153 lines
6.4 KiB
Markdown
153 lines
6.4 KiB
Markdown
# Msc-Summer-Project
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> My Msc Summer Project
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This repository contains my Masters-level summer project. The title is _LoRaWAN Signal Mapping_. The software contained here provides a complete system for mapping and visualising the signal coverage of [The Things Network](https://thethingsnetwork.org/).
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## Structure
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The system is split up into 2 primary parts:
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1. An Arduino program that collects the data.
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2. A Node.js program that stores and analyses the data
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It's best explained with the aid of a diagram or two:
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The above flowchart describes the workflow when using the system:
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1. First, the IoT device is turned on and the TTN Listener is launched.
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2. Then, the IoT device is taken around the target area that needs mapping. This can be done by anyone - the device does not require operation beyond turning it on and off once provisioned.
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3. Once the device has been carried around the target area, the `DATA.TSV` file on the IoT device's microSD card is copied off and placed on the server.
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4. The server data processor is then run to fold the data into the database.
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5. The AI trainer is now run on the collected data
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6. The data is displayed in a web browser, with the help of a web server
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The flow of data during use can be shown using a diagram:
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## System requirements
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This software has a number of requirements in order to function properly:
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- A Linux-based server (_Ubuntu Server LTS_ is recommended), with the following installed:
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+ Node.js v10+ with npm 6+ - see [this website](https://github.com/nodesource/distributions) for instructions on how to install a recent version of Node.js (for the various application server stuff)
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+ Git (for cloning the code repository)
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+ `awk` (for text processing by the build script)
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+ Bash v4+ (for the build script)
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+ PHP (for the temporary test web server; not otherwise used any static web server will do)
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- Arduino IDE (for programming the IoT device)
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- Git (for cloning the code repository)
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-
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## Getting Started
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### Step 0: Initial setup
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Before doing anything, clone this git repository to both the server (for the TTN listener etc.) and your local machine (for programming the IoT device):
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```bash
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git clone https://git.starbeamrainbowlabs.com/sbrl/Msc-Summer-Project.git
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```
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_If you have somehow obtained a static copy of the code (e.g. through the University's marking system), then skip the above and use that instead._
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Next, `cd` to the root of the repository and then run the setup task of the build script:
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```bash
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./build setup
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```
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On Windows, this should be run in _Git Bash_ (accessible from the start menu when _Git_ is installed - don't forget to `cd` to the root of the repository).
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### Step 1: Build a device
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First, a device must be built and provisioned. See `HARDWARE.md` in this repository for detailed instructions on how to do this.
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Once built, copy `iot/main/settings.custom.cpp.example` to `iot/main/settings.custom.cpp` and follow the instructions fill in the fields there. To do this, you'll need to register the device using ABP (Activation By Personalisation) on _The Things Network_. [This guide](https://www.thethingsnetwork.org/docs/devices/registration.html) tells you how to do this.
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It is suggested that the following Bash one-liner be used to generate a new encryption key in the right formats:
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```bash
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head -c16 /dev/urandom | od -tx1 | awk '{ gsub(/^0+\s+/, "", $0); toml=$0; gsub(/\s+/, "", toml); print("settings.toml format: " toml); arduino=toupper($0); gsub(/\s+/, ", 0x", arduino); print("settings.custom.cpp format: 0x" arduino); exit }'
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```
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_(Paste it into a terminal and hit enter)_
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Then, review `iot/main/settings.h` to make sure it matches your setup (e.g. all the pin numbers are correct)
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Next, copy the folders in `iot/libraries` to your Arduino IDE libraries folder.
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Finally, open `iot/main/main.ino` in the Arduino IDE and program the IoT device itself.
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### Step 2: The Things Network Setup
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_This step should be completed on the server._
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Once a device has been constructed, running the _The Things Network_ listener is next. This requires giving the system the _The Things Network_ credentials.
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Edit the file called `settings.toml` in the root of this repository (or create it if it doesn't exist), and make sure it contains the following:
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```toml
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[ttn]
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app_id = "{APPLICATION_ID}"
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access_key = "{TTN_ACCESS_ID}"
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encryption_key = "{ENCRYPTION_KEY_FROM_STEP_1}"
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port = 8883
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tls = true
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devices = [ "{DEVICE_NAME_FROM_TTN}" ]
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```
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The `app_id` and `access_key` can be obtained from _The Things Network Console_:
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The device name can be obtained from step #1, when you registered the device with _The Things Network_. Alternatively, if the device is already registered, it can be obtained from the device list if you click the "X registered devices" text.
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With `settings.toml` filled in, you can now start TTN Listener:
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```bash
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./build ttn-listener
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```
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....it will display an error message if you forgot a value.
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Now that the TTN listener is running, the IoT device can be carried around and data collected.
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### Step 3: Processing the data
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_This step should be completed on the server._
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Once data has been collected by the IoT device, it can then be processed by the Node.js server application.
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Copy the `DATA.TSV` file from the IoT device's microSD card to the root of the repository on the server. It is suggested that `scp` (Linux) or [WinSCP](https://winscp.net/) be used to do this.
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Next, fold `DATA.TSV` into the database like so:
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```bash
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./build process-data
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```
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Then, train the AIs on the collected data like this:
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```bash
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./build train-ai
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```
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The architecture of the neural networks trained can be customised by editing `settings.toml`. Check `server/settings.default.toml` for a guide on the different options available.
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### Step 4: Viewing the AI output
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With the AIs trained, the browser-based web interface can be used to display the output as a map. Any static web server will do, but the build script has one built-in (if PHP is installed) for convenience:
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```bash
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./build server
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```
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If an alternative server is to be used, it should serve the `app/` directory that can be found in the root of this repository.
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## Credits
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- AI: [TensorFlow.js](https://www.tensorflow.org/js/)
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- [Loading Animation](https://github.com/SamHerbert/SVG-Loaders)
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## Useful Links
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- Entropy extraction from the watchdog timer vs the internal clock: https://github.com/taoyuan/Entropy
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