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
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Hawki test video -> https://www.youtube.com/watch?v=EifW9AjWF0g&feature=youtu.be
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Hawki field test video ( in coex mall ) -> https://www.youtube.com/watch?v=PaCcq-pzsbY
$ git clone https://github.com/socc-io/Hawki.git
$ cd Hawki
$ ./start.py [PORT_NUMBER]
- ex) ./start.py 4000
Install Android-Studio : https://developer.android.com/studio/index.html?hl=ko
File -> Import Existing Project -> PATH_CLONE_HAWKI/APP/Hawki
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
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
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
Hyeok Oh [ [email protected] ] site : https://github.com/oh4851
Sunho Jung [ [email protected] ] site : https://github.com/sunhojeong
Youngje jo [ [email protected] ] site : https://github.com/siosio34
Jinwon Lee [ [email protected] ] site : https://github.com/jino678
SeoHyun Back [ [email protected] ] site : https://github.com/becxer
YoungJin Kim [ [email protected] ] site : https://github.com/smliu97
The MIT License (MIT)
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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