Fingerprint matching is adopted by a large family of indoor localization schemes, where collecting
fingerprints is inevitable but all consuming. While the increasingly popular crowdsourcing based approach provides
an opportunity to relieve the burden of fingerprints collecting, a number of formidable challenges for such an
approach have yet been studied. For instance, querying in a large fingerprints database for matching process takes
a lot of time and calculation; fingerprints collected by crowdsourcing lacks of robustness because of heterogeneous
devices problem. Those are important challenges which impede practical deployment of the fingerprint matching
indoor localization system. In this study, targeting on effectively utilizing and mining large amount fingerprint
data, enhancing the robustness of fingerprints under heterogeneous devices’ collection and realizing the real time
localization response, we propose a crowdsourcing based fingerprints collecting mechanism for indoor localization
systems. With the proposed approach, massive raw fingerprints will be divided into small clusters while diverse
devices’ uploaded fingerprints will be merged for overcoming device heterogeneity, both of which will contribute
to reduce response time. We also build a mobile cloud testbed to verify the proposed scheme. Comprehensive real
world experiment results indicate that the scheme can provide comparable localization accuracy.
HUANG Zheng-yong* (黄正勇), YU Hui (俞 晖), GUAN Yun-feng (管云峰), CHEN Kun (陈 坤)
. Indoor Localization with a Crowdsourcing Based Fingerprints Collecting[J]. Journal of Shanghai Jiaotong University(Science), 2015
, 20(5)
: 548
-557
.
DOI: 10.1007/s12204-015-1662-3
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