Design and Development of an Automated Carbon Monoxide Detection with Emergency Alert for Vehicle Safety

This study proposes a compact, real-time carbon monoxide (CO) detection system for in- vehicle environments using the MQ-7 sensor and Raspberry Pi 4 integrated with a SIM7600X GSM module. The system is designed to monitor CO levels and automatically alert emergency contacts via SMS when dangerous thresholds are detected. Testing was conducted in a vehicle at five distinct locations to evaluate sensor placement and communication response. Results show that dashboard placement yields the fastest detection (5 seconds) and consistent GSM message delivery within 2–3 seconds. This system addresses the limitations of standalone CO detectors by introducing emergency communication capabilities. The solution is compact, cost-effective, and suitable for retrofitting into existing vehicles. Findings suggest the system's potential for improving automotive safety and offer a scalable model for real-time hazardous gas alert systems.

Carbon Monoxide, GSM, Raspberry Pi, MQ-7

1. Ahmed, M., Rehman, M., & Farooq, U. (2021). Predictive analysis of toxic gas detection using machine learning for embedded safety systems. International Journal of Smart Sensor https://doi.org/10.1016/ijsst.2021.45 [Google Scholar] [Crossref]

2. Chen, X., Li, Z., & Wang, J. (2019). An integrated CO gas detection and alerting system using GSM and cloud interface. Procedia Computer Science, 155, 456 463. https://doi.org/10.1016/j.procs.2019.08.062 [Google Scholar] [Crossref]

3. Dey, A., Saha, B., & Ray, S. (2018). GSM based gas leakage detection system with SMS alert. 2018 4th International Conference on Computing Communication and Automation (ICCCA), 1–6. https://doi.org/10.1109/CCAA.2018.8777510. [Google Scholar] [Crossref]

4. Gupta, S., & Johnson, R. (2020). Optimal placement strategies for gas sensors in enclosed vehicle cabins. Journal of Automotive Safety and Environment, 14(3), 125–134. [Google Scholar] [Crossref]

5. Islam, M. S., Haque, M. A., & Mamun, A. A. (2020). Design and implementation of a low- cost CO gas detection system using Raspberry Pi. Journal of Electronics and Communication Engineering, 5(2), 88–93. [Google Scholar] [Crossref]

6. Kim, J. H., Park, M., & Lee, S. (2019). Design of a multi-functional automotive gas safety system using sensor fusion and GSM communication. IEEE Transactions on Intelligent Transportation Systems, 20(7), 2580–2590. https://doi.org/10.1109/TITS.2019.2895310 [Google Scholar] [Crossref]

7. Kumar, R., & Pal, S. (2018). IoT-based smart gas monitoring system with emergency alert via SMS. International Journal of Embedded Systems and Applications, 8(3), 23–31. https://doi.org/10.5121/ijesa.2018.8302 [Google Scholar] [Crossref]

8. Lee, Y., & Tanaka, H. (2020). The role of automation in CO detection systems: Lessons from industrial and vehicular applications. Sensors & Control Letters, 17(4), 202–211. [Google Scholar] [Crossref]

9. Mahmud, S. H., Hossain, F., & Rahman, M. (2021). GSM-enabled gas leakage monitoring system for vehicular safety applications. International Conference on Electrical and Computing Technologies and Applications (ICECTA), 1–5. https://doi.org/10.1109/ICECTA51268.2021.9673081 [Google Scholar] [Crossref]

10. Manohar, S., & Srinivasan, R. (2017). A smart system for detection of carbon monoxide gas using Raspberry Pi. International Journal of Computer Applications, 165(9), 7–10. https://doi.org/10.5120/ijca2017914189 [Google Scholar] [Crossref]

11. Nordin, M. A., Zulkifli, M. F., & Hasan, M. K. (2022). Development of CO detector system with emergency alert for vehicle cabins. Malaysian Journal of Engineering and Technology, 12(1), 33–41. [Google Scholar] [Crossref]

12. Rahman, M., & Chowdhury, F. (2020). A low-cost intelligent gas monitoring and alert system using embedded Linux. Journal of Embedded Systems and Applications, 6(4), 55– 63. [Google Scholar] [Crossref]

13. Rizwan, M., & Jamil, F. (2019). Analysis of GSM-based alert systems in vehicular environments. Journal of Automotive Embedded Systems, 5(1), 15–22. [Google Scholar] [Crossref]

14. Sani, M. F. A., & Nor, N. M. (2021). Implementation of MQ-7 CO gas sensor for vehicle safety monitoring system using Raspberry Pi. Proceedings of the 2021 IEEE Symposium on Smart Electronic Systems (iSES), 99–103. https://doi.org/10.1109/iSES52930.2021.9633684 [Google Scholar] [Crossref]

15. Sarker, S., Rahman, T., & Hossain, M. (2020). GSM-based toxic gas detection system for public transport. International Journal of Computer Science and Mobile Computing, 9(5), 143–150. [Google Scholar] [Crossref]

• LeafQuest: A Mobile-Based Augmented Reality for Plant Placement, Discovery, and Growth
• Participatory Ergonomic Intervention Approach on Musculoskeletal Disorder (MSD) in Construction Sectors: A Systematic Review
• Integrating GIS into Traffic Incident Management: A Web-Based System
• RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop
• Educational Technology Course Design in Pre-Service Teachers Education: A Bibliometric Review of the Research Landscape