IoT-Based Indoor Environment Monitoring Device

Indoor Environment Monitoring System

This project implements a smart IoT-based system designed to monitor and improve indoor environmental conditions. It uses low-cost hardware connected to Azure cloud services to deliver real-time monitoring, automated environmental control, anomaly detection, and temperature forecasting.

Project Overview

• Monitors temperature, humidity, CO, LPG, and smoke in real time.
• Automatically controls a humidity diffuser based on sensor readings.
• Detects abnormal gas levels and issues alerts to the user.
• Generates temperature predictions for the next 24 hours using historical data.
• Integrates hardware, cloud analytics, and dashboard visualizations into a single end-to-end solution.

Hardware Components

• ESP32 microcontroller for data processing and communication
• DHT22 temperature and humidity sensor
• MQ2 gas sensor calibrated for CO, LPG, and smoke detection
• Relay module and diffuser for automated humidity control
• Fully calibrated MQ2 sensor to ensure accurate clean-air reference values

Cloud Architecture

• Azure IoT Hub receives data from the ESP32 device.

• Azure Stream Analytics processes and routes the telemetry to multiple destinations:

  • Power BI for real-time dashboards
  • Cosmos DB for historical data storage
  • Blob Storage for reference statistics
  • Service Bus Queue for automated alert workflows
• Power Automate listens to the queue and sends notifications to the user when high gas levels are detected.

Figure 1: High-level architecture of the IEM Device.

Key Features

Real-Time Monitoring

• Live sensor data for gas, temperature, and humidity.
• Power BI dashboard displays up-to-date readings and alerts.
• Alerts appear directly on the dashboard for quick interpretation.

Figure 2: Detected Statistical Outliers.

Automated Humidity Control

• ESP32 calculates hourly average humidity.
• If the average drops below 40%, the relay activates the diffuser automatically.
• The diffuser can also be turned off manually through the dashboard.

Gas Leak Detection

• Uses a statistical method based on mean and standard deviation to identify anomalies.
• Detects sudden spikes in CO, LPG, or smoke readings.
• Sends an automated alert through Power Automate and Telegram.

Predictive Analytics

• Azure Function App runs a regression model using historical temperature data stored in Cosmos DB.
• Predicts temperature trends for the next 24 hours.
• Predicted values are stored in Blob Storage and displayed in Power BI.
• The combined view of historical and predicted data helps users anticipate changes in indoor conditions.

Outcome

This project results in a complete IoT-to-cloud pipeline that combines real-time sensing, automated decision-making, historical analysis, and predictive intelligence. Improved real-time safety monitoring in indoor environments through cloud-connected automation.

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