IoT-Based Smart Lake Fire Detection and Alarming System
The Smart Lake Fire Prevention System addresses the critical need for enhanced fire prevention in lakeside environments, which are increasingly vulnerable to fires due to both natural and human-induced factors. Traditional fire detection methods often fall short in providing an immediate and scaled response, leading to potential delays in extinguishing fires. Given that time is of the essence, a more efficient and cost-effective system is required—one that can promptly alert and initiate swift action to safeguard both the environment and the communities in arid regions. The system uses strategically placed temperature and smoke sensors to monitor environmental conditions in real time. A microcontroller with Wi-Fi or GSM capabilities collects data and sends it via the MQTT protocol to AWS IoT Core. AWS Lambda analyzes the data to detect fire risks based on predefined thresholds. If a risk is identified, alerts are sent through Amazon SNS via SMS notifications to relevant authorities for rapid response. The system effectively tracks environmental conditions and transmits data to the cloud via AWS IoT Core. Testing confirmed its ability to detect fire hazards and send reliable SMS notifications through AWS SNS, all while being easy to deploy. Its scalable design allows for additional sensors, enhancing monitoring across larger lakeside areas. This cost-effective, time-efficient system simplifies traditional fire detection, providing rapid alerts and supporting firefighting efforts. Overall, it significantly advances the protection of natural ecosystems and public safety through IoT-enabled cloud solutions.
Authors
- C. S. (ORCID: https://orcid.org/0000-0002-2657-7153)
- Shubhi Srivastava
- Neha Jadhav (ORCID: https://orcid.org/0009-0005-5464-3559)
- Rama Bansidhar Dan
- SriHarshini Devi Kaipa
- K R Chinmay
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/c2i663243.2024.10895908
- Citations
- 1
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.36