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Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further...

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Autores principales: Javed, Abdul Rehman, Hassan, Muhammad Abul, Shahzad, Faisal, Ahmed, Waqas, Singh, Saurabh, Baker, Thar, Gadekallu, Thippa Reddy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229631/
https://www.ncbi.nlm.nih.gov/pubmed/35746176
http://dx.doi.org/10.3390/s22124394
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author Javed, Abdul Rehman
Hassan, Muhammad Abul
Shahzad, Faisal
Ahmed, Waqas
Singh, Saurabh
Baker, Thar
Gadekallu, Thippa Reddy
author_facet Javed, Abdul Rehman
Hassan, Muhammad Abul
Shahzad, Faisal
Ahmed, Waqas
Singh, Saurabh
Baker, Thar
Gadekallu, Thippa Reddy
author_sort Javed, Abdul Rehman
collection PubMed
description The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.
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spelling pubmed-92296312022-06-25 Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey Javed, Abdul Rehman Hassan, Muhammad Abul Shahzad, Faisal Ahmed, Waqas Singh, Saurabh Baker, Thar Gadekallu, Thippa Reddy Sensors (Basel) Review The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks. MDPI 2022-06-10 /pmc/articles/PMC9229631/ /pubmed/35746176 http://dx.doi.org/10.3390/s22124394 Text en © 2022 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 Review
Javed, Abdul Rehman
Hassan, Muhammad Abul
Shahzad, Faisal
Ahmed, Waqas
Singh, Saurabh
Baker, Thar
Gadekallu, Thippa Reddy
Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
title Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
title_full Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
title_fullStr Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
title_full_unstemmed Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
title_short Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
title_sort integration of blockchain technology and federated learning in vehicular (iot) networks: a comprehensive survey
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229631/
https://www.ncbi.nlm.nih.gov/pubmed/35746176
http://dx.doi.org/10.3390/s22124394
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