIEEE Access2169-35362023-01-0111553705537910.1109/ACCESS.2023.328079810138178Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoTThavavel Vaiyapuri0https://orcid.org/0000-0001-5494-5278K. Shankar1https://orcid.org/0000-0002-2803-3846Surendran Rajendran2Sachin Kumar3https://orcid.org/0000-0003-3949-0302Srijana Acharya4https://orcid.org/0000-0002-0724-8936Hyunil Kim5https://orcid.org/0000-0002-4018-4540College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaBig Data and Machine Learning Laboratory, South Ural State University, Chelyabinsk, RussiaDepartment of Convergence Science, Kongju National University, Gongju-si 32588, South KoreaDepartment of Convergence Science, Kongju National University, Gongju-si 32588, South KoreaInternet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.https://ieeexplore.ieee.org/document/10138178/BlockchainInternet of Thingsconsensus algorithmfault detectiondeep learninghyperparameter tuning
spellingShingle Thavavel Vaiyapuri
K. Shankar
Surendran Rajendran
Sachin Kumar
Srijana Acharya
Hyunil Kim
Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
IEEE Access
Blockchain
Internet of Things
consensus algorithm
fault detection
deep learning
hyperparameter tuning
title Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
title_full Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
title_fullStr Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
title_full_unstemmed Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
title_short Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
title_sort blockchain assisted data edge verification with consensus algorithm for machine learning assisted iot
topic Blockchain
Internet of Things
consensus algorithm
fault detection
deep learning
hyperparameter tuning
url https://ieeexplore.ieee.org/document/10138178/
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AT sachinkumar blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot
AT srijanaacharya blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot
AT hyunilkim blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot