Remote Gas Monitoring with Raspberry Pi |
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© 2025 by IJCTT Journal | ||
Volume-73 Issue-5 |
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Year of Publication : 2025 | ||
Authors : James Anankack Kadia | ||
DOI : 10.14445/22312803/IJCTT-V73I5P113 |
How to Cite?
James Anankack Kadia, "Remote Gas Monitoring with Raspberry Pi," International Journal of Computer Trends and Technology, vol. 73, no. 5, pp. 96-100, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I5P113
Abstract
Air pollution and gas leaks threaten human health and the environment. Conventional gas monitoring systems often depend on a stable internet connection, which limits their effectiveness in remote or infrastructure-poor regions. This paper presents a cost-effective, offline gas monitoring system with a Raspberry Pi, MQ-135 sensor, MCP3008 ADC, and SIM800L GSM module. The system enables real-time detection of gas concentrations and provides immediate SMS alerts without requiring internet access, addressing critical limitations of current IoT solutions. By emphasizing modularity, scalability, and offline functionality, this design offers a reliable and practical approach for monitoring gas levels in environments with unreliable or unavailable network connectivity.
Keywords
Gas Detection, GSM Alert System, MQ-135 Sensor, MCP3008, Raspberry Pi.
Reference
[1] Sourabh Jamadagni et al., “Gas Leakage and Fire Detection using Raspberry Pi,” 2019 3rd International Conference on Computing Methodologies and Communication, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ananya Chandran, and S. Kavitha, “A Smart Gas Stove with Gas Leakage Detection and Multistage Prevention System Using IoT,” International Journal of Modern Development in Engineering and Science, vol. 1, no. 9, pp. 5-9, 2022.
[Google Scholar] [Publisher Link]
[3] K. Lalitha et al., “IOT Enabled Pipeline Leakage Detection and Real Time Alert System in Oil and Gas Industry,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Yanjun Zuo, and Zhenyu Qi, “A Blockchain-Based IoT Framework for Oil Field Remote Monitoring and Control,” IEEE Access, vol. 10, pp. 2497-2514, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Duo Ma et al., “Automatic Detection and Counting System for Pavement Cracks Based on PCGAN and YOLO-MF,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 22166-22178, 2022.
[CrossRef] [Google Scholar] [Publisher Link]