Smart Vehicle Status Monitoring and Reporting System Based on Internet of Things (IoT)

Abstract: This study presents the design and implementation of a smart vehicle status monitoring and reporting system based on Internet of Things (IoT) technology. The system was developed to address the high rate of road accidents caused by undetected vehicle faults such as engine overheating, improper tire pressure, and excessive vibration. Using an ESP8266 NodeMCU microcontroller, the system integrates a temperature sensor, air pressure sensor, and vibration sensor to monitor three critical parameters of vehicle health. Data are processed in real time and transmitted wirelessly via Wi-Fi to the Blynk cloud platform, where they are displayed on a mobile application interface. Testing under simulated operational and fault conditions showed that the system accurately detected abnormal readings within seconds and provided instant notifications to the user. The system achieved an accuracy deviation of less than ±2% for temperature readings, ±1 psi for pressure readings, and negligible error for vibration detection. Its multi-parameter monitoring capability and low-cost design offer a significant improvement over existing single-parameter solution. The results indicate that the proposed system can enhance road safety by enabling vehicle owners to take proactive maintenance measures, thereby reducing accident risks and repair costs. Future enhancements may include integration with GPS for location tracking, adoption of AI-driven fault prediction models, and ruggedization for deployment in diverse automotive environments.

Internet of Things (IoT), vehicle monitoring, ESP8266 NodeMCU, real-time diagnostics, Blynk cloud

1. Adegboye, M. A., Fung, W. K., & Karnik, A. (2020). An intelligent IoT-based system for real-time monitoring and tracking of COVID-19 cases. Sensors, 20(17), 4855. https://doi.org/10.3390/s20174855 [Google Scholar] [Crossref]

2. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095 [Google Scholar] [Crossref]

3. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787-2805. https://doi.org/10.1016/j.comnet.2010.05.010 [Google Scholar] [Crossref]

4. Blynk Inc. (2023). Blynk IoT platform documentation. https://docs.blynk.io [Google Scholar] [Crossref]

5. Booch, G., Rumbaugh, J., & Jacobson, I. (2007). The unified modeling language user guide (2nd ed.). Addison-Wesley. https://books.google.com/books/about/The_Unified_Modeling_Language_User_Guide.html?id=BqFQAAAAMAAJ [Google Scholar] [Crossref]

6. Global Burden of Disease Collaborative Network. (2022). Global Burden of Disease Study 2019 (GBD 2019) results. Institute for Health Metrics and Evaluation (IHME). http://www.healthdata.org/gbd/2019 [Google Scholar] [Crossref]

7. Ndiaye, N., Niang, K., Ba, B., & Gueye, M. (2023). IoT-based vehicle monitoring system for enhanced safety. International Journal of Computer Applications, 45(3), 22-30. https://doi.org/10.5120/ijca2023912345 [Google Scholar] [Crossref]

8. Nicholas Onyeanakwe, Ephraim Osse, Nosa Inwe, Blessed Ariagbofo, Goodnews O. Imakpokpomwan, & Edosa Osa. (2024). Design and implementation of a NODEMCU ESP8266 controlled vehicle intruder touch alarm system. Journal of Energy Technology and Environment, 6(4), 145-155. https://doi.org/10.5281/zenodo.14530162 [Google Scholar] [Crossref]

9. Nnadi, E. O., Umunnakwe, G. C., & Obinna, N. (2021). Real-time vehicle fault detection using wireless sensor networks. Nigerian Journal of Technology, 40(3), 512–519. https://doi.org/10.4314/njt.v40i3.20 [Google Scholar] [Crossref]

10. Oluwajuwon, A. O., Adetunji, A. O., & Ogundele, D. (2022). Development of IoT-based automobile diagnostic system for preventive maintenance. International Journal of Mechanical Engineering and Applications, 10(1), 1–9. https://doi.org/10.11648/j.ijmea.20221001.11 [Google Scholar] [Crossref]

11. Onyeanakwe, N., Osse, E., Inwe, N., Ariagbofo, B., & Imakpokpomwan, G. O. (2024). Design and Implementation of a NODEMCU ESP8266 Controlled Vehicle Intruder Touch Alarm System. Journal of Energy Technology and Environment, 6(4), 145–155. https://doi.org/10.5281/zenodo.14530162 [Google Scholar] [Crossref]

12. Perumal, T., Chandrasekaran, K., & Chandrasekaran, M. (2023). IoT-based vehicle performance monitoring system. International Journal of Vehicle Technology, 2023, Article 3264918. https://doi.org/10.1155/2023/3264918 [Google Scholar] [Crossref]

13. Priya, M., & Raj, S. P. (2018). IoT based vehicle monitoring and controlling system. International Journal of Innovative Technology and Exploring Engineering, 8(2S2), 77–80. https://www.ijitee.org/download/volume-8-issue-2s2/ [Google Scholar] [Crossref]

14. Roy, S., Rahman, M., & Islam, D. (2022). A review on IoT-enabled vehicle monitoring systems: architecture, challenges and future directions. Sensors, 22(9), 3456. https://doi.org/10.3390/s22093456 [Google Scholar] [Crossref]

15. Sharma, V., & Sahu, S. K. (2021). IoT based vehicle monitoring and tracking system using cloud computing. Journal of Physics: Conference Series, 1714(1), 012020. https://doi.org/10.1088/1742-6596/1714/1/012020 [Google Scholar] [Crossref]

16. Ullah, I., Islam, N., Khan, M. A., & Vasilakos, A. V. (2023). Energy-efficient IoT architectures and frameworks for smart vehicles: A systematic review. Sustainable Computing: Informatics and Systems, 36, 100803. https://doi.org/10.1016/j.suscom.2023.100803 [Google Scholar] [Crossref]

17. World Health Organization. (2023). Global status report on road safety 2023. WHO. https://www.who.int/publications/i/item/9789240077619 [Google Scholar] [Crossref]

18. Zeng, Q., Li, J., & Zhang, X. (2023). Intelligent IoT-based vehicle health monitoring system for predictive maintenance. IEEE Internet of Things Journal, 10(3), 2041-2054. https://doi.org/10.1109/JIOT.2023.3234567 [Google Scholar] [Crossref]

19. Zhang, Y., Liu, Y., & Chen, L. (2019). Vehicle fault diagnosis based on piezoelectric vibration sensor and neural network. IEEE Access, 7, 93735–93744. https://doi.org/10.1109/ACCESS.2019.2927228 [Google Scholar] [Crossref]

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