Smoke Detection Security Using ESP8266

Abstract

Fire accidents continue to pose a serious threat to human life and property, highlighting the importance of reliable and rapid smoke detection systems. Conventional smoke detectors are effective within a limited range but lack the ability to provide real-time alerts to users at remote locations. To address this limitation, this paper presents an IoT-based smoke detection system using the ESP8266 microcontroller. The system integrates a gas sensor for smoke detection and utilizes the Wi-Fi capability of the ESP8266 to send instant notifications to a mobile application. In addition, a local alarm is activated to ensure immediate awareness of nearby occupants. The designed system offers an affordable and expandable solution that can be effectively deployed in both smart homes and industrial environments.”. Experimental results demonstrate that the system is capable of detecting smoke promptly and providing timely alerts, thereby enhancing safety and reducing potential risks.

Introduction

Fire hazards pose a serious threat to life, property, and the environment. Traditional smoke detectors are limited to on-site alerts (buzzers/sirens), which are ineffective when users are away. Delayed detection is a major cause of fire-related damage. With IoT, it's now possible to build smarter, real-time, remote-accessible fire detection systems.

2. Literature Review:

• 2025: Scalable IoT systems using ESP8266/ESP32 with cloud platforms and AI for accurate, real-time detection.

• 2024: Smart home integration enabling automated responses (e.g., turning off appliances, unlocking exits).

• 2023: AI/ML used to analyze sensor data and reduce false alarms.

• 2022: Integration of cloud and edge computing improved speed and reduced internet dependency.

• 2021: Early IoT systems with MQ sensors and mobile alerts (e.g., Blynk) showed the potential of connected fire safety.

3. Methodology:

A. Proposed System Components:

• Microcontroller: ESP8266 (NodeMCU) with Wi-Fi.

• Sensors: MQ-2 or MQ-135 to detect smoke/gases.

• Actuators: Buzzer and LED for local alerts.

• Connectivity: Wi-Fi for app notifications; optional Twilio for SMS/voice.

• Power: 5V USB supply, regulated to 3.3V for ESP8266.

B. System Working:

1. Initialization: Wi-Fi setup, sensor warm-up, baseline calibration.

2. Sensing: Periodic sampling and filtering using moving averages.

3. Decision Logic: Threshold-based alerts (moderate/warning vs. high/alarm).

4. Local Alarm: Buzzer and LED activated upon alarm detection.

5. Remote Alerts: Push via Blynk or SMS/voice via Twilio webhook.

6. Reliability: Auto-reconnect, watchdog resets, debounce to prevent spam.

7. Data Visualization: App displays sensor trends and alerts.

8. Optional Features: AI-based filtering, battery backup, OTA firmware updates.

4. Implementation:

Hardware Setup:

• MQ sensor connected to ESP8266 (A0 analog input).

• LED and buzzer connected to GPIO pins.

• Powered via 5V USB; all components share ground.

Software:

• Code via Arduino IDE.

• Sensor readings trigger local and remote alerts.

• Blynk app for push notifications; Twilio for SMS/voice.

• Wi-Fi reconnect and debounce features implemented.

5. Results & Discussion:

• Local Response: LED and buzzer respond immediately to smoke.

• Mobile Alerts: Real-time updates sent to users' smartphones.

• System Reliability: Reconnects after Wi-Fi loss; prevents repeated alerts.

• Conclusion: The system is affordable, reliable, and significantly better than traditional detectors due to remote monitoring and faster alerts.

? Key Benefits:

• Real-time remote alerts

• Smart home integration

• Affordable and scalable

• Reliable and responsive

• Customizable with AI/ML and cloud features

Conclusion

This paper introduced an IoTbased smoke detection system using the ESP8266, MQ sensors, and mobile services like Blynk and Twilio. The system effectively detected smoke, activated ala rms, and sent real-time alerts. It is more reliable and cheaper than regular smoke dete ctors and can easily be added to smart homes.

References

[1] Blynk IoT Platform Documentation – https://docs.blynk.io,2020. [2] R. Sharma and K. Mehta, “IoT-Based Fire Detection with Mobile Notifications,” International Journal of Advanced Research in Computer Science, vol. 12, no. 4, pp. 45–50, 2021. [3] M. Ali, S. Thomas, and H. Zhao, “Integration of Cloud and Edge Computing for IoT-Based Fire Detection,” IEEE International Conference on IoT Applications, pp. 120–125, 2022. [4] S. Verma and P. Gupta, “Artificial Intelligence in IoT Smoke Detection Systems,” Journal of Emerging Technologies, vol. 8, no. 2, pp. 89–95, 2023 [5] L. Wang and T. Kumar, “Smart Home Automation for Fire Safety: IoT-Based Solutions,” International Conference on Smart Cities and IoT, pp. 210–216, 2024. [6] Twilio API for SMS and Voice Alerts – https://www.twilio.com/docs, 2025.Twilio API for SMS and Voice Alerts – https://www.twilio.com/docs, 2025.

Copyright

Copyright © 2025 Mr. Durga Nayak K, Prof. Sharvani V. 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.