An efficient crowd-sourcing-based approach for fingerprint database updating

Xi Liu, Jian Cen, Yiju Zhan and Chengpei Tang

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 6, 1550147719858512

Abstract: Fingerprint-based indoor localization has become one of the most attractive and promising techniques; however, one primary concern for this technology to be fully practical is to maintain the fingerprint database to combat harsh indoor environmental dynamics, especially in the large-scale and long-term deployment. In this article, focusing on three key problems now existing in fingerprint database updating approaches such as the mechanism for triggering updates, the collection of new fingerprints and determination of fingerprints’ location information, we propose a fuzzy map mechanism and decision methods of neighbours’ fingerprints in response to all kinds of changes in indoor environments. Meanwhile, we design a static data collecting mechanism to filter reliable information from numerous users’ inputs and propose a neighbours’ fingerprint-assisted technique to calculate the location of fingerprints. Experimental results demonstrate that the proposed solution not only improves the performance of updating the fingerprint database in real time and robustness by 40% and 50%, respectively, but also reduces the update frequency and improves mean location accuracy by over 40%.

Keywords: Indoor location; crowd-sourcing; automatic fingerprint update; deterministic techniques (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:6:p:1550147719858512

DOI: 10.1177/1550147719858512

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