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Digital FootPrint

CUHacking 2017 - "Digital FootPrint" is a web application that visualizes a Personalized Home Network of interconnected devices.

Inspiration

This project was inspired by Martello's Coding Challenge of creating an application that continuously collects and visualizes all of the devices which are connected on a simulated home network.

What it does

Our Hack displays a personlized dashboard of a user's connected devices in their home network. Martello's API is accessed, filtered, analyzed and sent to the user's Personalized Home Network which will display key information on all connected devices.

How we built it

Digital FootPrint is built using a Web application as the primary source of UI to the end users. Back End development is done in Python utilizing libraries such as Flask, Pandas, Numpy and urllib. The Web application is built through bootstrapping javascript/html/css.

Challenges we ran into

Some challenges the team ran into included fetching and cleaning the dataset from Martello's API an integration of both Front End and Back End of the project.

Accomplishments that we're proud of

Built a sleak user interface for the web application. Team Members understood fundamental networking concepts and the Simple Network Management Protocol used to create the dataset for Martello's API. Complete the logic of the implementation. Every member was able to learned new technologies. Team was able to work off each other’s skill sets while assisting one another on all aspects of the Hack. The team is proud of the completion of an Web Application. The team gained development experience Bootstrapping for Web Development and Python Development.

What's next for Digital FootPrint

Plans to make additions to the dashboard with more visualizations and analysis that the end user wants to personally track.

Built With

Submitted to

CUHacking

Created by

I worked as a Back End Developer.

deleted deleted
I worked on the front-end. Made use of HTML,CSS and Bootstrap. Assisted by finding solutions for the issue of connecting front end to back-end.

Ameera Alam
I worked on the back-end. I used Python to connect to Martello's API to fetch the dataset and analyze it to find patterns of system(s) in critical condition or if the internet is slowing down. I also extracted real-time data from Martello's server to create a dataset in CSV format that contains 4-hour worth of information about the devices' state in the network.

We used Python's Flask API to host a local server and connect with the javascript-based front-end.

Shamir Alavi
Interests: Deep Learning
I worked on the front-end. HTML, CSS, Bootstrap and javascript. I used javascript libraries and helped connect to backend using the flask api.

Parthkhanna150 Khanna

Updates

deleted deleted started this project — Mar 05, 2017 09:17 AM EST

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