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

D. Lymberopoulos‚ Jie Liu‚ Xue Yang‚ R. R. Choudhury‚ V. Handziski‚ S. Sen‚ F. Lemic‚ J. Buesch‚ Z. Jiang‚ Han Zou‚ Hao Jiang‚ Chi Zhang‚ Ashwin Ashok‚ Chenren Xu‚ P. Lazik‚ N. Rajagopal‚ A. Rowe‚ A. Ghose‚ N. Ahmed‚ Zhuoling Xiao‚ Hongkai Wen‚ T. E. Abrudan‚ A. Markham‚ T. Schmid‚ D. Lee‚ M. Klepal‚ C. Beder‚ M. Nikodem‚ S. Szymczak‚ P. Hoffmann‚ L. Selavo‚ D. Giustiniano‚ V. Lenders‚ M. Rea‚ A. Marcaletti‚ C. Laoudias‚ D. Zeinalipour−Yazti‚ Yu−Kuen Tsai‚ A. Bestmann‚ R. Reimann‚ Liqun Li‚ Chunshui Zhao‚ S. Adler‚ S. Schmitt‚ V. Dentamaro‚ D. Colucci‚ P. Ambrosini‚ A. Ferraz‚ L. Martins‚ P. Bello‚ A. Alvino‚ V. Sark‚ G. Pirkl and P. Hevesi


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.

Book Title
The 14th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN 2015)
ACM – Association for Computing Machinery