The WiMap: A Dynamic Indoor WLAN Localization System

The WiMap: A Dynamic Indoor WLAN Localization System

Junjun Xu (Beijing University of Posts and Telecommunications, China), Haiyong Luo (Institute of Computing Technology Chinese Academy of Sciences, China), Fang Zhao (Beijing University of Posts and Telecommunications, China), Rui Tao (Beijing University of Posts and Telecommunications, China), Yiming Lin (Institute of Computing Technology Chinese Academy of Sciences, China) and Hui Li (Institute of Computing Technology Chinese Academy of Sciences, China)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/978-1-4666-2645-4.ch004
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

As positioning technology is an important foundation of the Internet of Things, a dynamic indoor WLAN localization system is proposed in this paper. This paper mainly concentrates on the design and implementation of the WiMap-a dynamic indoor WLAN localization system, which employs grid-based localization method using RSS (received signal strength). To achieve high localization accuracy and low computational complexity, Gaussian mixture model is applied to approximate the signal distribution and a ROI (region of interest) is defined to limit the search region. The authors also discuss other techniques like AP selection and threshold control, which affects the localization accuracy. The experimental results indicate that an accuracy of 3m with 73.8% probability can be obtained in WiMap. Moreover, the running time is reduced greatly with limited ROI method.
Chapter Preview
Top

WLAN localization, based on whether using the trained dataset, can mainly be categorized into parameter-estimation-based technique or pattern-mapping technique. Parameter-estimation-based technique usually considers severe multipath and shadowing conditions and nonline-of-sight propagation caused by the presence of walls, humans and other objects in indoors (Kushi, Plataniotis, & Venetsanopoulos, 2010), trying to give the estimation through geometrically or statistically calculating distance, angle and connection information between unknown nodes and anchor points (nodes with known location). One such model is Goldsmith (2005):

Complete Chapter List

Search this Book:
Reset