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IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 2: 49-57

Free Access | 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: inam@gachon.ac.kr
Received: 24 August 2024, Accepted: 13 September 2024, Published: 20 September 2024  

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

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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[12] Baek, S. H., & Jang, J. W. (2015). Implementation of integrated OBD-II connector with external network. Information Systems, 50, 69-75.

[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.

[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.

[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.

[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.

[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).

[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.

[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.

[20] OBD Mini Logger [Online]. Available: https://hemdata.com/products/dawn/ obd-mini-logger/

[21] CarChip - Model Fleet Pro - Data Logger [Online]. Available: https://www.environmental-expert.com/products/ carchip-model-fleet-pro-data-logger-311723

[22] CarTwin Data Logger [Online]. Available: https://cartwin.ai/data-logger/

[23] DashDyno SPD ProPack [Online]. Available: http://www.auterraweb.com/ dashdynopropack.html

[24] IOSiX OBD-II Datalogger [Online]. Available: http://caflor.net/datalogger.html

[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.

[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.

[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.

[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.

[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.


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

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