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
Implementation of Pix2Seq in PyTorch

pix2seq-pytorch Implementation of Pix2Seq paper Different from the paper image input size 1280 bin size 1280 LambdaLR scheduler used instead of Linear

Tony Shin 9 Dec 15, 2022
Nvidia Semantic Segmentation monorepo

Paper | YouTube | Cityscapes Score Pytorch implementation of our paper Hierarchical Multi-Scale Attention for Semantic Segmentation. Please refer to t

NVIDIA Corporation 1.6k Jan 04, 2023
Implementation of BI-RADS-BERT & The Advantages of Section Tokenization.

BI-RADS BERT Implementation of BI-RADS-BERT & The Advantages of Section Tokenization. This implementation could be used on other radiology in house co

1 May 17, 2022
Code for Dual Contrastive Learning for Unsupervised Image-to-Image Translation, NTIRE, CVPRW 2021.

arXiv Dual Contrastive Learning Adversarial Generative Networks (DCLGAN) We provide our PyTorch implementation of DCLGAN, which is a simple yet powerf

119 Dec 04, 2022
Papers about explainability of GNNs

Papers about explainability of GNNs

Dongsheng Luo 236 Jan 04, 2023
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai

Amazon Web Services - Labs 123 Dec 23, 2022
Implement some metaheuristics and cost functions

Metaheuristics This repot implement some metaheuristics and cost functions. Metaheuristics JAYA Implement Jaya optimizer without constraints. Cost fun

Adri1G 1 Mar 23, 2022
Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Deepak Nandwani 1 Dec 31, 2021
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
Multiband spectro-radiometric satellite image analysis with K-means cluster algorithm

Multi-band Spectro Radiomertric Image Analysis with K-means Cluster Algorithm Overview Multi-band Spectro Radiomertric images are images comprising of

Chibueze Henry 6 Mar 16, 2022
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
social humanoid robots with GPGPU and IoT

Social humanoid robots with GPGPU and IoT Social humanoid robots with GPGPU and IoT Paper Authors Mohsen Jafarzadeh, Stephen Brooks, Shimeng Yu, Balak

0 Jan 07, 2022
A project that uses optical flow and machine learning to detect aimhacking in video clips.

waldo-anticheat A project that aims to use optical flow and machine learning to visually detect cheating or hacking in video clips from fps games. Che

waldo.vision 542 Dec 03, 2022
Official PyTorch implementation of the paper: Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting.

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Official PyTorch implementation of the paper: Improving Graph Neural Net

Giorgos Bouritsas 58 Dec 31, 2022
The code for our paper "AutoSF: Searching Scoring Functions for Knowledge Graph Embedding"

AutoSF The code for our paper "AutoSF: Searching Scoring Functions for Knowledge Graph Embedding" and this paper has been accepted by ICDE2020. News:

AutoML Research 64 Dec 17, 2022
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

Benedek Rozemberczki 202 Dec 27, 2022
Codes for NeurIPS 2021 paper "On the Equivalence between Neural Network and Support Vector Machine".

On the Equivalence between Neural Network and Support Vector Machine Codes for NeurIPS 2021 paper "On the Equivalence between Neural Network and Suppo

Leslie 8 Oct 25, 2022
Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory

Approximate Outer Product Gradient Descent with Memory Code for the numerical experiment of the paper Speeding-Up Back-Propagation in DNN: Approximate

2 Mar 02, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
moving object detection for satellite videos.

DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos Algorithm Introduction DSFNet: Dynamic and Static Fusion Net

xiaochao 39 Dec 16, 2022