Smoke Detection and Alert System Using Arduino Uno Microcontroller and ThingSpeak IoT-Cloud

Okwori Anthony Okpe¹, Oladunjoye John Abiodun², Adogwu Samuel Junior³, Ekoro Ekoro Igo⁴

^1,2,3 Lecturer, Department of Computer Science, Federal University Wukari, Wukari, Nigeria.

⁴ Lecturer, Computer Science Education, University of Education and Entrepreneurship, Akamkpa, Cross-River State, Nigeria.

Abstract

The alarming rate of fire disasters in homes and industries is largely due to untimely awareness of fire outbreaks and slow response time for fire control as a result of a poor fire outbreak identification and reporting system. The Smoke Detector and Alert System using an Arduino Uno Microcontroller and ThingSpeak IoT-Cloud is a smart fire detection equipment that targets timely information and quick response to fire disasters within private homes and industrial centers. This system was developed using an Arduino Uno Microcontroller as the control hub with multiple sensors (DHT11, MQ135, and flame sensor) for data acquisition and actuators (LEDs, buzzer, relay) for alert notification. The software components comprise the Arduino IDE for programming and cloud integration via ThingSpeak, Mailjet, and Twilio, supported by a stable wifi network and API credentials for notification. The system utilized a multi-sensor approach that reduced false positives, offered scalability, and maintained energy efficiency with a peak consumption of 280 MA at 5v. Performance evaluation demonstrated a 98% data upload success rate to ThingSpeak over a 24-hour testing period, with email alerts delivered in 10–12 seconds and SMS notifications in 15 seconds. The multi-sensor fusion approach achieved detection accuracy comparable to existing systems, with the flame sensor reliably detecting infrared emissions at 0.8 meters and the MQ135 sensor tracking smoke concentrations within ±10 ppm after calibration. The system maintained stable operation across 50 test cycles, with the DHT11 maintaining ±2°C accuracy and the WiFi reconnection logic recovering from disruptions within two attempts.

Keywords: Internet of Things (IoT); Fire detection; Arduino Uno; ThingSpeak; Smoke sensor; Real-time monitoring; Multi-sensor fusion; Cloud computing

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