A Novel Algorithm Based on Clustering and Access Points Selection for Indoor Fingerprint Localization

Abstract:

With the growing popularity of location-based service (LBS), wireless local area networks (WLAN) indoor positioning has gained widespread attention. Unlike the traditional algorithm concentrating on positioning accuracy, we discuss how to improve the real-time property in WLAN indoor fingerprinting localization systems. In this paper, we present a novel algorithm which first divides the positioning area into sub-areas utilizing k-means clustering, and then selects appropriate access points (APs) for positioning to make the calculated amount as less as possible. By collecting data and performing in the real WLAN environment, our proposed algorithm shows high positioning accuracy while the computational burden has been decreased almost 93.7%.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 3527-3531
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3527
• Online since: September 2013
• Authors: Xing Zhang, Lin Ma, Xue Zhi Tan, Shi Zeng Guo
• Keywords: Access Points Selection, Indoor Positioning, K-Means Clustering, Location Fingerprinting, WLAN

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