An Effective Adaptive Autocorrelation-Based Neighboring Matching Location-Aware Computing in WLANs

Abstract:

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The recent advances of ubiquitous wireless infrastructures and requirements for high speed context-aware computing have created the opportunities to supply the high efficient location based service (LBS) in indoor wireless local area network (WLAN) environment. Because of the serious multi-path effect, unpredictable co-channel interference and inherent equipment noise, the measured signal strengths vary a lot in the real-world indoor environment. And this strength variation will also result in the performance deterioration of radio map-based neighboring matching algorithm. In response to this compelling problem, we propose the adoption of adaptive autocorrelation-based signal preprocessing method as a specific solution by effectively eliminating the singular strength from the original fingerprint set. Finally, the feasibility and effectiveness of autocorrelation-based preprocessing are also verified by decreasing about 33.4% and 32.9% of errors in k nearest neighbor (KNN) and weighted KNN (WKNN).

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

2011-2014

DOI:

10.4028/www.scientific.net/AMR.204-210.2011

Citation:

Y. B. Xu et al., "An Effective Adaptive Autocorrelation-Based Neighboring Matching Location-Aware Computing in WLANs", Advanced Materials Research, Vols. 204-210, pp. 2011-2014, 2011

Online since:

February 2011

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$35.00

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