training-labs

Lab 5: Sending and Saving Real Data

The goal of this lab is to read real data with LoRaWAN device and send it to TTN, while also retrieving your data from TTN using MQTT. You will accomplish the stable display and storage of actual measurement data on the TTN platform. The team project’s ultimate objective is to attain a comparable outcome.

Objective

By completing the fifth training lab, you will be able to:

• read real data with your LoRaWAN device and send it to The Things Network
• retrieve your data from The Things Network using MQTT
• save your data for later processing

Prelab

Please do the following before the Friday lab section:

• Set up an API Key for TTN V3
• Install Python and Python libraries

Set up an API Key for TTN V3

App: Integrations: MQTT: Generate Key. Copy it to a safe place now for further use. TTN cannot share the key you generated after you exit that page, so if you lose it, you have to make another key.

Installing Python

Please install a recent version of Python. You can find Python packages in the “Files” section of the Python 3.11.2 Page. Be sure to check the box to install pip as well. On Windows, please also check the box to install the py launcher and to add Python to PATH. If you have taken CS 1110, that setup should work.

Python Basics

Python is run from a command line in our case. On MacOS, search for the “Terminal” application, and on Windows, search for “PowerShell.” Throughout this lab, certain symbols placed before commands will notate MacOS Terminal ($), Windows PowerShell (PS >), and Python interactive-mode (>>>)command lines. If you copy commands, do not copy the leading symbols ($, PS >, >>>, etc.).

On MacOS, use

$ python3

to start an interactive Python session.

From PowerShell, use

PS > py

to start an interactive Python session.

In Python, you can use the quit() function to quit.

>>> quit()

On MacOS, Control-D is the interactive-mode quit keyboard shortcut, and on Windows, Control-Z then enter quits.

You can also put Python statements into a file. Python essentially reads and executes your file line by line. You can instruct Python to run a file myPythonCode.py with the following command.

$ python3 myPythonCode.py

PS > python myPythonCode.py

The above command assumes that you are in the same directory (folder) as your Python file. If you are not, you can use the cd command to change directory. For example, if your Python file was in Documents/sp23/MAE4220, you could use

cd Documents/sp23/MAE4220

to go to that directory. Spaces and special characters are sometimes challenging to use on the command line, but using tab to auto-complete can help. For example, if I have a folder called Test Folder, if I hit tab when I have typed

cd Tes

MacOS will autocomplete it to

cd Test\ Folder/

and Windows will autocomplete it to

cd '.\Test Folder\'

Installing Paho

Python has a package manager called pip. Many packages are available in the PyPi repository, but we will just use one this week.

pip3 install paho-mqtt==1.6.1

In some cases, pip3 or pip doesn’t appear as a command, so you can try commands like

python3 -m pip install paho-mqtt==1.6.1

or

python -m pip install paho-mqtt==1.6.1

or

py -m pip install paho-mqtt==1.6.1

Set up your Feather to send real data

In lab 2, you made a small set of libraries to read temperature data. In lab 3, you practiced loading different kinds of values into packets and decoding. In this lab, you will merge the two. Please set up your Feather and decoder to send and decode thermistor and digital sensor temperature measurements.

To read your temperature sensors, use your library code. You should copy your TemperatureSensor.h and TemperatureSensor.cpp files from your Lab 2 sketch folder to your Lab 5 sketch folder. Review your Lab 2 sketch as necessary to see what #include files and class instantiations (like PANE103395_VD thermistor;) are necessary to use your library.

Change struct pkt_fmt to include a few floats to hold temperature data. Update your packet with a reading of (float) thermistor.getTemperature() before calling sendBuffer. There is not a great need to send a double (twice as much data) over LoRaWAN because neither sensor is very precise.

Your lab 4 does not have to work in order for you to save data and meet the lab 5 objective. You simply have to be able to send and decode data.

Watching Your Messages in Python

You can modify a simple MQTT script in Python using a module such as Paho MQTT to subscribe to the updates that your app creates. This is especially useful if you want to do further processing on your data. The tool can give json, which is easy to work with on Python on Linux, macOS, and Windows. There is an example given below.

Example Paho Script

Here’s an example script in Python that receives data via MQTT and saves the data to a file. You may download it here.

#!/usr/bin/env python3

# from https://stackoverflow.com/questions/54292179/saving-mqtt-data-from-subscribe-topic-on-a-text-file

import paho.mqtt.client as mqttClient
import time

def on_connect(client, userdata, flags, rc):


    if rc == 0:

        print("Connected to broker")

        global Connected                #Use global variable
        Connected = True                #Signal connection

    else:
      
        print("Connection failed")
        
def on_message(client, userdata, message):
    print("")
    print("Message received: "  + str(message.payload))

    with open('myData.txt','a+') as f: # You can change the data file name here
         f.write(str(message.payload)[2:-1]+"\n")

Connected = False   #global variable for the state of the connection

broker_address= "nam1.cloud.thethings.network"  #host
port = 1883                         #Broker port
user = "yourAppHere@ttn" #<--  Put your TTN V3 app here                    #Connection username
password = "" #<--  Put your TTN V3 API key in quotes           #Connection password

client = mqttClient.Client("Python")               #create new instance
client.username_pw_set(user, password=password)    #set username and password
client.on_connect= on_connect                      #attach function to callback
client.on_message= on_message                      #attach function to callback
client.connect(broker_address,port,60) #connect
client.subscribe(f"v3/{user}/devices/+/up") #subscribe
client.loop_forever() #then keep listening forever

Fill in your app name and MQTT API key. When you run the Python script, Python will subscribe to your app’s MQTT feed and append messages to myData.txt. You may change myData.txt to another file name. You will make a plot of such data in Lab 6.

Lab 5 deliverable

Please let paho run for a while and submit your data file as a .txt file to Canvas as a deliverable.