Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol.

Overview

Updated

Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol.

Introduction

This balenaCloud (previously resin.io) setup is based on the Multi-protocol Packet Forwarder by Jac Kersing.

An alternative guide to use this balenaCloud setup can be found in the official TTN documentation at: https://www.thethingsnetwork.org/docs/gateways/rak831/

Difference between Poly-packet-forwarder and Multi-protocol-packet-forwarder

mp-pkt-fwd uses the new protocolbuffers-over-mqtt-over-tcp protocol for gateways, as defined by TTN and used by the TTN kickstarter gateway. Using this protcol the gateway is authenticated, which means it is registered under a specific user and can thus be trusted. Because it uses TCP, the chance of packet loss is much lower than with the previous protocol that used UDP. Protocolbuffers packs the data in a compact binary mode into packets, using much less space than the plaintext json that was previously used. It should therefore consume less bandwidth.

balenaCloud TTN Gateway Connector for Raspberry Pi

balenaCloud Dockerfile & scripts for The Things Network gateways based on the Raspberry Pi. This updated version uses the gateway connector protocol, not the old packet forwarder. See the TTN documentation on Gateway Registration, you need to create a gateway API key.

Currently any Raspberry Pi with one of the following gateway boards, communicating over SPI, are supported, but not limited to these:

Prerequisites

  1. Build your hardware.
  2. Create a new gateway that uses gateway connector on the TTN Console. Also set the location and altitude of your gateway. Go to API keys and create a new API key with 'link as Gateway to a Gateway Server for traffic exchange, i.e. write uplink and read downlink' rights. Copy the secret and use it for GW_KEY later on.
  3. Create and sign into an account at https://www.balena.io/cloud/, which is the central "device dashboard".

Create a balenaCloud application

  1. On balenaCloud, create an "Application" for managing your TTN gateway devices. I'd suggest that you give it the name "ttngw", select the appropriate device type (i.e. Raspberry Pi 2 or Raspberry Pi 3), and click "Create New Application". You only need to do this once, after which you'll be able to manage one or many gateways of that type.
  2. You'll then be brought to the Device Management dashboard for that Application. Follow the instructions to "Add device" and create a boot SD-card for your Raspberry Pi. (Pro Tip: Use a fast microSD card and a USB 3 adapter if you can, because it can take a while to copy all that data. Either that, or be prepared to be very patient.)
  3. When the (long) process of writing the image to the SD card completes, insert it into your Raspberry Pi, connect it to the network with Ethernet, and power it up.
  4. After several minutes, on the balenaCloud Devices dashboard you'll now see your device - first in a "Configuring" state, then "Idle". Click it to open the Devices control panel.
  5. If you like, enter any new Device Name that you'd like, such as "my-gateway-amsterdam".

Configure the gateway device

Click the "Environment Variables" section at the left side of the screen. This will allow you to configure this and only this device. These variables will be used to pull information about this gateway from TTN, and will be used to create a "global_conf.json" and "local_conf.json" file for this gateway.

For a more complete list of possible environment variables, see CONFIGURATION.

Device environment variables - no GPS

For example, for an IMST iC880A or RAK831 with no GPS, the MINIMUM environment variables that you should configure at this screen should look something like this:

Name Value
GW_TTSCE_CLUSTER The TTS(CE) cluster being used: eu1, nam1, au1
GW_ID The gateway ID from the TTN console
GW_KEY The gateway API KEY secret key you copied earlier
GW_RESET_PIN 22 (optional)

GW_RESET_PIN can be left out if you are using Gonzalo Casas' backplane board, or any other setup using pin 22 as reset pin. This is because pin 22 is the default reset pin used by this balenaCloud setup.

Device environment variables - with GPS

For example a LinkLabs gateway, which has a built-in GPS, you need:

Name Value
GW_TTSCE_CLUSTER The TTS(CE) cluster being used: eu1, nam1, au1
GW_ID The gateway ID from the TTN console
GW_KEY The gateway API KEY secret key you copied earlier
GW_GPS true
GW_RESET_PIN 29

Reset pin values

Depending on the way you connect the concentrator board to the Raspberry Pi, the reset pin of the concentrator might be on a different GPIO pin of the Raspberry Pi. Here follows a table of the most common backplane boards used, and the reset pin number you should use in the GW_RESET_PIN environment variable.

Note that the reset pin you should define is the physical pin number on the Raspberry Pi. To translate between different numbering schemes you can use pinout.xyz.

Backplane Reset pin
Gonzalo Casas backplane
https://github.com/gonzalocasas/ic880a-backplane
https://www.tindie.com/stores/gnz/
22
ch2i
https://github.com/ch2i/iC880A-Raspberry-PI
11
Linklabs Rasberry Pi Hat
https://www.amazon.co.uk/868-MHz-LoRaWAN-RPi-Shield/dp/B01G7G54O2
29
Rising HF Board
http://www.risinghf.com/product/risinghf-iot-dicovery/?lang=en
26
IMST backplane or Lite gateway
https://wireless-solutions.de/products/long-range-radio/lora_lite_gateway.html
29 (untested)
Coredump backplane
https://github.com/dbrgn/ic880a-backplane/
https://shop.coredump.ch/product/ic880a-lorawan-gateway-backplane/
22
RAK backplane
11
Pi Supply IoT LoRa Gateway HAT for Raspberry Pi
https://uk.pi-supply.com/products/iot-lora-gateway-hat-for-raspberry-pi
15

If you get the message ERROR: [main] failed to start the concentrator after balenaCLoud is finished downloading the application, or when restarting the gateway, it most likely means the GW_RESET_PIN you defined is incorrect. Alternatively the problem can be caused by the hardware, typically for the IMST iC880A-SPI board with insufficient voltage, try another power supply or slightly increase the voltage.

Special note for using a Raspberry Pi 3

There is a backward incomatibility between the Raspberry Pi 1 and 2 hardware, and Raspberry Pi 3. For Raspberry Pi 3, it is necessary to make a small additional configuration change.

Click <- to go back to the Device List, and note that on the left there is an option called "Fleet Configuration". Click it.

Add a New config variable as follows:

Application config variables

Name Value
BALENA_HOST_CONFIG_core_freq 250
BALENA_HOST_CONFIG_dtoverlay pi3-miniuart-bt

TRANSFERRING TTN GATEWAY SOFTWARE TO BALENACLOUD SO THAT IT MAY BE DOWNLOADED ON YOUR DEVICES

  1. On your computer, clone this git repo. For example in a terminal on Mac or Linux type:

    git clone https://github.com/kersing/ttn-resin-gateway-rpi-1
    cd ttn-resin-gateway-rpi-1/
  2. Now, type the command that you'll see displayed in the edit control in the upper-right corner of the balenaCloud devices dashboard for your device. This command "connects" your local directory to the balenaCloud GIT service, which uses GIT to "receive" the gateway software from TTN, and it looks something like this:

    git remote add balena [email protected]:youraccount/yourapplication.git
  3. Add your SSH public key to the list at https://dashboard.balena-cloud.com/preferences/sshkeys. You may need to search the internet how to create a SSH key on your operating system, where to find it afterwards, copy the content, and paste the content to the balenaCloud console.

  4. Type the following commands into your terminal to "push" the TTN files up to balenaCloud:

    git add .
    git commit -m "first upload of ttn files to balenaCloud"
    git push -f balena master
  5. What you'll now see happening in terminal is that this "git push" does an incredible amount of work:

  6. It will upload a Dockerfile, a "build script", and a "run script" to balenaCloud

  7. It will start to do a "docker build" using that Dockerfile, running it within a QEMU ARM virtual machine on the balenaCloud service.

  8. In processing this docker build, it will run a "build.sh" script that downloads and builds the packet forwarder executable from source code, for RPi+iC880A-SPI.

  9. When the build is completed, you'll see a unicorn 🦄 ASCII graphic displayed in your terminal.

  10. Now, switch back to your device dashboard, you'll see that your Raspberry Pi is now "updating" by pulling the Docker container from the balenaCloud service. Then, after "updating", you'll see the gateway's log file in the window at the lower right corner. You'll see it initializing, and will also see log output each time a packet is forwarded to TTN. You're done!

Troubleshooting

If you get the error below please check if your ssh public key has been added to you balenaCloud account. In addition verify whether your private key has the correct permissions (i.e. chmod 400 ~/.ssh/id_rsa).

$ git push -f balena master
Connection closed by xxx.xxx.xxx.xxx port 22
fatal: Could not read from remote repository.

Please make sure you have the correct access rights
and the repository exists.
$

Pro Tips

  • At some point if you would like to add a second gateway, third gateway, or a hundred gateways, all you need to do is to add a new device to your existing Application. You needn't upload any new software to balenaCloud, because balenaCloud already knows what software belongs on the gateway. So long as the environment variables are configured correctly for that new device, it'll be up and running immediately after you burn an SD card and boot it.

  • balenaCloud will automatically restart the gateway software any time you change the environment variables. You'll see this in the log. Also, note that balenaCloud restarts the gateway properly after power failures. If the packet forwarder fails because of an error, it will also automatically attempt to restart.

  • If you'd like to update the software across all the gateways in your device fleet, simply do the following:

    git add .
    git commit -m "Updated gateway version"
    git push -f balena master
    
  • For devices without a GPS, the location that is configured on the TTN console is used. This location is only read at startup of the gateway. Therefore, after you set or changed the location, restart the application from the balenaCloud console.

Device statistics

If you want to show nice looking statistics for your gateway(s) there are a couple of additional steps to take. First, copy Dockerfile.template.metering to Dockerfile.template. Next copy start.sh.metering to start.sh. Now use the instructions above to update the balenaCloud image.

Once the new image is deployed, go to the balenaCloud dashboard for your devices and select 'Enable Public device URL' in the drop down menu (the one to the right of the light bulb). That is all that is required to provide metrics. Now you will need to install a metrics collector on a seperate system as outlined in Fleet-wide Machine Metrics Monitoring in 20mins.

(To show packet forwarder graphs you need to add your own graphs to the provided templates)

Credits

You might also like...
Chinese Mandarin tts text-to-speech  中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,
Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,

Chinese mandarin text to speech based on Fastspeech2 and Unet This is a modification and adpation of fastspeech2 to mandrin(普通话). Many modifications t

Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Parallel Tacotron2 Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Pytorch Implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension)
Pytorch Implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension)

DiffSinger - PyTorch Implementation PyTorch implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension). Status

This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).

Non-autoregressive Deep Learning-Based TTS Template This is a template for the Non-autoregressive TTS model. It contains Data Preprocessing Pipeline D

Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

One model to speak them all 🌎 Audio Language Text ▷ Chinese 人人生而自由,在尊严和权利上一律平等。 ▷ English All human beings are born free and equal in dignity and rig

Make your AirPlay devices as TTS speakers

Apple AirPlayer Home Assistant integration component, make your AirPlay devices as TTS speakers. Before Use 2021.6.X or earlier Apple Airplayer compon

🗣️ Microsoft Edge TTS for Home Assistant, no need for app_key

Microsoft Edge TTS for Home Assistant This component is based on the TTS service of Microsoft Edge browser, no need to apply for app_key. Install Down

Releases(Alpha)
  • Alpha(Oct 17, 2021)

    A working setup where docker containers communicate through the host networking. This might expose port 1680 which is undesired. A solution should be found by using the docker bridge that will hide port 1680.

    Source code(tar.gz)
    Source code(zip)
Owner
Remko
Wireless (RF) specialist, Teacher Embedded Systems Engineering, IOT and Embedded enthusiast. Scout and Radio amateur.
Remko
A Python library for unevenly-spaced time series analysis

traces A Python library for unevenly-spaced time series analysis. Why? Taking measurements at irregular intervals is common, but most tools are primar

Datascope Analytics 516 Dec 29, 2022
Piotr - IoT firmware emulation instrumentation for training and research

Piotr: Pythonic IoT exploitation and Research Introduction to Piotr Piotr is an emulation helper for Qemu that provides a convenient way to create, sh

Damien Cauquil 51 Nov 09, 2022
Contrastively Disentangled Sequential Variational Audoencoder

Contrastively Disentangled Sequential Variational Audoencoder (C-DSVAE) Overview This is the implementation for our C-DSVAE, a novel self-supervised d

Junwen Bai 35 Dec 24, 2022
Python code for the paper How to scale hyperparameters for quickshift image segmentation

How to scale hyperparameters for quickshift image segmentation Python code for the paper How to scale hyperparameters for quickshift image segmentatio

0 Jan 25, 2022
Multilingual Image Captioning

Multilingual Image Captioning Authors: Bhavitvya Malik, Gunjan Chhablani Demo Link: https://huggingface.co/spaces/flax-community/multilingual-image-ca

Gunjan Chhablani 32 Nov 25, 2022
Time Delayed NN implemented in pytorch

Pytorch Time Delayed NN Time Delayed NN implemented in PyTorch. Usage kernels = [(1, 25), (2, 50), (3, 75), (4, 100), (5, 125), (6, 150)] tdnn = TDNN

Daniil Gavrilov 79 Aug 04, 2022
Oscar and VinVL

Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks VinVL: Revisiting Visual Representations in Vision-Language Models Updates

Microsoft 938 Dec 26, 2022
Reproduction of Vision Transformer in Tensorflow2. Train from scratch and Finetune.

Vision Transformer(ViT) in Tensorflow2 Tensorflow2 implementation of the Vision Transformer(ViT). This repository is for An image is worth 16x16 words

sungjun lee 42 Dec 27, 2022
CARL provides highly configurable contextual extensions to several well-known RL environments.

CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments.

AutoML-Freiburg-Hannover 51 Dec 28, 2022
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Mayur 119 Nov 24, 2022
GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

If you are using this code in your own project, please cite our paper: @inproceedings{awiszus2020toadgan, title={TOAD-GAN: Coherent Style Level Gene

Maren A. 13 Dec 14, 2022
Full body anonymization - Realistic Full-Body Anonymization with Surface-Guided GANs

Realistic Full-Body Anonymization with Surface-Guided GANs This is the official

Håkon Hukkelås 30 Nov 18, 2022
[Nature Machine Intelligence' 21] "Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence"

[UCADI] COVID-19 Diagnosis With Federated Learning Intro We developed a Federated Learning (FL) Framework for global researchers to collaboratively tr

HUST EIC AI-LAB 30 Dec 12, 2022
A Python implementation of active inference for Markov Decision Processes

A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove

235 Dec 21, 2022
A Python wrapper for Google Tesseract

Python Tesseract Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded i

Matthias A Lee 4.6k Jan 05, 2023
Distilled coarse part of LoFTR adapted for compatibility with TensorRT and embedded divices

Coarse LoFTR TRT Google Colab demo notebook This project provides a deep learning model for the Local Feature Matching for two images that can be used

Kirill 46 Dec 24, 2022
Modelisation on galaxy evolution using PEGASE-HR

model_galaxy Modelisation on galaxy evolution using PEGASE-HR This is a labwork done in internship at IAP directed by Damien Le Borgne (https://github

Adrien Anthore 1 Jan 14, 2022
A deep learning framework for historical document image analysis

DIVA-DAF Description A deep learning framework for historical document image analysis. How to run Install dependencies # clone project git clone https

9 Aug 04, 2022
DumpSMBShare - A script to dump files and folders remotely from a Windows SMB share

DumpSMBShare A script to dump files and folders remotely from a Windows SMB shar

Podalirius 178 Jan 06, 2023
AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

Frank Liu 26 Oct 13, 2022