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DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents

Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization’s 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collis...

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Autores principales: Farooq, Hamza, Altaf, Ayesha, Iqbal, Faiza, Galán, Juan Castanedo, Aray, Daniel Gavilanes, Ashraf, Imran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301524/
https://www.ncbi.nlm.nih.gov/pubmed/37420557
http://dx.doi.org/10.3390/s23125388
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author Farooq, Hamza
Altaf, Ayesha
Iqbal, Faiza
Galán, Juan Castanedo
Aray, Daniel Gavilanes
Ashraf, Imran
author_facet Farooq, Hamza
Altaf, Ayesha
Iqbal, Faiza
Galán, Juan Castanedo
Aray, Daniel Gavilanes
Ashraf, Imran
author_sort Farooq, Hamza
collection PubMed
description Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization’s 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver’s impairment level by monitoring the driver’s blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model.
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spelling pubmed-103015242023-06-29 DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents Farooq, Hamza Altaf, Ayesha Iqbal, Faiza Galán, Juan Castanedo Aray, Daniel Gavilanes Ashraf, Imran Sensors (Basel) Article Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization’s 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver’s impairment level by monitoring the driver’s blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model. MDPI 2023-06-07 /pmc/articles/PMC10301524/ /pubmed/37420557 http://dx.doi.org/10.3390/s23125388 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
Farooq, Hamza
Altaf, Ayesha
Iqbal, Faiza
Galán, Juan Castanedo
Aray, Daniel Gavilanes
Ashraf, Imran
DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents
title DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents
title_full DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents
title_fullStr DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents
title_full_unstemmed DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents
title_short DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents
title_sort drunkchain: blockchain-based iot system for preventing drunk driving-related traffic accidents
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301524/
https://www.ncbi.nlm.nih.gov/pubmed/37420557
http://dx.doi.org/10.3390/s23125388
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