A Weighted K-AP Query Method for RSSI based Indoor Positioning

Huan Huo, Xiufeng Liu, Jifeng Li, Huhu Yang, Dunlu Peng, Qingkui Chen

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    The paper studies the establishment of offline fingerprint library based on RSSI (Received Signal Strength Indication), and proposes WF-SKL algorithm by introducing the correlation between RSSIs. The correlations can be transformed as AP fingerprint sequence to build the offline fingerprint library. To eliminate the positioning error caused by instable RSSI value, WF-SKL can filter the noise AP via online AP selection, meanwhile it also reduces the computation load. WF-SKL utilizes LCS algorithm to find out the measurement between the nearest neighbors, and it proposes K-AP (P,Q) nearest neighbor queries between two sets based on Map-Reduce framework. The algorithm can find out K (P,Q) nearest positions and weighted them for re-positioning to accelerate the matching speed between online data and offline data, and also improve the efficiency of positioning. According to a large scale positioning experiments, WF-SKL algorithm proves its high accuracy and positioning speed comparing with KNN indoor positioning.
    Original languageEnglish
    Title of host publicationDatabases Theory and Applications. ADC 2016
    Publication date2016
    ISBN (Print)978-3-319-46921-8
    ISBN (Electronic)978-3-319-46922-5
    Publication statusPublished - 2016
    Event27th Australasian Database Conference - Sydney, Australia
    Duration: 28 Sep 201629 Sep 2016


    Conference27th Australasian Database Conference
    Internet address
    SeriesLecture Notes in Computer Science

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