Hawki (Indoor Positioning Framework)
Hawki is the framework system for indoor positioning service.
Indoor positioning technology will use in a variety of ways including IOT, Indoor-navigation. Hawki allows you to find where you are in the building or subway by using your wifi-enabled device such as android, iphone, etc.
Simply, Hawki provide whole systems for indoor positioning that include Server-side, Client-side, Predicting model
Hawki system is built on three main components,
- Server : Mediating between Predictor and client. Built with Flask (python)
- Client Application : Collect wifi radio map or etc, show position on the map. Android >= 6.0, iPhone(Not-Implemented)
- Predictors (server) : Predicting user's position in the building. Built with Scikit, Pytrain
Hawki test video -> https://www.youtube.com/watch?v=EifW9AjWF0g&feature=youtu.be
Hawki field test video ( in coex mall ) -> https://www.youtube.com/watch?v=PaCcq-pzsbY
1. Install server
$ git clone https://github.com/socc-io/Hawki.git $ cd Hawki $ ./start.py [PORT_NUMBER] - ex) ./start.py 4000
2. Install Client
Install Android-Studio : https://developer.android.com/studio/index.html?hl=ko File -> Import Existing Project -> PATH_CLONE_HAWKI/APP/Hawki
3. Select Building
4. Collecting your indoor data using APP
1) After Open application, Click the Collector button 2) Search building name that you are located on and select 3) Input your coordinate on map and push collect button
5. Training indoor data
1) After Step 3, You can see your collected building's indoor data $ ./lsbid.sh YOUR BUILDING DATAS ================================================= 12665691.dat 17573702.dat 18059921.dat 22251293.dat 27539636.dat 2) Train your building data $ ./trainer.py [BUILDING_ID] - ex) ./trainer.py 12665691
6. Predicting location using APP
1) After Open application, Click the Finder button 2) Search building name that you are located on and push find button
An Unsupervised Indoor Localization Method based on Received Signal Strength RSS Measurements [http://www.merl.com/publications/docs/TR2015-129.pdf]
Unsupervised Indoor Localization No Need to War-Drive [http://engr.uconn.edu/~song/classes/nes/unloc.pdf]
Building a Practical Wifi-Based Indoor Navigation System, Dongsoo Han, Sukhoon Jung, Minkyu Lee and Giwan Yoon, KAIST
Indoor Location Sensing Using Geo-Magnetism, Jaewoo Chung, Matt Donahoe, Chris Schmandt, Ig-Jae Kim, Pedram Razavai, Micaela Wiseman, MIT Media Laboratory 20 Ames St.
Vessel integrated information management system based on Wifi Positioning technology, Hyuk-soon Kwan, Dongsoo Han, Song-Que Park, Won-Hee An, Taehyun Park, Net Co.Ltd.
Copyright (c) 2016 Captain-Americano
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