---
title: "Cursor IoT Edge Computing"
description: "Configures Cursor for IoT edge computing with MQTT, sensor processing, and cloud connectivity."
category: "configuration"
tags: ["cursor", "iot", "edge-computing", "mqtt", "sensors", "cloud-connectivity", "device-management", "real-time-monitoring"]
tech_stack: ["python", "mqtt", "raspberry-pi", "arduino", "aws-iot", "azure-iot", "edge-analytics"]
---

This configuration creates an IoT edge computing setup that uses MQTT for communication. It allows for sensor data processing, cloud synchronization, and real-time monitoring.

### Configuration Overview
This setup makes it easy for IoT devices to communicate with cloud services. It helps gather, process, and analyze data right at the edge.

### Prerequisites
To get started, you will need:
- **Hardware**: A Raspberry Pi or Arduino
- **Software**: 
  - Python 3.x
  - Paho MQTT library (`paho-mqtt`)
  - AWS IoT SDK for Python
  - Azure IoT SDK for Python
- **Network**: A stable internet connection to connect to the cloud.

### Installation & Setup
1. **Install Python and Paho MQTT**:
   ```bash
   sudo apt update
   sudo apt install python3 python3-pip
   pip3 install paho-mqtt
   ```
2. **Set up AWS IoT**:
   - Create an AWS account and go to the IoT Core service.
   - Create a new IoT Thing and download your security credentials, which include a certificate and a private key.
3. **Set up Azure IoT**:
   - Create an Azure account and navigate to the IoT Hub.
   - Register a new device and get the connection string.
4. **Clone the repository**:
   ```bash
   git clone https://github.com/your-repo/iot-edge-computing.git
   cd iot-edge-computing
   ```
5. **Configure environment variables**:
   Create a `.env` file in the project root with this content:
   ```env
   AWS_IOT_ENDPOINT=your_aws_iot_endpoint
   AWS_CERT_PATH=path_to_your_certificate
   AWS_KEY_PATH=path_to_your_private_key
   AZURE_IOT_CONNECTION_STRING=your_azure_iot_connection_string
   ```

### File Structure
```
iot-edge-computing/
├── src/
│   ├── main.py
│   ├── mqtt_client.py
│   ├── sensor_data_processor.py
│   └── cloud_sync.py
├── config/
│   └── .env
├── requirements.txt
└── README.md
```

### Key Configuration Files
- **`main.py`**: This is the main entry point for the application.
  ```python
  import os
  from mqtt_client import MQTTClient
  from sensor_data_processor import process_sensor_data

  def main():
      mqtt_client = MQTTClient()
      mqtt_client.connect()
      while True:
          data = read_sensor_data()
          process_sensor_data(data)
          mqtt_client.publish(data)

  if __name__ == "__main__":
      main()
  ```
- **`mqtt_client.py`**: This file manages MQTT communication.
  ```python
  import paho.mqtt.client as mqtt
  import os

  class MQTTClient:
      def __init__(self):
          self.client = mqtt.Client()
          self.client.tls_set()
          self.client.username_pw_set(os.getenv('AWS_IOT_USERNAME'), os.getenv('AWS_IOT_PASSWORD'))

      def connect(self):
          self.client.connect(os.getenv('AWS_IOT_ENDPOINT'))

      def publish(self, data):
          self.client.publish('topic/sensor', data)
  ```
- **`sensor_data_processor.py`**: This file handles incoming sensor data.
  ```python
  def process_sensor_data(data):
      # Implement data processing logic
      print("Processing data:", data)
  ```

### Advanced Options
- **MQTT QoS Levels**: You can change the Quality of Service level in `mqtt_client.py` to ensure reliable message delivery.
- **Data Caching**: Consider implementing local caching of sensor data to reduce cloud requests during connectivity issues.

### Troubleshooting
- **Connection Issues**: Make sure your device is connected to the internet and that your AWS/Azure credentials are correct.
- **MQTT Subscription Failures**: Double-check your topic names and ensure your device has permission to publish or subscribe to those topics.
- **Sensor Data Not Processing**: Check that your sensor is wired correctly and that the data reading function is set up properly.

### Best Practices
- **Use Environment Variables**: Keep sensitive information like API keys in environment variables rather than hardcoding them.
- **Implement Logging**: Utilize Python's `logging` module to record important events and errors, which makes debugging easier.
- **Regularly Update Dependencies**: Keeping your libraries up to date helps you benefit from security patches and new features.

### Performance Optimizations
- **Batch Data Processing**: Instead of processing data one at a time, try accumulating data and processing it in batches to minimize overhead.
- **Optimize MQTT Settings**: Adjust the MQTT keep-alive interval and message size to enhance performance based on your application needs.