My LoRaWAN Signal Mapping MSc summer project. This is a copy of the actual repository with personal information removed.
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# Default settings file.
#
# DO NOT EDIT THIS FILE. Instead edit ../settings.toml (or create it if it doesn't exist yet).
program_name = "LoRaWAN Signal Mapper"
version = "v0.1"
description = "assists in mapping LoRaWAN signal coverage"
[database]
### Database settings ###
# The path to the sqlite database file. If it doesn't exist it will be created.
filename = "lorawan.sqlite"
# The options to pass to better-sqlite3. You probably don't need to change this.
[database.options]
[ttn]
### The Things Network settings ###
# The host to connect to via MQTT.
# See https://www.thethingsnetwork.org/docs/applications/mqtt/api.html
# and also "Application Overview -> Handler"
host = "eu.thethings.network"
# The port number to connect on.
# The Things Network uses 1883 for plain-text, and 8883 for TLS.
port = 8883
# Whether to use TLS or not.
tls = true
# The id of The Things Network application to connect with.
# Basically your application's name. Get this from the things network
# console - e.g. "lorawan-signal-mapping".
app_id = "CHANGE_THIS"
# The access key to connect to The Things Network with.
# Get this from the TTN console too. Click on your application, scroll to the
# "access keys" section at the bottom of the page, and copy the value you see
# there.
access_key = "CHANGE_THIS"
# The additional encryption key, in hex, generated when setting up the IoT device.
# This is used as an exrtra layer of encryption to ensure that The Things Network does not have access to the decrypted data.
encryption_key = "CHANGE_THIS"
# A list of devices to monitor.
# If a device isn't specified here, then we won't hear messages from it.
# FUTURE: Automatically fetch a list of devices from the TTN API
devices = [
"CHANGE_THIS"
]
[ai]
# Settings relating to the training of the AI. Note that a number of these settings can also be specified by environment variables, to aid with fiddling with the parameters to find the right settings.
# Min / max dataset values when training the AI, since neural networks only take values between 0 and 1.
# Note that changing these means that you've got to retrain the AIs all over again!
rssi_min = -150
rssi_max = 0
# Data is streamed from the SQLite databse. The batch size specifies how many
# rows to gather for training at once.
# Note that the entire available dataset will eventually end up being used -
# this setting just controls how much of ti is in memory at once.
batch_size = 32
# The number of epochs to train for.
epochs = 5
# The percentage, between 0 and 1, of data that should be set aside for validation.
validation_split = 0.1
# The directory to output trained AIs to, relative to the repository root.
output_directory = "app/ais/"
[logging]
# The format the date displayed when logging things should take.
# Allowed values: relative (e.g like when a Linux machine boots), absolute (e.g. like Nginx server logs), none (omits it entirely))
date_display_mode = "relative"
# Whether we should be verbose and log a bunch of stuff to the console.
# Disabled by default, but useful for debugging.
verbose = false
# Whether we should output ANSI escape sequences to colourise the output or not.
# Defaults to true, but you should turn it off if you're using syslog.
colour = true