Zhongliang, Zhao; Carrera, Jose; Niklaus, Joël; Braun, Torsten (18 June 2018). Machine Learning-based Real-Time Indoor Landmark Localization In: Chowdhury, K.; Di Felice, M.; Matta, I.; Sheng, B.; (eds.) 16th International Conference on Wired/Wireless Internet Communication. Lecture Notes in Computer Science: Vol. 10866 (pp. 95-106). Springer 10.1007/978-3-030-02931-9_8
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Nowadays, smartphones can collect huge amounts of data from their surroundings with the help of highly accurate sensors. Since the combination of the Received Signal Strengths of surrounding access points and sensor data is assumed to be unique in some locations, it is possible to use this information to accurately predict smartphones' indoor locations. In this work, we apply machine learning methods to derive the correlation between smartphones' locations and the received Wi-Fi signal strength and sensor values. We have developed an Android application that is able to distinguish between rooms on a floor, and special landmarks within the detected room. Our real-world experiment results show that the Voting ensemble predictor outperforms individual machine learning algorithms and it achieves the best indoor landmark localization accuracy of 94% in office-like environments. This work provides a coarse-grained indoor room recognition and landmark localization within rooms, which can be envisioned as a basis for accurate indoor positioning.
Item Type: |
Conference or Workshop Item (Paper) |
|---|---|
Division/Institute: |
Business School > Institute for Public Sector Transformation > Data and Infrastructure Business School |
Name: |
Zhongliang, Zhao; Carrera, Jose; Niklaus, Joël  0000-0002-2779-1653; Braun, Torsten; Chowdhury, K.; Di Felice, M.; Matta, I.; Sheng, B. and |
ISBN: |
978-3-030-02930-2 |
Series: |
Lecture Notes in Computer Science |
Publisher: |
Springer |
Language: |
English |
Submitter: |
Safiya Verbruggen |
Date Deposited: |
25 Aug 2023 09:57 |
Last Modified: |
28 Apr 2024 02:22 |
Publisher DOI: |
10.1007/978-3-030-02931-9_8 |
Related URLs: |
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Uncontrolled Keywords: |
Machine learning Indoor localization Real-time landmark detection |
ARBOR DOI: |
10.24451/arbor.19708 |
URI: |
https://arbor.bfh.ch/id/eprint/19708 |
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