Smart Sensor Network

A distributed sensor network for environmental monitoring using ESP32 microcontrollers, MQTT communication, and custom PCB design.

Introduction

This project explores the creation of a distributed sensor network capable of monitoring environmental conditions across multiple locations. The system uses ESP32 microcontrollers as sensor nodes, communicating wirelessly through MQTT protocol to a central data collection hub.

The motivation for this project came from the need to monitor temperature, humidity, and air quality in different areas of a workspace, providing real-time data visualization and historical tracking capabilities.

Key features include:

• Wireless sensor nodes with battery backup
• Real-time data transmission via MQTT
• Web-based dashboard for data visualization
• Custom PCB design for compact sensor nodes
• 3D printed enclosures for weather protection

Research

Before starting the project, I conducted extensive research on wireless sensor networks and IoT communication protocols:

Communication Protocols

I evaluated several wireless communication options:

• WiFi: High bandwidth, good range, but higher power consumption
• LoRa: Low power, long range, but limited bandwidth
• Bluetooth: Low power, short range, good for local networks
• Zigbee: Mesh networking, low power, moderate range

Sensor Selection

For environmental monitoring, I researched various sensor options:

• DHT22: Temperature and humidity, good accuracy, low cost
• BME280: Temperature, humidity, and pressure, I2C interface
• MQ-135: Air quality sensor for detecting various gases
• PIR Sensor: Motion detection for occupancy monitoring

Power Management

Battery life was a critical consideration. I researched:

• Sleep modes and wake-up strategies
• Power consumption optimization techniques
• Solar charging solutions for outdoor deployment
• Battery monitoring and low-power alerts

Making Process

The development process was divided into several phases, each building upon the previous work:

Phase 1: Prototype Development

Started with breadboard prototypes to test individual components and communication protocols. This phase involved:

• Setting up ESP32 development environment
• Testing sensor readings and calibration
• Implementing basic MQTT communication
• Creating simple web dashboard for data display

Phase 2: PCB Design

Once the prototype was working, I designed custom PCBs for the sensor nodes:

• Used KiCad for schematic and PCB layout
• Designed for ESP32-S3 module with minimal external components
• Included power management circuit with battery charging
• Added connectors for various sensor modules

Phase 3: Enclosure Design

Created 3D printed enclosures for weather protection:

• Designed in Fusion 360 with parametric modeling
• Included ventilation for sensor accuracy
• Added mounting points for easy installation
• Used weather-resistant materials (PETG filament)

Phase 4: Software Development

Developed the complete software stack:

• Arduino firmware for sensor nodes
• MQTT broker setup and configuration
• Web dashboard using HTML, CSS, and JavaScript
• Database integration for data storage

Final Outcome

The completed Smart Sensor Network successfully demonstrates a working distributed monitoring system with the following achievements:

Performance Results

• Battery Life: 7-10 days continuous operation on single charge
• Range: 50-100 meters indoor, 20-30 meters outdoor
• Data Accuracy: ±0.5°C temperature, ±3% humidity
• Update Frequency: 30-second intervals during active monitoring

Key Features Delivered

• Real-time environmental monitoring dashboard
• Historical data visualization with charts and graphs
• Mobile-responsive web interface
• Alert system for threshold violations
• Data export functionality for analysis

Lessons Learned

This project taught me valuable lessons about IoT development:

• Power Management: Sleep modes are crucial for battery life
• Network Reliability: MQTT QoS levels help ensure data delivery
• Sensor Calibration: Environmental factors significantly affect readings
• Enclosure Design: Proper ventilation is essential for accurate measurements

Future Improvements

Several enhancements could be implemented in future iterations:

• Solar panel integration for indefinite operation
• Machine learning algorithms for predictive analysis
• Integration with home automation systems
• Additional sensor types (light, sound, vibration)
• Mesh networking for extended range