# Imperial College London
# Sensing and IoT Coursework Project
#### Reducing water waste by automatically dispensing lower bound water amounts, calculated using a ML Model using present weather data

## About
This project aims to integrate hardware IoT sensors (some RPi Zero's with I2C probes) and Machine Learning (Deep-NN) to predict how much water a outdoor garden will need every day.

The ML Model uses the last 24hrs of collected weather data to make a prediction around mow much water needs to be dispensed to keep the plants alive.

This approach hopes to reduce the overall water use by not watering plants in excess.

## Project diagram
```TODO```

## File Structure

<details>
<summary>This part is long, click to expand</summary>

- `data_collector/` Main data collector and uploader module
  - `secrets/` Private API keys
    - `icl-iot-weather-firebase-adminsdk.json` Firebase key for database access
    - `weather_api_key.txt` OpenWeather API key
  - `data_colelctor.py` Python script for data collection
  - `Dockerfile` Containerizing the application
  - `requirements.txt` Python requirements for running the script
- `data_processor/` ML on demand data processing module
  - `firebase_python_image/` Since building a tf image takes ages, we build it once and use it for testing the ml script later
    - `Dockerfile` Building a tensorflow image for armv7l
  - `secrets/` Private API keys
    - `icl-iot-weather-firebase-adminsdk.json` Firebase key for database access
  - `watering_model.model/` Saved ML modelfor water predicitons
  - `Dockerfile` Containerizing the application
  - `water_predictor.py` On demand, real time watering predictor script
- `Diagrams/` Process and block diagrams
  - `block_and_process_diagrams.drawio` TODO
- `lora_nodes/` Scripts running on the Pi Zero nodes
  - `master/` Scripts running on the primary, internet connected Pi
    - `irrigator/` Daily watering module
      - `Dockerfile` Containerizing the application
      - `irrigator.py` Daily watering script
      - `requirements.txt` Python requirements for running the script
    - `Dockerfile` Containerizing the application
    - `main.py` Main data server, collects data from satellite over LoRa and returns over http
    - `requirements.txt` Python requirements for running the script
  - `satellite/` Data reading and sending module for the sensor Pi
    - `Dockerfile` Containerizing the application
    - `main.py` LoRa commend listener, temperature reader and pump controller
    - `requirements.txt` Python requirements for running the script
- `manual_data_processing/` iPython notebooks used for data processing and model training
  - `datasets/*` Various datasets used for processing and training
  - TODO
- `site/*` Monitoring website, HTML+CSS+JS, hosted on Firebase
- `.gitignore` Gitignore file preventing all my API keys from showing up online...
- `README.md` See `README.md`

</details>

## Installation and running
1. Clone this repo
2. Create the secrets folders
3. Generate a set of firebase and OpenWeather API credentials and place them in the secrets folders, rename accordingly
4. Move the `master` folder to the main Pi
5. Move the `satellite` folder to the sensor Pi
6. Install Docker on all Devices
7. Build the docker images on the relevant architectures (for me everythong was build on armv7)
   - `data_colelctor`: `docker build -t siot-weather-collector .`
   - `data_processor`: `docker build -t siot-data-processor .`
   - `master/irrigator`: `docker build -t master-node .`
   - `master`: `docker build -t irrigator .`
   - `satellite`: `docker build -t satellite .`
8. *NOTE: The ML Dockerfile is very long and complicated because tensorflow does not play well with a 32 bit arm architeture, if you are building for x86 or arm64, you may need to change the file*
9.  Run the docker containers:
   - The `data_collector` on the cloud device: `docker run -d --restart always --name siot_weather_collector siot-weather-collector`
   - The `data_processor` on the cloud device: `docker run -dp 3535:3535 --restart always --name siot_watering_predictor  siot-data-processor`
   - The `master` on the master Pi: `docker run -dp 3333:3333 --privileged --restart always master-node`
   - The `master/irrigator` on the master Pi: `docker run -d --privileged --restart always irrigator`
   - The `satellite` on the sensor Pi: `docker run -d --privileged --restart always satellite`
10. While you are free to use my ML model included in this repo, I suggest you explore the `manual_data_processing` folder and create your own.
11. You will also need at least 24hrs of data before the model can make predictions, so the `siot-weather-collector` image must be started at least 24hrs before the others

## Maintainers and Contributors

   Max Hunt: [me@maxhunt.design](mailto:me@maxhunt.design)