A Realistic Evaluation and Comparison of Indoor Location Technologies: Experiences and Lessons Learned

Filip Lemic, Jasper Buesch, Zhiping Jiang, Han Zou, Hao Jiang, Chi Zhang, Ashwin Ashok, Chenren Xu, Patrick Lazik, Niranjini Rajagopal, Anthony Rowe, Avik Ghose, Nasim Ahmed, Zhuoling Xiao, Hongkai Wen, Traian E. Abrudan, Andrew Markham, Thomas Schmid, Daniel Lee, Martin Klepal, Christian Beder, Maciej Nikodem, Szymon Szymczak, Pawel Hoffmann, Leo Selavo, Domenico Giustiniano, Vincent Lenders, Maurizio Rea, Andreas Marcaletti, Christos Laoudias, Demetrios Zeinalipour-Yazti, Yu-Kuen Tsai, Arne Bestmann, Ronne Reimann, Liqun Li, Chunshui Zhao, Stephan Adler, Simon Schmitt, Vincenzo Dentamaro, Domenico Colucci, Pasquale Ambrosini, Andre Ferraz, Lucas Martins, Pedro Bello, Alan Alvino, Vladica Sark, Gerald Pirkl, Peter Hevesi, Xue Yang, Romit Roy Choudhury, Vlado Handziski, Souvik Sen, Dimitrios Lymberopoulos, Jie Liu

The 14th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN '15) |

Published by ACM - Association for Computing Machinery

We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation study’s results, discuss the current state-ofthe-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.