A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City

Syed Haider Ali; Inam Ullah; Syed Ashraf Ali; M Ihtisham UL Haq; Niamat Ullah

doi:10.62762/TIS.2024.329126

Article Information

Published in ICCK Transactions on Intelligent Systematics

Volume/Issue Volume 1, Issue 2, 2024

Pages 49-57

Cited by 7 (Crossref) 5 (Scopus)

Abstract

This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute $CO_{2}$ gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages sent data. A graphical user interface (GUI) is created to facilitate data analysis. It undergoes a sequence of performance tests to verify the system's functionality. The results demonstrate that the system can accurately read parameters, process data, transfer information, and display readings.

Graphical Abstract

A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City

Keywords

Automobiles Intelligent Vehicle Microcontroller Cyber-Physical System Embedded System

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

Inam Ullah served as an Associate Editor of ICCK Transactions on Intelligent Systematics at the time of manuscript submission. To ensure the integrity of the peer-review process, Inam Ullah was not involved in the editorial handling, peer review, or decision-making process for this manuscript, which was handled independently by another editor. Syed Haider Ali is affiliated with the Pakistan Council of Scientific \& Industrial Research, Peshawar, Pakistan. The authors declare that this affiliation had no influence on the study design, data collection, analysis, interpretation, or the decision to publish, and that no other competing interests exist.

Ethical Approval and Consent to Participate

This study involved vehicle testing conducted on public roads using a privately owned 2013 Toyota Passo. The driving tests were performed by a member of the research team who provided informed consent for data collection. The data collected, including GPS trajectory, vehicle speed, and engine parameters, were used solely for research purposes and stored securely with access restricted to the research team. No personally identifiable information beyond anonymized vehicle performance metrics was retained in the final dataset. The GPS location data collected during testing was processed in accordance with applicable data protection regulations. As this study did not involve clinical interventions, medical procedures, or third-party human subjects, formal ethics committee approval was not required under the applicable institutional guidelines. Nevertheless, all procedures were conducted in compliance with local traffic regulations and with due regard for public safety.

References

Fridman, L., Brown, D. E., Glazer, M., Angell, W., Dodd, S., Jenik, B., ... & Reimer, B. (2019). MIT advanced vehicle technology study: Large-scale naturalistic driving study of driver behavior and interaction with automation. IEEE Access, 7, 102021-102038.
[CrossRef] [Google Scholar]
Naik, P., Kumbi, A., Telkar, N., Kotin, K., & Katti, K. C. (2017, December). An automotive diagnostics, fuel efficiency and emission monitoring system using CAN. In 2017 International Conference on Big Data, IoT and Data Science (BID) (pp. 14-17). IEEE.
[CrossRef] [Google Scholar]
Santa, J., Sanchez-Iborra, R., Rodriguez-Rey, P., Bernal-Escobedo, L., & Skarmeta, A. F. (2019). LPWAN-based vehicular monitoring platform with a generic IP network interface. Sensors, 19(2), 264.
[CrossRef] [Google Scholar]
Shafi, U., Safi, A., Shahid, A. R., Ziauddin, S., & Saleem, M. Q. (2018). Vehicle remote health monitoring and prognostic maintenance system. Journal of advanced transportation, 2018(1), 8061514.
[CrossRef] [Google Scholar]
Rimpas, D., Papadakis, A., & Samarakou, M. (2020). OBD-II sensor diagnostics for monitoring vehicle operation and consumption. Energy Reports, 6, 55-63.
[CrossRef] [Google Scholar]
Srinivasan, A. (2018, September). IoT cloud based real time automobile monitoring system. In 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE) (pp. 231-235). IEEE.
[CrossRef] [Google Scholar]
Mathe, S. E., Pamarthy, A. C., Kondaveeti, H. K., & Vappangi, S. (2022, February). A review on raspberry pi and its robotic applications. In 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-6). IEEE.
[CrossRef] [Google Scholar]
Karthikeyan, S., Raj, R. A., Cruz, M. V., Chen, L., Vishal, J. A., & Rohith, V. S. (2023). A systematic analysis on raspberry pi prototyping: Uses, challenges, benefits, and drawbacks. IEEE Internet of Things Journal, 10(16), 14397-14417.
[CrossRef] [Google Scholar]
Kumari, M., Kumar, A., & Khan, A. (2020, February). IoT based intelligent real-time system for bus tracking and monitoring. In 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) (pp. 226-230). IEEE.
[CrossRef] [Google Scholar]
Manalu, S. R., Moniaga, J., Hadipurnawan, D. A., & Sahidi, F. (2017). OBD-II and raspberry Pi technology to diagnose car’s machine current condition: study literature. Library Hi Tech News, 34(10), 15-21.
[CrossRef] [Google Scholar]
Pan, Y. J., Yu, T. C., & Cheng, R. S. (2017, May). Using OBD-II data to explore driving behavior model. In 2017 international conference on applied system innovation (ICASI) (pp. 1816-1818). IEEE.
[CrossRef] [Google Scholar]
Moniaga, J. V., Manalu, S. R., Hadipurnawan, D. A., & Sahidi, F. (2018, March). Diagnostics vehicle’s condition using OBD-II and raspberry pi technology: study literature. In Journal of Physics: Conference Series (Vol. 978, No. 1, p. 012011). IOP Publishing.
[CrossRef] [Google Scholar]
Ben Othmane, L., Alvarez, V., Berner, K., Fuhrmann, M., Fuhrmann, W., Guss, A., & Hartsock, T. (2018, September). A low-cost fleet monitoring system. In 2018 IEEE International Smart Cities Conference (ISC2) (pp. 1-2). IEEE.
[CrossRef] [Google Scholar]
Cumin, J., Novoselović, D., Marić, D., & Šolić, T. (2023, December). Maintenance of Automobiles and Motorcycles Through Prism of OBD II Diagnostic Tools. In International Conference on Organization and Technology of Maintenance (pp. 171-183). Cham: Springer Nature Switzerland.
[CrossRef] [Google Scholar]
Rivera-Campoverde, N. D., Muñoz-Sanz, J. L., & Arenas-Ramirez, B. D. V. (2021). Estimation of pollutant emissions in real driving conditions based on data from OBD and machine learning. Sensors, 21(19), 6344.
[CrossRef] [Google Scholar]
Türk, E., & Challenger, M. (2018, May). An android-based IoT system for vehicle monitoring and diagnostic. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
[CrossRef] [Google Scholar]
Wahl, H., Naz, E., Kaufmann, C., & Mense, A. (2016). Simplifying the complexity for vehicle health management system. In 2016 7th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2016 (pp. 2-6).
[Google Scholar]
Baghli, L., Benmansour, K., & Djemai, M. (2014, November). Development of a data acquisition and tracking system for vehicles. In 3rd International Symposium on Environmental Friendly Energies and Applications (EFEA) (pp. 1-6). IEEE.
[CrossRef] [Google Scholar]
Nugroho, S. A., Ariyanto, E., & Rakhmatsyah, A. (2018, May). Utilization of Onboard Diagnostic II (OBD-II) on four wheel vehicles for car data recorder prototype. In 2018 6th International Conference on Information and Communication Technology (ICoICT) (pp. 7-11). IEEE.
[CrossRef] [Google Scholar]
HEM data » OBD mini Logger™. (n.d.). HEM Data. Retrieved from https://hemdata.com/products/dawn/obd-mini-logger/
[Google Scholar]
Carchip fleet pro data logger. (n.d.). Environmental XPRT. Retrieved from https://www.environmental-expert.com/products/carchip-model-fleet-pro-data-logger-311723
[Google Scholar]
Popa, L., Berdich, A., & Groza, B. (2022). CarTwin—Development of a digital twin for a real-world in-vehicle CAN network. Applied Sciences, 13(1), 445.
[CrossRef] [Google Scholar]
Smith, K., & Miller, J. (2013, October). OBDII data logger design for large-scale deployments. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) (pp. 670-674). IEEE.
[CrossRef] [Google Scholar]
Ramai, C., Ramnarine, V., Ramharack, S., Bahadoorsingh, S., & Sharma, C. (2022). Framework for building low-cost OBD-II data-logging systems for battery electric vehicles. Vehicles, 4(4), 1209-1222.
[CrossRef] [Google Scholar]
Sim, A. X. A., & Sitohang, B. (2014, November). OBD-II standard car engine diagnostic software development. In 2014 International Conference on Data and Software Engineering (ICODSE) (pp. 1-5). IEEE.
[CrossRef] [Google Scholar]
Arduino.cc. (n.d.). Arduino UNO Rev3. Retrieved from https://docs.arduino.cc/hardware/uno-rev3.
[Google Scholar]
Abediasl, H., Ansari, A., Hosseini, V., Koch, C. R., & Shahbakhti, M. (2024). Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 238(12), 3779-3793.
[CrossRef] [Google Scholar]
Meseguer, J. E., Toh, C. K., Calafate, C. T., Cano, J. C., & Manzoni, P. (2017). Drivingstyles: A mobile platform for driving styles and fuel consumption characterization. Journal of Communications and networks, 19(2), 162-168.
[CrossRef] [Google Scholar]
MathWorks, Inc. (2021). ThingSpeak™ for IoT projects: Measure, analyze and act on data in the cloud. https://www.mathworks.com/products/thingspeak.html
[Google Scholar]

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Cite This Article

APA Style

Ali, S. H., Ullah, I., Ali, S. A., Haq, M. I. U, & Ullah, N. (2024). A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City. ICCK Transactions on Intelligent Systematics, 1(2), 49-57. https://doi.org/10.62762/TIS.2024.329126

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TY - JOUR
AU - Ali, Syed Haider
AU - Ullah, Inam
AU - Ali, Syed Ashraf
AU - Haq, M Ihtisham UL
AU - Ullah, Niamat
PY - 2024
DA - 2024/09/20
TI - A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City
JO - ICCK Transactions on Intelligent Systematics
T2 - ICCK Transactions on Intelligent Systematics
JF - ICCK Transactions on Intelligent Systematics
VL - 1
IS - 2
SP - 49
EP - 57
DO - 10.62762/TIS.2024.329126
UR - https://www.icck.org/article/abs/TIS.2024.329126
KW - Automobiles
KW - Intelligent Vehicle
KW - Microcontroller
KW - Cyber-Physical System
KW - Embedded System
AB - This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute $CO_{2}$ gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages sent data. A graphical user interface (GUI) is created to facilitate data analysis. It undergoes a sequence of performance tests to verify the system's functionality. The results demonstrate that the system can accurately read parameters, process data, transfer information, and display readings.
SN - 3068-5079
PB - Institute of Central Computation and Knowledge
LA - English
ER -

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@article{Ali2024A,
  author = {Syed Haider Ali and Inam Ullah and Syed Ashraf Ali and M Ihtisham UL Haq and Niamat Ullah},
  title = {A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {1},
  number = {2},
  pages = {49-57},
  doi = {10.62762/TIS.2024.329126},
  url = {https://www.icck.org/article/abs/TIS.2024.329126},
  abstract = {This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute \$CO\_{2}\$ gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages sent data. A graphical user interface (GUI) is created to facilitate data analysis. It undergoes a sequence of performance tests to verify the system's functionality. The results demonstrate that the system can accurately read parameters, process data, transfer information, and display readings.},
  keywords = {Automobiles, Intelligent Vehicle, Microcontroller, Cyber-Physical System, Embedded System},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}

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