Indoor Localization with a Crowdsourcing Based Fingerprints Collecting

doi:10.1007/s12204-015-1662-3

摘要/Abstract

摘要： 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.

关键词: indoor localization, crowdsourcing, cluster, device diversity, fingerprint extraction

Abstract: 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.

Key words: indoor localization, crowdsourcing, cluster, device diversity, fingerprint extraction

中图分类号:

TN 929.5

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]. 上海交通大学学报（英文版）, 2015, 20(5): 548-557.

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.

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