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...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10138178/ |
_version_ | 1827927959928832000 |
---|---|
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%. |
first_indexed | 2024-03-13T06:00:36Z |
format | Article |
id | doaj.art-e03de033f4ab4631948a3f6a8e7e9941 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-03-13T06:00:36Z |
publishDate | 2023-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
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/ |
work_keys_str_mv | AT thavavelvaiyapuri blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot AT kshankar blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot AT surendranrajendran blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot AT sachinkumar blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot AT srijanaacharya blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot AT hyunilkim blockchainassisteddataedgeverificationwithconsensusalgorithmformachinelearningassistediot |