WiSpy is a Flask web application predicting room occupancy across the University College Dublin campus based on historical Wi-Fi log data. It also employs RSSI and audio data alongside face detection methods in rooms with a leaking Wi-Fi signal.

WiSpy is has a live version

Users can:

• View predicted occupancy (both occupancy sensing and a continuous headcount) for any room and period for which we have data.
• Compare the occupancy of different rooms and classes.
• Add new data by dragging and dropping files on the "Upload new data" page.

Version

1 . 3

Technologies

WiSpy uses a number of open-source projects:

• Python 3.5.x -
• AngularJS - HTML enhanced for web apps
• Flask - Micro-web framework for Python apps
• node-sass - CSS with more features
• SQLite - software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine.
• node.js - used to install front-end dependencies via NPM
• Various Python modules used for statistical modelling, database interaction and data gathering, installed via requirements.txt.

Installation

WiSpy requires Node.JS and Python 3 be installed.

To install WiSpy:

$ git clone https://github.com/lukekearney/research-practicum

Run the install script, which will prompt you if you need to install any dependencies:

python install.py

Python dependencies are installed via:

pip install -r requirements.txt

If assets are not compiled or installed, switch directory to app/static and run:

npm install to install additional assets and third party libraries

npm run build-css to compile SCSS to CSS

gulp to create minified versions of the relevant assets

License

MIT

Free Software