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Farooq, H.; Altaf, A.; Iqbal, F.; Galán, J.C.; Aray, D.G.; Ashraf, I. DrunkChain: Blockchain-Based IoT System. Encyclopedia. Available online: https://encyclopedia.pub/entry/45671 (accessed on 27 July 2024).
Farooq H, Altaf A, Iqbal F, Galán JC, Aray DG, Ashraf I. DrunkChain: Blockchain-Based IoT System. Encyclopedia. Available at: https://encyclopedia.pub/entry/45671. Accessed July 27, 2024.
Farooq, Hamza, Ayesha Altaf, Faiza Iqbal, Juan Castanedo Galán, Daniel Gavilanes Aray, Imran Ashraf. "DrunkChain: Blockchain-Based IoT System" Encyclopedia, https://encyclopedia.pub/entry/45671 (accessed July 27, 2024).
Farooq, H., Altaf, A., Iqbal, F., Galán, J.C., Aray, D.G., & Ashraf, I. (2023, June 15). DrunkChain: Blockchain-Based IoT System. In Encyclopedia. https://encyclopedia.pub/entry/45671
Farooq, Hamza, et al. "DrunkChain: Blockchain-Based IoT System." Encyclopedia. Web. 15 June, 2023.
DrunkChain: Blockchain-Based IoT System
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This entry is adapted from the peer-reviewed paper 10.3390/s23125388

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.

Internet of Things blockchain drunk driver detection

1. Introduction

Road accidents have emerged as a significant problem and can result in both human casualties and property loss. With more speeding automobiles on the road, the likelihood of automobile collisions has risen. Approximately 80 million automobiles are manufactured worldwide, and the number continues to rise [1]. Since 1991, only 39 highways, motorways, expressways, and vital routes have been constructed, which is fewer than the number of automobiles generated annually. As a result, there are significantly more automobiles on the road, and even minor carelessness might put road users at risk. Pakistan has stringent rules on alcohol consumption [2]; however, as one of the most economically developing nations, it is observed that the lowest-class citizens, such as laborers, consume the most alcohol [3]. Numerous Pakistani commercial drivers are addicted to narcotics and alcohol, which increases the number of deadly accidents.
Currently, the government and traffic police in Pakistan do not use new technology in the traffic system. According to the WHO’s (World Health Organization’s) worldwide status report on road safety [4], in 2010, there were 4448 deaths at the accident scenes, and an additional 27,582 deaths resulting from road accidents. According to one report, intoxicated drivers were involved in 150 fatal road accidents in Pakistan over a period of ten months [5]. Pakistan has a national rule against drinking and driving, but its implementation is only at a rate of 30%. The enforcement of laws against driving under the influence of alcohol is carried out by the authorities [2]. If a driver is tired or intoxicated, the appropriate police officials will utilize roadblocks and inspect each vehicle individually. They must evaluate drivers based on indicators such as the smell of alcohol on the driver’s breath, the driver’s physical appearance, and driving performance. Fuel officers visually assess the suspect and the suspect’s car in order to determine the suspect’s BAC. Despite best practices, police officers cannot determine the alcohol concentration of every driver by analyzing the driving behavior. This method is inadequate since it is sometimes inaccurate and prone to bias and human error. Implementing more automated and sensor-driven technology can reduce the time and government resources required to detect drunk driving. Table 1 displays the distribution of road-related fatalities in Pakistan.
Table 1. Total number of victims in fatal automobile accidents.
Nature of Data Count
Total fatalities in the country [4] 27,582 per year
Total accidents in Karachi city [5] 154 in 10 months
Total fatalities caused by inebriated drivers in Karachi city [5] 150 in 10 months
This research presents a system that utilizes IoT (Internet of Things) technology and enhanced security measures to prevent drunk driving accidents. The transmission of information is secured, and authentication and permission mechanisms have been introduced alongside IoT devices. Using blockchain to encrypt data is a step forward in the search for viable solutions, and there is a high likelihood of its official implementation.

2. DrunkChain

Due to the limited time available each day for activities such as work, chores, and responsibilities, it is common for individuals to drive quickly in order to complete tasks. Frequent road accidents are caused by individuals who move quickly and disregard traffic regulations. It has been observed that the majority of drivers disregard traffic regulations. Most public attempts to avoid traffic wardens occur if a traffic light is malfunctioning or when there is additional personnel on the road managing heavy traffic [6]. Table 2 provides a summary of the distinct findings from various research studies in this field, along with a discussion of the focal points and gaps identified in the literature. The categories listed in the table are as follows:
Table 2. Emphasis and omissions in each study that contributes to the formation of the safe driving system.
Reference Focus of the Paper Gaps
Authentication and Authorization Data Encryption and Security Backup and Recovery Underlying Technology Used Problem Solution
[7] Measures to shut down and track vehicles to make the roads safer from drunk driving accidents 0 0 0 1 2
[8] Implementation of an embedded system with an alcohol sensor, which enables the vehicle to prevent the drunk individual from driving 0 1 1 0 1
[9] A system that controls traffic using IoT and AI by signaling and detecting the roads and traffic 1 0 1 1 2
[10] A safe driving mechanism that involves tracking driving behavior with detection accuracy and alarm rates 0 0 0 1 1
[11] Online system that detects anomalies by quantitatively evaluating the information of the driver 1 0 1 1 1
[12] An IoV-based system that detects if a driver is fatigued via neural networks (ML) and the normalization algorithm 1 1 1 1 1
[13] DL and AI-based systems to recognize driving hazards for light transport vehicles (LTVs), providing early warnings prior to predicted collisions 2 1 0 1 2
[14] Identifies the factors that contribute to the overall driving experience and compares these factors between older and younger drivers 0 0 0 1 1
[15] A virtual reality (VR) driving ’game’ that educates the public more effectively on the hazards of drunk driving using an evidence-based approach; includes real alcohol-impaired participants 1 2 1 1 1
  • 3 If comprehensive work is done, well-explained, and tested.
  • 2 If work is somewhat conducted and explained.
  • 1 If a tad bit of an idea is given.
  • 0 If work is not explained at all.
For better visual comprehension, the research articles are categorized based on the underlying technology used, as depicted in Figure 1. Further details can be found in Table 3. Considering the work in [16], it is a good idea to protect the system from cyberattacks. However, IoT devices typically lack a solid foundation when it comes to security and privacy. Interoperability is another crucial issue to consider. This is not handled very well by IoT devices. The system should be sufficiently able to reduce the need for micromanaging security issues. This solution combines blockchain technology with IoT devices that serve as security capsules for internet communication. Decentralized authentication will also be implemented in this domain. Modern vehicles with IoT-enabled devices are used to monitor traffic scenarios based on the behaviors and locations of drivers. Through the cloud, different traffic and road conditions are continuously forecasted. The dataset is then classified according to a variety of circumstances using real-time modules of machine learning. Here, blockchain and IoT devices are implemented, resulting in the creation of a secure system [17].
Sensors 23 05388 g001
Figure 1. Technology distribution of each system presented in existing studies.
Table 3. Summary of the variations of technological indicators across different studies.
Technological Dissection of Each System Presented in the Study Study Qty
Android [18][19] 2
Modules using Wi-Fi [7][9][11][17][18] 5
Arduino [8][16] 2
Blockchain [17][20][21] 3
Sensors [7][8][22] 3
GPU [7][8][10][12][23] 5
IoT [7][9][16][19][20][22][23][24][25] 9
GPS [7][8][10][16][22][24] 6
AI and machine learning models [9][10][11][12][19][23][25] 7
The authors of [23] proposed a system that predicts if the vehicle’s operator is alert or drowsy. This is accomplished by integrating IoT devices and video streaming processors. In conjunction with the vehicle’s positioning, the eye aspect ratio is analyzed, and in the event of a collision, a notification is sent to the police station and the nearest emergency assistance. The face detection algorithm is used to achieve precise results for eye blinking and eye positioning. This system could be enhanced by incorporating an alcohol detection sensor into the module. The MQ-3 sensor improves the process of apprehending an intoxicated driver by detecting BAC on the driver’s breath. By integrating a Q3 sensor and an Arduino Uno microcontroller interface with a GPS (global positioning system) system that monitors the driver and detects any errant motions in the vehicle’s motion, the system can detect any erratic driving behavior. This hybrid approach categorizes a driver’s data based on the driver’s level of intoxication. The ITP (information transfer process) flow is complex in the government sector. This must be streamlined and swiftly action-oriented. To improve processing efficiency, blockchain technology is implemented. There is a flow that controls and distributes the stages of the entire process. This flow restructures the business and operational phases and supports the e-government infrastructure [20]. Studies in the domain of trust-based IoT networks can be integrated with vehicular networks. In [26], the authors identified and highlighted existing solutions and trust models of IoT. Their survey identified existing challenges, issues, and future directions. Similarly, another study [27] presented trust-based solutions for IoT in smart buildings. Another study [28] utilized trust models of IoT to present security solutions for smart city networks. In [29], the authors provided a context-based trust model to provide edge intelligence and security to IoT nodes. In [30], the authors highlighted blockchain architecture, applied domains, and platforms. They provided a detailed description of the existing security threats of blockchain technology. An important survey [31] was conducted in the domain, which provided detailed guidelines for driving safety with sensing in vehicular communications. The authors utilized artificial intelligence-based collision avoidance mechanisms and a testbed-based analysis to present solutions for collision avoidance.
In [18], the authors proposed using a smartphone to determine whether or not a person is intoxicated. The application is installed on a mobile device in the same manner as any other app. The application is then subject to the behavior and position of the vehicle when it is in motion. If the system detects any behavior indicating that a person is intoxicated, it alerts the concerned party to seize or rescue the individual. This alert can be sent to the local police station or a family member. Several IoT devices are integrated and intercommunicated to enable smart homes; this is the future of technology. On the other hand, the likelihood of cyberattacks is high, and the repercussions can be much more severe. The system presented in [21] utilizes blockchain to protect against cyberattacks. When edge computing is integrated with transaction protocols, such as smart contract blockchains, it prevents data privacy breaches. The reduction in the latency of IoT devices is an additional factor that should be considered. Scalability plays an important role in this type of system, especially when time is a critical factor for reporting the location, monitoring driver behavior, or generating alerts in sensitive situations, such as heavy traffic.
An AI (artificial intelligence) module was integrated with IoT devices in [25] to ensure that the drunk driver was constantly monitored and arrived at their destination safely. In the event of an emergency or in drunk driving, information regarding the driver’s speed and location is collected, analyzed, and reported as necessary. Similarly, in [19], the dangers and driving behaviors were evaluated. The system identifies situations where something goes wrong, which is referred to as the detection of sleepiness. When detected, the system immediately alerts the relevant parties when an accident is predicted. When one of the factors reaches an alarming rate, this solution detects the driving pattern. For this, it considers speed, unpredictable driving, and speed control. The system is installed on a smartphone with detectors and employs data correction and classification algorithms to filter the data. After the data are cleaned from noise and undergo a cross-correlation procedure, the speed is estimated, and the driver’s behavior is determined, based on the real-time data.
Along the same direction, the authors of [22] focused on reducing the causes of motor vehicle collisions. Using IoT devices and IR (infrared) sensors, the vehicle’s current location and velocity are determined. The RFID (radio frequency identification) system determines the number of vehicles in motion. A module is used for GSM (global system for mobile) communications, and a solution is created by combining the three modules, each of which employs one sensor.

References

  1. Estimated Worldwide Motor Vehicle Production from 2000 to 2021. 2021. Available online: https://www.statista.com/statistics/262747/worldwide-automobile-production-since-2000/ (accessed on 19 October 2022).
  2. The Provincial Motor Vehicles Ordinance 1965. 2022. Available online: http://punjablaws.gov.pk/laws/189.html (accessed on 19 October 2022).
  3. Global Status Report on Alcohol and Health 2018. 2018. Available online: https://www.who.int/publications/i/item/9789241565639 (accessed on 26 October 2022).
  4. Global Status Report on Road Safety. 2018. Available online: https://www.who.int/publications/i/item/9789241565684 (accessed on 19 October 2022).
  5. Deaths by Inebriated Drivers In Pakistan. 2020. Available online: https://www.dawn.com/news/1598181 (accessed on 19 October 2022).
  6. Sanghvi, K. Drunk driving detection. Comput. Sci. Inf. Technol. 2018, 6, 24–30.
  7. Uzairue, S.; Ighalo, J.; Matthews, V.O.; Nwukor, F.; Popoola, S.I. IoT-enabled alcohol detection system for road transportation safety in smart city. In Proceedings of the International Conference on Computational Science and Its Applications, Melbourne, Australia, 2–5 July 2018; Springer: Cham, Switzerland, 2018; pp. 695–704.
  8. Gupta, A.; Ojha, S.; Kumar, V.; Singh, V.; Malav, V. Alcohol detection with vehicle controlling. Int. J. Eng. Manag. Res. (IJEMR) 2016, 6, 20–23.
  9. Deb, T. IoT based road traffic control system for Bangladesh. Int. J. Recent Technol. Eng. 2021, 10, 2277–3878.
  10. Chen, L.W.; Chen, H.M. Driver behavior monitoring and warning with dangerous driving detection based on the internet of vehicles. IEEE Trans. Intell. Transp. Syst. 2020, 22, 7232–7241.
  11. Zhang, M.; Chen, C.; Wo, T.; Xie, T.; Bhuiyan, M.Z.A.; Lin, X. SafeDrive: Online driving anomaly detection from large-scale vehicle data. IEEE Trans. Ind. Inform. 2017, 13, 2087–2096.
  12. Han, M.; Wu, J.; Bashir, A.K.; Yang, W.; Imran, M.; Nasser, N. Adversarial learning-based bias mitigation for fatigue driving detection in fair-intelligent iov. In Proceedings of the GLOBECOM 2020-2020 IEEE Global Communications Conference, Taipei, Taiwan, 7–11 December 2020; pp. 1–6.
  13. Halim, Z.; Sulaiman, M.; Waqas, M.; Aydın, D. Deep neural network-based identification of driving risk utilizing driver dependent vehicle driving features: A scheme for critical infrastructure protection. Ambient Intell. Humaniz. Comput. 2022.
  14. Park, D.E.; Park, S.E. Factors affecting perceived safety and enjoyment based on driver experience. Transp. Res. Part F Traffic Psychol. Behav. 2021, 83, 148–163.
  15. Andrews, J.; Shareef, Z.; Mohammed, M.; Nwobi, E.; Masri-zada, T.; Head, T.; Zohr, T.; Head, D.; Commissaris, R. A ‘Hands on’ Public Service Program to Help People Stay Sober and Safer on the Roadway. Sustainability 2021, 13, 10979.
  16. Šarac, M.; Pavlović, N.; Bacanin, N.; Al-Turjman, F.; Adamović, S. Increasing privacy and security by integrating a Blockchain Secure Interface into an IoT Device Security Gateway Architecture. Energy Rep. 2021, 7, 8075–8082.
  17. Hjalmar, D.; Jamente, C.; Pillai Sivakumar, I. How Blockchain and AI accelerate the application of IoT technology to keep drivers’ safe on the road. Proc. Stud.-Fac. Res. Day CSIS Pace Univ. 2018, B8-1.
  18. Dai, J.; Teng, J.; Bai, X.; Shen, Z.; Xuan, D. Mobile phone based drunk driving detection. In Proceedings of the 2010 4th International Conference on Pervasive Computing Technologies for Healthcare, Munich, Germany, 22–25 March 2010; pp. 1–8.
  19. Ma, C.; Dai, X.; Zhu, J.; Liu, N.; Sun, H.; Liu, M. Drivingsense: Dangerous driving behavior identification based on smartphone autocalibration. Mob. Inf. Syst. 2017, 2017, 9075653.
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  21. Qashlan, A.; Nanda, P.; He, X.; Mohanty, M. Privacy-preserving mechanism in smart home using blockchain. IEEE Access 2021, 9, 103651–103669.
  22. Ullah, A.; Sumiraj, A.; Shanawoaz, M.; Kabir, A. IoT based vehicle speed monitoring and controlling system for reducing accident occurs on the road in Bangladesh. J. Adv. Parallel Comput. 2020, 3, 1–17.
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  26. Altaf, A.; Abbas, H.; Iqbal, F.; Derhab, A. Trust models of internet of smart things: A survey, open issues, and future directions. J. Netw. Comput. Appl. 2019, 137, 93–111.
  27. Altaf, A.; Abbas, H.; Iqbal, F.; Khan, M.M.Z.M.; Daneshmand, M. Robust, Secure, and Adaptive Trust-Oriented Service Selection in IoT-Based Smart Buildings. IEEE Internet Things J. 2021, 8, 7497–7509.
  28. Altaf, A.; Abbas, H.; Iqbal, F.; Khan, M.M.Z.M.; Rauf, A.; Kanwal, T. Mitigating service-oriented attacks using context-based trust for smart cities in IoT networks. J. Syst. Archit. 2021, 115, 102028.
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  30. Altaf, A.; Iqbal, F.; Latif, R.; Yakubu, B.M.; Latif, S.; Samiullah, H. A Survey of Blockchain Technology: Architecture, Applied Domains, Platforms, and Security Threats. Soc. Sci. Comput. Rev. 2022, 08944393221110148.
  31. Fu, Y.; Li, C.; Yu, F.R.; Luan, T.H.; Zhang, Y. A Survey of Driving Safety With Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance. IEEE Trans. Intell. Transp. Syst. 2022, 23, 6142–6163.
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Subjects: Computer Science, Interdisciplinary Applications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Hamza Farooq
,
Ayesha Altaf
,
Faiza Iqbal
,
Juan Castanedo Galán
,
Daniel Gavilanes Aray
,
Imran Ashraf
View Times: 382
Revisions: 2 times (View History)
Update Date: 16 Jun 2023
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