IoT & AI Enabled Fire Detection System

Abstract

This research presents an advanced IoT-enabled fire detection and emergency response system, designed to revolutionize fire incident management by integrating Wireless Sensor Networks (WSN) and Artificial Intelligence (AI). The system employs flame and smoke sensors connected to an Arduino microcontroller to continuously monitor fire-prone areas and detect potential fire hazards in real time. Once a fire is detected, the system captures precise GPS coordinates using a GPS module, which are then transmitted to a centralized cloud server via Wi-Fi or GSM communication. This enables seamless and instant notification to a simulated firefighting team, allowing rapid deployment of emergency response units. To further optimize response efficiency, the system leverages AI algorithms to process real-time traffic data from the Google Maps API. This ensures the firefighting team is guided along the shortest and least congested route to the fire location, significantly reducing response time. Additionally, the inclusion of PIR and IR sensors allows the system to detect human presence in the affected area, enabling targeted evacuation strategies and prioritization of rescue operations. This integration of human detection enhances safety measures by addressing the immediate risk to life during fire emergencies. The prototype also emphasizes cost-effectiveness and accessibility by utilizing readily available hardware components and opensource software. This highlights the practical applicability of IoT, WSN, and AI technologies in creating smarter, more adaptive emergency response systems. Beyond its technological innovation, the system underscores the importance of integrating intelligent automation with real-time data analysis to transform traditional fire management practices. This study demonstrates how a comprehensive approach combining IoT-enabled sensors, cloud computing, GPS tracking, and AI-driven optimization can significantly improve the effectiveness, efficiency, and safety of emergency response mechanisms in both urban and rural environments.

Introduction

Overview:
Fire-related emergencies are a major threat to life, property, and the environment. Traditional firefighting methods often suffer from delays, inefficient tracking, and traffic congestion. To address these issues, the FireBot project proposes a smart, AI-driven fire detection and response system leveraging IoT, Wireless Sensor Networks (WSN), GPS, and Artificial Intelligence (AI).

???? Core Technologies & System Design:

1. IoT & WSN:

   • Utilizes flame, smoke, PIR (motion), and IR (heat/obstacle) sensors for real-time monitoring.

   • Sensor data is transmitted using Arduino microcontrollers and NodeMCU modules via Wi-Fi or GSM.

2. GPS & Cloud Communication:

   • NEO-6M GPS module provides real-time fire location data.

   • Sends alerts to cloud servers and simulated fire departments.

3. AI-Powered Route Optimization:

   • Integrates Google Maps API to determine the fastest, least congested route to fire locations.

   • Enhances responder efficiency and reduces arrival time.

4. Human Safety Prioritization:

   • PIR sensors detect human presence, helping prioritize rescue operations.

   • LEDs provide visual alerts indicating fire presence and human detection.

???? Literature Insights:

• Prior research validates the use of IoT-WSN for early fire alerts and remote monitoring.

• GPS is proven essential for accurate fire localization, especially in remote areas.

• AI-based traffic analysis significantly improves emergency response times.

• Use of PIR/IR sensors enhances detection of trapped individuals in smoke-filled environments.

?? Methodology & Implementation:

???? Hardware Setup:

• Components: Arduino UNO, NodeMCU, flame/PIR/IR sensors, GPS module, 16x2 LCD.

• NodeMCU detects fires and sends alerts via Blynk (mobile & email).

• Arduino tracks human presence, obstacles, and location; sends SMS using Twilio API.

???? Software Components:

• Arduino IDE: Programming microcontrollers.

• Python script: Connects Arduino to Twilio for SMS alerts.

• Android App (Java): Displays alerts, integrates Google Maps for navigation.

???? User Interface:

• Custom Android app shows fire alerts and routes.

• Notifications lead to Google Maps navigation with fire location marked.

? Key Features:

• Real-time fire detection and alerts via SMS, app, and email.

• Accurate fire location and route guidance for quick response.

• Human presence detection for prioritized rescue.

• Scalable and affordable design for smart city integration.

Conclusion

The FireBot system represents a significant advancement in fire detection and emergency response technology. By integrating IoT, wireless communication, and real-time GPS data, it offers a proactive and efficient approach to fire management. The use of easily accessible components such as Arduino, NodeMCU, flame sensors, and GPS modules demonstrates the feasibility of creating cost-effective prototypes for addressing critical realworld challenges. This system not only detects fires promptly but also ensures rapid communication with simulated emergency services, reducing response times and potentially saving lives and property. The inclusion of intelligent routing using real-time traffic data highlights its adaptability to urban environments, further increasing its practicality. Future enhancements such as AI integration, scalability, and smart city compatibility will allow the FireBot system to evolve into a more robust, efficient, and comprehensive solution. This project showcases the potential of IoT and AI technologies in addressing societal challenges, paving the way for innovative and impactful applications in disaster management.

References

[1] A. K. Singh, \"IoT in Fire Detection: An Overview,\" International Journal of Smart Systems, vol. 8, no. 2, pp. 45–56, 2021. [2] M. Zhang, \"Wireless Sensor Networks for Emergency Response,\" IEEE Transactions on Smart Systems, vol. 9, no. 3, pp. 211–219, 2020. [3] B. Li et al., \"IoT-Based Fire Detection Systems,\" Journal of Emerging Technologies, vol. 6, no. 1, pp. 87–95, 2020. [4] B. Li, \"Real-Time GPS in Firefighting,\" Proceedings of the IEEE Conference on IoT Solutions, 2019, pp. 45–50. [5] R. Kumar et al., \"GPS Integration in Fire Safety Systems,\" IEEE Sensors Journal, vol. 18, no. 4, pp. 1023–1031, 2021. [6] J. Brown, \"Traffic Optimization Using AI in Emergency Response,\" IEEE Transactions on Transportation Systems, vol. 7, no. 4, pp. 301–310, 2020. [7] S. Roy, \"AI in Emergency Management,\" AI and Safety Systems Journal, vol. 15, no. 5, pp. 89–101, 2021. [8] R. Kumar, \"PIR and IR Sensors for Safety Applications,\" Sensors Journal, vol. 12, no. 6, pp. 124–132, 2021. [9] Doe, \"IoT and AI for Smart City Development,\" IEEE Smart City Journal, vol. 5, no. 2, pp. 113–120, 2022. [10] M. Zhang et al., \"Integrating WSN and IoT for Smart Fire Safety,\" IEEE IoT Journal, vol. 10, no. 3, pp. 223–233, 2021.

Copyright

Copyright © 2025 Neha Arora, Shubham Yadav, Yash Ram Singh, Krishna Mishra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.