Bluetooth Beacon Based Accurate Indoor Positioning Using Machine Learning

The objective of this work is to develop an indoor location system with high precision and continuous position monitoring in real time with the use of a mobile phone without any special hardware using only commercially available low-cost sensors. Finding the location is done using the measured Received Signal Strength Indicator (RSSI) value of Bluetooth beacons received from mobile phones combined with measurements from other phone sensors. For the development of our model, we collected measurements for the RSSI values from Beacons which we placed in a space of 30.75 sqm and the values from the mobile accelerometer in motion. We divided the space into 16 subareas of 1.45m x 1.35m and used our measurements to develop a machine learning model using the open source TensorFlow framework to predict the correct subarea of the user. Through experiments, we show that our model can reach an accuracy of 0.7209 which means that our system can predict the correct user location in 72% of the cases with accuracy less than 1 meter.

[1]  Serkan Günal,et al.  A comparative study on machine learning algorithms for indoor positioning , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[2]  Robert Harle,et al.  Location Fingerprinting With Bluetooth Low Energy Beacons , 2015, IEEE Journal on Selected Areas in Communications.

[3]  Sudarshan S. Chawathe,et al.  Beacon Placement for Indoor Localization using Bluetooth , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[4]  Sheikh Tahir Bakhsh,et al.  Indoor positioning in Bluetooth networks using fingerprinting and lateration approach , 2011, 2011 International Conference on Information Science and Applications.

[5]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.

[6]  Yuwei Chen,et al.  Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints , 2012, Wireless Personal Communications.

[7]  Shueng-Han Gary Chan,et al.  Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons , 2016, IEEE Communications Surveys & Tutorials.

[8]  Yunhao Liu,et al.  Enhancing wifi-based localization with visual clues , 2015, UbiComp.

[9]  Bruno Sinopoli,et al.  ALPS: A Bluetooth and Ultrasound Platform for Mapping and Localization , 2015, SenSys.

[10]  Ravi Jain,et al.  Error characteristics and calibration-free techniques for wireless LAN-based location estimation , 2004, MobiWac '04.

[11]  Stefano Chessa,et al.  A stigmergic approach to indoor localization using Bluetooth Low Energy beacons , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[12]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Swarun Kumar,et al.  Decimeter-Level Localization with a Single WiFi Access Point , 2016, NSDI.

[14]  Habib Mohammed Hussien,et al.  Survey on Indoor Positioning Techniques and Systems , 2017, ICT4DA.

[15]  Eckehard Steinbach,et al.  Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning , 2014, UbiComp.

[16]  Yong Ren,et al.  Does BTLE measure up against WiFi? A comparison of indoor location performance , 2014 .

[17]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[18]  Yunhao Liu,et al.  From RSSI to CSI , 2013, ACM Comput. Surv..

[19]  Andreas F. Molisch,et al.  Localization via Ultra- Wideband Radios , 2005 .

[20]  Jue Wang,et al.  Dude, where's my card?: RFID positioning that works with multipath and non-line of sight , 2013, SIGCOMM.

[21]  Aman Jantan,et al.  State-of-the-art in artificial neural network applications: A survey , 2018, Heliyon.