IELS: Indoor Equipment Localization System: Conclusion (Part 6/6 final) (IoT)



We conclude there is a gap between a few of the mentioned research papers  and the commercial products. Most  researches are missing the practical usage of iBeacons and focus primarily on specific problems. However, all commercial solutions does not offer  free-of-cost solutions. We think there is a place for Open Source standard software for indoor localizing System. We therefore aim to focus on practical usage of iBeacons based on Indoor Localization System and in short term share our  knowledge from  this thesis with  potential contributors. We would like to continue working on this project and improve it.  All this we aim  to release it at some point as an Open Source Indoor Localization System standard software and make it possible to use  the  system as crowdsensing. That means people who  has  smartphones with the proper application and thereby be able to collect the location of the smartphone and as well as trackable objects location information periodically. This  should be send to a centralized backend infrastructure based on  end-user behavior and requirement.

The work from Bulten [1], Radboud University in Holland has been a major inspiration for our  thesis. Bulten focuses on practical usage of the iBeacons and has already released source code based on JavaScript.

We think the combination of iBeacons’ price together with long  life battery and easy-to-deploy makes it a perfect choice to use for indoor localization purpose.

Another aspect for future research would be the improvement of the algorithm’s efficiency, and overall improvement of the system as mentioned in discussion.

Finally, it should be concluded that the  environment surrounding iBeacons and smartphone hardware type and model have  a major impact in the final quality of the results regardless the technique.

Extra stuff

IT University of CopenhagenExperitment in 5th floor in ITU IELS longterm concept   360 panorama from PitLabIELS infographic


[1]  Wouter Bulten.   Human slam simultaneous  localisation and configuration (slac) of indoor wireless sensor networks and their user, 2015.

[2]  Song  Chai,  Renbo An, and Zhengzhong Du.   An indoor positioning algorithm using bluetooth low energy RSSI.

[3]  Yu-Chung Cheng, Yatin Chawathe, Anthony LaMarca, and John  Krumm. Accu- racy characterization for metropolitan-scale wi-fi localization.

[4]  Ramsey Faragher and Robert Harle.  An analysis  of the accuracy of bluetooth low energy for indoor positioning applications. In Proceedings of the 27th Inter- national Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2014), Tampa Florida, September 2014, pp. 2011-210., 2014.

[5]  Atul Gosai and Rushi Raval. Real time location based tracking using wifi signals.

[6]  Xiaofan Jiang, Chieh-Jan Mike Liang, Kaifei Chen, Ben Zhang, Jeff Hsu,  Jie Liu, Bin Cao,  and Feng  Zhao. Design and evaluation of a wireless magnetic-based proximity detection platform for indoor applications.

[7]  Philippe Bonne Jonathan Fürst, Kaifei Chen. Evaluating and improving blue- tooth low energy performance in the wild.

[8]  John  Krumm. Ubiquitous Computing Fundamentals. CRC Press, 2010.

[9]  Anthony LaMarca and Eyal de Lara.  Location systems: An introduction to the technology behind location awareness.  Synthesis Lectures  on Mobile and  Per- vasive  Computing, 3(1):1–122,  2008.

[10]  Jea-Gu   Lee,    Byung-Kwan  Kim,    Sung-Bong  Jang,    Seung-Ho  Yeon,   and Young Woong Ko. Accuracy enhancement of rssi-based distance estimation by applying gaussian filter. Indian Journal of Science and  Technology, 9(20), 2016.

[11]  Zhouchi Li, Yang Yang, and Kaveh  Pahlavan.  Using iBeacon for newborns lo- calization in hospitals. In 2016 10th  International Symposium on Medical In- formation and  Communication Technology (ISMICT).  IEEE, 2016.

[12]  Filip  Mazan and Alena  Kovarova.  A study of devising neural network based indoor localization using beacons: First results. In CISJ Vol19 No1 2015., 2015.

[13]  V. N. Padmanabhan P. Bahl.  Radar: An in-building rf-based user location and tracking system. In INFOCOM  2000. Nineteenth Annual Joint Conference of the IEEE Computer and  Communications Societies. Proceedings. IEEE, vol.  2, pp. 775-784., 2000.

[14]  Jeongyeup Paek, JeongGil Ko, and Hyungsik Shin.  A measurement study of BLE iBeacon and geometric adjustment scheme for indoor location-based mobile applications. Mobile Information Systems, 2016:1–13,  2016.

[15]  Piotr  Sapiezynski,  Arkadiusz Stopczynski,  Radu  Gatej,   and Sune Lehmann. Tracking human mobility using WiFi signals. Tracking Human Mobility Using WiFi Signals, 10(7):e0130824, 2015.

[16]  Takahiro Uchiya Ichi Takumi Shinsuke Kajioka,  Tomoya Mori and Hiroshi Mat- suo.  Experiment of indoor position presumption based on rssi of bluetooth le beacon. In IEEE 3rd Global  Conference on Consumer Electronics (GCCE) 2014, 7-10 Oct. 2014., 2014.

[17]  H. Jiang Z. Chen, Q. Zhu  and Y. C. Soh.  Indoor localization using smartphone sensors and ibeacons.  In IEEE 10th  Conference on Industrial Electronics and Applications (ICIEA), Auckland, 2015, pp. 1723-1728., 2015.

[18]  Faheem Zafari and Ioannis Papapanagiotou. Enhancing iBeacon based micro- location with particle filtering.

[19]  Luo Haiyong Zhu Jianyong, Chen Zili and Li Zhaohui. Rssi based bluetooth low energy indoor positioning.  In IEEE 2014 International Conference for Indoor Positioning and  Indoor Navigation (IPIN), 2014.

Leave a Reply

Your email address will not be published. Required fields are marked *