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Mobile Information Systems - Volume 2016 2016, Article ID 4041291, 16 pages -

Review Article

Shanghai Key Laboratory of Navigation and Location-Based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Department of Computer Science, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, 02431 Masala, Finland

Received 17 January 2016; Accepted 26 April 2016

Academic Editor: Daniele Riboni

Copyright © 2016 Ling Pei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sensor-rich smartphone enables a novel approach to training the fingerprint database for mobile indoor localization via crowd sensing. In this survey, we discuss the crowd sensing based mobile indoor localization in terms of foundational knowledge, signals of fingerprints, trajectory of obtaining fingerprints, indoor maps, evolution of a fingerprint database, positioning algorithms, state-of-the-art solutions, and challenges. The survey concludes that the crowd sensing is a low cost solution of generating and updating an organic fingerprint database. Although the crowd sensing concept is widely accepted by the academic community in these years, there are a lot of unsolved problems which hinder the concept of transferring into a practical system. We address the challenges and predict future trends in the end.





Autor: Ling Pei, Min Zhang, Danping Zou, Ruizhi Chen, and Yuwei Chen

Fuente: https://www.hindawi.com/



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