Loading web-font TeX/Math/Italic
A Feature-Scaling-Based --Nearest Neighbor Algorithm for Indoor Positioning Systems | IEEE Journals & Magazine | IEEE Xplore

A Feature-Scaling-Based k-Nearest Neighbor Algorithm for Indoor Positioning Systems


Abstract:

With the increasing popularity of WLAN infrastructure, WiFi fingerprint-based indoor positioning systems have received considerable attention recently. Much existing work...Show More

Abstract:

With the increasing popularity of WLAN infrastructure, WiFi fingerprint-based indoor positioning systems have received considerable attention recently. Much existing work in this aspect adopts classification techniques that match a vector of radio signal strengths (RSSs) reported by a mobile station (MS) to pretrained reference fingerprints sampled from different access points (APs) at different reference points (RPs) with known positions. However, in the calculation of signal distances between different RSS vectors, existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal differences in geometrical distances in complex indoor environment. To address this issue, in this paper, we propose a feature-scaling-based k-nearest neighbor (FS-kNN) algorithm for achieving improved localization accuracy. In FS-kNN, we build a novel RSS-level-based FS model, which introduces RSS-level-based scaling weights in the computation of effective signal distances between signal vector reported by a MS and reference fingerprints in a radio map. Experimental results show that FS-kNN can achieve an average location error as low as 1.70 m, which is superior to existing work.
Published in: IEEE Internet of Things Journal ( Volume: 3, Issue: 4, August 2016)
Page(s): 590 - 597
Date of Publication: 27 October 2015

ISSN Information:

Funding Agency:


I. Introduction

Indoor positioning has received great attention recently because position information is essential for providing location-based services (LBSs) [1], which offers intelligent services in various fields in the context of Internet of Things (IoT) [2]–[4]. For example, position-based navigation has been used inside Copenhagen Airport [5]. Passengers there can use it to plan their paths inside the airport and to get expected information in an interactive way. Moreover, an indoor tracking system was deployed in Hartford hospital, which helps tracking expensive equipment and also assisting patients there to efficiently use medical resources in the hospital [6]. In addition, location-aware advertising usually delivers location-specific coupons or discount information to customers based on their locations and interests [7]. However, indoor environments are very complicated such that there usually exist many obstacles, such as walls, furniture, human beings, and consequently fluctuations of wireless signals because of multipath effects. These obstacles and the signal fluctuations at different scales can cause significant degradation in the accuracy of indoor positioning, which limits the usefulness and degree of comfortableness for providing practical LBS services.

References

References is not available for this document.