LoRa Air Quality Monitor

Professional-grade IoT platform for environmental monitoring using LoRaWAN, MQTT, InfluxDB and Grafana.

           

LoRa Air Quality Monitor is a professional-grade, end-to-end IoT solution designed for environmental monitoring in remote areas. By leveraging the long-range capabilities of LoRa technology, the efficiency of MQTT, and the power of time-series data with InfluxDB, this project provides a robust framework for tracking temperature, humidity, and atmospheric pressure across vast distances.

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🚀 Overview

The system captures environmental data from remote end-devices (nodes) using LoRa modulation. These data packets are received by a central Gateway, which bridges the LoRa network to an IP network by publishing messages to a Mosquitto MQTT Broker. A dedicated subscriber service ingests these messages and persists them into InfluxDB for real-time analysis and visualization via Grafana.

Key Features

• Long Range Communication: Utilizes LoRa for low-power, wide-area coverage.
• Scalable Architecture: Easily add more sensors or gateways.
• Time-Series Optimized: Efficient storage and querying of sensor data.
• Containerized Stack: Quick deployment using Docker and Docker Compose.
• Real-time Monitoring: Dashboard-ready data flow.

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🏗️ System Architecture

┌─────────────────┐       ┌─────────────────┐       ┌───────────────────┐
│  LoRa End Node  │       │  LoRa Gateway   │       │  MQTT Broker      │
│  (ESP32+SX1276) ├──────▶│ (Raspberry Pi/  ├──────▶│  (Mosquitto)      │
│  [Temp/Hum/Pres]│ LoRa  │  ESP32 Bridge)  │ MQTT  │                   │
└─────────────────┘       └─────────────────┘       └─────────┬─────────┘
                                                              │
                                                              │ Subscribe
                                                              ▼
┌─────────────────┐       ┌─────────────────┐       ┌───────────────────┐
│    Grafana      │       │    InfluxDB     │       │  Python Ingestor  │
│  (Dashboards)   │◀──────┤  (Time-Series)  │◀──────┤   (Subscriber)    │
└─────────────────┘       └─────────────────┘       └───────────────────┘

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🛠️ Hardware Requirements

Component	Description	Example
End Device	Microcontroller with LoRa radio	ESP32 + SX1276 (Heltec WiFi LoRa 32 / TTGO LoRa32)
Sensors	Environmental sensors	BME280 (Temp, Humidity, Pressure)
Gateway	Bridge between LoRa and Internet	Raspberry Pi with LoRa HAT or ESP32 LoRa Gateway
Power	Battery or Solar for remote nodes	18650 Li-ion batteries + Solar Panel

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💻 Software Stack

• Mosquitto: Lightweight MQTT message broker.
• InfluxDB 2.7: High-performance time-series database.
• Python 3.x: Service logic for data ingestion.
• Docker: Containerization and orchestration.
• Grafana: (Optional) Visualization platform.

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📂 Project Structure

.
├── config/
│   └── mosquitto.conf      # MQTT Broker configuration
├── docker/
│   └── docker-compose.yml  # Infrastructure orchestration
├── docs/
│   └── architecture.png    # High-level diagram
├── gateway/
│   └── subscriber.py       # MQTT-to-InfluxDB bridge script
└── README.md

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🔧 Installation & Configuration

Prerequisites

• Docker and Docker Compose installed.
• Python 3.10+ (for the subscriber script).
• Basic knowledge of LoRa and MQTT.

Step 1: Spin up the Infrastructure

Navigate to the docker/ directory and start the services:

cd docker
docker-compose up -d

This will launch Mosquitto, InfluxDB, and Grafana.

Step 2: Configure InfluxDB

By default, the docker-compose.yml initializes InfluxDB with the following credentials:

• Org: my-org
• Bucket: lora_data
• Token: my-super-secret-auth-token

Step 3: Install Python Dependencies

For the ingestion script, install the required libraries:

pip install paho-mqtt influxdb-client

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MQTT Topic Structure & Payload

Topic Pattern

The gateway should publish data to the following topic: lora/devices/{device_id}/data

Example Payload (JSON)

{
  "temperature": 24.5,
  "humidity": 55.2,
  "pressure": 1013.2,
  "location": "field-alpha-01"
}

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InfluxDB Data Schema

The subscriber.py script maps MQTT messages to InfluxDB as follows:

• Bucket: lora_data
• Measurement: sensor_data
• Tags:
  • device_id: The ID extracted from the MQTT topic.
  • location: The location field from the JSON payload.
• Fields:
  • temperature (float)
  • humidity (float)
  • pressure (float)

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Ingestion Script (Subscriber)

The gateway/subscriber.py is the bridge between MQTT and InfluxDB.

Brief Explanation:

1. It connects to the Mosquitto broker and subscribes to lora/devices/+/data.
2. When a message arrives, it parses the JSON payload.
3. It creates an InfluxDB Point and writes it synchronously to the database.

To run the subscriber:

python gateway/subscriber.py

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📈 Usage & Visualization

Querying Data (Flux)

You can verify data in the InfluxDB UI (localhost:8086) or via CLI using Flux:

from(bucket: "lora_data")
  |> range(start: -1h)
  |> filter(fn: (r) => r["_measurement"] == "sensor_data")
  |> filter(fn: (r) => r["_field"] == "temperature")

Grafana Integration

1. Login to Grafana at http://localhost:3000 (Default: admin/admin).
2. Add a Data Source: Select InfluxDB.
3. Set Query Language to Flux.
4. URL: http://influxdb:8086.
5. Enter Org, Token, and Bucket details from Step 2.
6. Create a dashboard and add a Time Series panel.

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❓ Troubleshooting

• Connection Refused (MQTT): Ensure the mosquitto.conf allows connections and the container is running.
• LoRa Packet Loss: Check the distance between nodes and the gateway, or adjust Spreading Factor (SF).
• InfluxDB Authentication: Verify that the token in subscriber.py matches the one in docker-compose.yml.
• JSON Parsing Error: Ensure the LoRa Gateway is sending a valid, uncorrupted JSON string.

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🤝 Contributing

Contributions make the IoT community a better place!

1. Fork the Project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

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📜 License

Distributed under the MIT License. See LICENSE for more information.

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Developed with ❤️ for the IoT community.