-
CiteScore
1.44
Impact Factor
IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 2: 49-57

Free to Read | Research Article | 20 September 2024
1 Pakistan Council of Scientific & Industrial Research, Peshawar, Pakistan
2 Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
3 Iqra National University Peshawar, Pakistan
4 University of Agriculture Peshawar, Peshawar, Pakistan
5 Department of Computer Science, University of Malakand, Pakistan
* Corresponding Author: Inam Ullah, [email protected]
Received: 24 August 2024, Accepted: 13 September 2024, Published: 20 September 2024  
Cited by: 2  (Source: Web of Science) , 4  (Source: Google Scholar)
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 CO2 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 qualification 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

Funding

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. IECE Transactions on Intelligent Systematics, 1(2), 49–57. https://doi.org/10.62762/TIS.2024.329126

References
  1. 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.
    [Google Scholar]
  2. 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.
    [Google Scholar]
  3. 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.
    [Google Scholar]
  4. 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.
    [Google Scholar]
  5. BinMasoud, A., & Cheng, Q. (2019, November). Design of an iot-based vehicle state monitoring system using raspberry pi. In 2019 International Conference on Electrical Engineering Research & Practice (ICEERP) (pp. 1-6). IEEE.
    [Google Scholar]
  6. Silva, M., Vieira, E., Signoretti, G., Silva, I., Silva, D., & Ferrari, P. (2018). A customer feedback platform for vehicle manufacturing compliant with industry 4.0 vision. Sensors, 18(10), 3298.
    [Google Scholar]
  7. 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.
    [Google Scholar]
  8. 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.
    [Google Scholar]
  9. 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.
    [Google Scholar]
  10. 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.
    [Google Scholar]
  11. 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.
    [Google Scholar]
  12. Baek, S. H., & Jang, J. W. (2015). Implementation of integrated OBD-II connector with external network. Information Systems, 50, 69-75.
    [Google Scholar]
  13. 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.
    [Google Scholar]
  14. Hassan, M. A., Javed, R., Granelli, F., Gen, X., Rizwan, M., Ali, S. H., ... & Ullah, S. (2023, March). Intelligent transportation systems in smart city: a systematic survey. In 2023 International Conference on Robotics and Automation in Industry (ICRAI) (pp. 1-9). IEEE.
    [Google Scholar]
  15. Dabarera, W. N. S., Jayatilake, N. T., Jayathissa, R. H. N. S., & Weerawardane, T. L. (2022). Towards an IoT based Vehicle Management System for Vehicle Tracking & Vehicle Diagnostics with OBD2 telem.
    [Google Scholar]
  16. 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.
    [Google Scholar]
  17. 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]
  18. 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.
    [Google Scholar]
  19. 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. 56IECE Transactions on Intelligent Systematics In 2018 6th International Conference on Information and Communication Technology (ICoICT) (pp. 7-11). IEEE.
    [Google Scholar]
  20. OBD Mini Logger [Online]. Available: https://hemdata.com/products/dawn/ obd-mini-logger/
    [Google Scholar]
  21. CarChip - Model Fleet Pro - Data Logger [Online]. Available: https://www.environmental-expert.com/products/ carchip-model-fleet-pro-data-logger-311723
    [Google Scholar]
  22. CarTwin Data Logger [Online]. Available: https://cartwin.ai/data-logger/
    [Google Scholar]
  23. DashDyno SPD ProPack [Online]. Available: http://www.auterraweb.com/ dashdynopropack.html
    [Google Scholar]
  24. IOSiX OBD-II Datalogger [Online]. Available: http://caflor.net/datalogger.html
    [Google Scholar]
  25. De Rango, F., Tropea, M., Serianni, A., & Cordeschi, N. (2022). Fuzzy inference system design for promoting an eco-friendly driving style in IoV domain. Vehicular Communications, 34, 100415.
    [Google Scholar]
  26. Signoretti, G., Silva, M., Dias, A., Silva, I., Silva, D., & Ferrari, P. (2019, June). Performance evaluation of an edge obd-ii device for industry 4.0. In 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0&IoT) (pp. 432-437). IEEE.
    [Google Scholar]
  27. Kondaveeti, H. K., Kumaravelu, N. K., Vanambathina, S. D., Mathe, S. E., & Vappangi, S. (2021). A systematic literature review on prototyping with Arduino: Applications, challenges, advantages, and limitations. Computer Science Review, 40, 100364.
    [Google Scholar]
  28. 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.
    [Google Scholar]
  29. Balakrishna, S., & Thirumaran, M. (2020). Semantic interoperability in IoT and big data for health care: a collaborative approach. In Handbook of data science approaches for biomedical engineering (pp. 185-220). Academic Press.
    [Google Scholar]

Article Metrics
Citations:

Crossref

0

Scopus

2

Web of Science

2
Article Access Statistics:
Views: 4119
PDF Downloads: 367

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Intelligent Systematics

IECE Transactions on Intelligent Systematics

ISSN: 2998-3355 (Online) | ISSN: 2998-3320 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.