Review of Labs

FastApi + MQTT

Tasks:

[English below]
Implement communication using the MQTT protocol, the Raspberry PI device should be both a client and a broker

• At least 3 topics and different QoS
• At least 2 subscriptions and 1 publication from paho-mqtt

Implement the REST API service using fast-api and communicate with the API from the level of the client application At least 3 services that download data and at least 2 that add/modify data - data stored in the file

SenseHat

Tasks:

[English below]
Build an application that allows you to use Raspberry PI sensors and actuators Sensors (at least 4): Accelerometer, Gyroscope, Magnetometer/compass, Thermometer, Humidity sensor, Barometer, Joystick Actuators: LED display

Docker Project

Tasks:

[English below]
4 containers (based on Dockerfile and docker-compose.yml):

• Container with the MQTT broker
• Container with FastAPI services, services also available from the host
• Container with an application that retrieves data from FastAPI after receiving a certain message (MQTT), the data is then published on another topic
• Container with an MQTT client subscribing to a topic that another client is posting about

AWS + Linear Regression

Tasks:

[English below]
Cloud:

• Defining the activity in the stream (pipeline):
• Determining the value of a new attribute based on others
• Filtering/add other attributes

Locally:

• Define an MQTT client that saves data to a csv file
• Write a script that generates a plot of values against time
• Determine the mean value, standard deviation
• Use at least one ML method

Useful commands:

 pip install uvicorn
 
 pip3 install fastapi
 
 /home/pi/.local/bin/uvicorn script:app --reload
 
 pip install paho-mqtt
 
 apt-get install mosquitto
 
 apt-get install mosquitto-client
 
 ps -ef | grep mosquitto