Research Article
An Enhanced Density-based Clustering Algorithm for the Autonomous Indoor Localization
@INPROCEEDINGS{10.4108/icst.mobilware.2013.254284, author={Yaqian Xu and Rico Kusber and Klaus David}, title={An Enhanced Density-based Clustering Algorithm for the Autonomous Indoor Localization}, proceedings={6th International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications}, publisher={IEEE}, proceedings_a={MOBILWARE}, year={2014}, month={7}, keywords={time complexity of algorithms run time of algorithms density-based clustering algorithm fingerprinting-based indoor localization}, doi={10.4108/icst.mobilware.2013.254284} }- Yaqian Xu
Rico Kusber
Klaus David
Year: 2014
An Enhanced Density-based Clustering Algorithm for the Autonomous Indoor Localization
MOBILWARE
IEEE
DOI: 10.4108/icst.mobilware.2013.254284
Abstract
Indoor localization applications are expected to become increasingly popular on smart phones. Meanwhile, the development of such applications on smart phones has brought in a new set of potential issues (e.g., high time complexity) while processing large datasets. The study in this paper provides an enhanced density-based cluster learning algorithm for the autonomous indoor localization algorithm DCCLA (Density-based Clustering Combined Localization Algorithm). In the enhanced algorithm, the density-based clustering process is optimized by “skipping unnecessary density checks” and “grouping similar points”. We conducted a theoretical analysis of the time complexity of the original and enhanced algorithm. More specifically, the run times of the original algorithm and the enhanced algorithm are compared on a PC (personal computer) and a smart phone, identifying the more efficient density-based clustering algorithm that allows the system to enable autonomous Wi-Fi fingerprint learning from large Wi-Fi datasets. The results show significant improvements of run time on both a PC and a smart phone.