eWINE project - SIGFOX toolbox

This repository contains the SIGFOX experimentation toolbox developed in the Horizon 2020 eWINE project. The toolbox allows for rapid development of experiments involving Low-power wide-area networks (LP-WAN) and ultra-narrowband (UNB) technology.

Most components can be used with a visual editor and a flow-based programming approach in the Node-RED tool. They have been developed for use with equipment in the LOG-a-TEC testbed and the SIGFOX network, although some may find a wider use.

Note that some components are not present in the public repository due to a non-disclosure agreement.

Getting started

The recommended way of getting started with the toolbox:

1. Follow the general installation instructions in this README.md

2. Install all individual components from the subdirectories, following their specific installation instructions.

3. Try running the packet loss Node-RED flow.

Installation instructions

These instructions assume a reasonably up-to-date Linux system with a Python 3.x interpreter already installed.

While exact versions of software packages shouldn't matter, this toolbox was last checked to be working with the following:

• Debian GNU/Linux release 8.7 (Jessie),
• Node-RED 0.15.1, running under Node.js 4.7.3,
• Python 3.5.2,
• bokeh 0.12.4,
• numpy 1.12.0,
• vesna-spectrumsensor 1.0.6

To perform example experiments from the toolbox you will also need:

• Network access to a SNE-ESHTER spectrum sensor (part of the LOG-a-TEC testbed)

• SIGFOX back-end login credentials that allow callback configuration or access to a host in LOG-a-TEC testbed that is already set up to receive back-end callbacks.

• Network access to a SIGFOX test device.

Setting up a new SIGFOX test device also requires:

• SIGFOX back-end login credentials that allow device management.

• SFXLIB binary library.

• SIGFOX device id, AES key and PAC code.

• VESNA SNC board, Olimex ARM-USB-OCD programmer, USRP device.

Node-RED

See installation section in the Node-RED documentation. Some components in the repository require that Node-RED is installed under your home directory, in the ~/node_modules/node-red directory.

Please note that installing Node-RED on an Internet-connected computer will by default enable anyone to run arbitrary code on your computer. This is a security risk. Reading the security section of the manual is highly recommended before proceeding.

Python virtual environment

It is recommended that a clean Python virtual environment is made before installing individual Python packages contained in this repository. See Creation of virtual environments for details.

In brief, you can create a new environment by running the following commands in a terminal:

$ cd sigfox-toolbox
$ export PYTHONPATH=
$ python3 -mvenv venv
$ . venv/bin/activate
$ pip install -U pip

Then, in each new terminal you wish to use the Python components, run:

$ cd sigfox-toolbox
$ . venv/bin/activate

Contents

See README.md files in individual sub-directories for details and installation instructions.

• node-red-python - Support for Node-RED Python blocks.

• node-red-spectrum-sensing - Node-RED blocks for spectrum sensing.

• node-red-sigfox-web-api - Node-RED blocks for communicating with the SIGFOX backend API.

• node-red-visualization - Real-time visualization block for Node-RED flows.

• vesna-sfxlib-rpc-firmware - Firmware for running the proprietary SIGFOX MAC protocol library on a VESNA sensor node over a remote-procedure call interface.

• flow-keyapp - Node-RED flow for controlling the SIGFOX KeyApp device.

• flow-packet-loss - Node-RED flow for measuring packet loss in a SIGFOX network.

• sigfox-usrp-phy - Physical layer implementation of the SIGFOX protocol using GNU Radio and a USRP N200 device, with a related Node-RED block. (Not in public repository)

• sigfox-test-device - Full software implementation of a SIGFOX modem usable from Node-RED. (Not in public repository)

License

See README.md files in individual sub-directories for details.

The research leading to these results has received funding from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116.