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

【Author】 Farooq, Hamza; Altaf, Ayesha; Iqbal, Faiza; Castanedo Galan, Juan; Gavilanes Aray, Daniel; Ashraf, Imran

【Source】SENSORS

【影响因子】3.847

【Abstract】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.

【Keywords】Internet of Things; blockchain; drunk driver detection; network security; MQ3

【发表时间】2023 JUN

【收录时间】2023-07-15

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-交通领域