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forecastnet's Introduction

ML-enhanced weather station network

This project is a IoT weather station network that displays past data and forecasts.

Overview

  • A station in the network consists of an ESP32 and a BMP280 sensor.
  • The Raspberry Pi works as an always-on server.
  • MQTT is used for communication between the stations and the server.
  • LAMP stack (Linux/ Apache/ MySQL/ PHP) and Python manages and stores data in a MySQL database and builds web interface for displaying data.
  • Prediction is performed using tinyML.

Preparation

Parts required

  • A Raspberry Pi board.
  • An ESP32 and a BMP280 sensor for each station.

Required libraries

For ESP32:

  • esp32 by Espressif Systems
  • Adafruit Unified Sensor
  • Adafruit BMP280
  • NPTClient by Taranais
  • PubSubClient by Nick O'Leary
  • ESPAsyncWebServer
  • AsyncTCP

For Raspberry Pi Raspbian OS:

  • apache2
  • php
  • python3
  • mariadb-server
  • php-mysql
  • phpmyadmin
  • mosquitto
  • mosquitto-clients

How to run this project

On ESP32

  • Setup the board following this schema
  • Install all required libraries
  • Load the source code at ./esp32/esp32_sketch/esp32_sketch.ino to Arduino IDE
  • Change WiFi and MQTT credentials
  • Upload the modified code to ESP32

On Raspberry Pi

  • Install all required libraries
  • Setup mosquitto MQTT broker and MySQL database following this tutorial
  • EXTRA: Check if the broker port 1883 on the server is open by running nmap -p 1883 <broker address> from a computer in the local network. Open the port if closed.
  • (Recommended) Create and use a Python virtual environment
  • Run pip install -r ./raspberrypi/requirements.txt to install necessary Python libraries
  • Modify MQTT and MySQL credentials and run python ./raspberrypi/forecastnet_server.py to start the server for uploading sensor data.
  • Data sent from ESP32 can now be browsed by accessing <your server address>/myphpadmin from a browser on any computer in the local network

... To be updated

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