Survey on the Indoor Localization Technique of Wi-Fi Access Points

Survey on the Indoor Localization Technique of Wi-Fi Access Points

Yimin Liu (Zhengzhou Science and Technology Institute, China), Wenyan Liu (Zhengzhou Science and Technology Institute, China) and Xiangyang Luo (Zhengzhou Science and Technology Institute, China)
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJDCF.2018070103


This article describes how indoor localization of Wi-Fi AP (access point) plays an important role in the discovery of illegal indoor Wi-Fi and for the safety inspection of confidential places. There have been many related research results in recent years. In this article, a review is presented on the indoor localization technique of Wi-Fi AP. First, indoor localization methods of Wi-Fi AP can be divided into three categories: localization based on signal strength; fingerprint feature; and distance measurement. Then, the basic principles of the three methods are described respectively, and an evaluation of the typical methods are provided. Finally, the authors point out some research tendency of the indoor localization techniques of Wi-Fi AP.
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1. Introduction

In recent years, the popularity of Wi-Fi continues to improve, which has brought great convenience to people's daily life, but also provides the possibility for hackers to set up illegal AP and trick users into accessing. Besides, Wi-Fi signals are prone to privacy leaks. For example, lawless people can set a Wi-Fi without a password. In this case, all computers equipped with a wireless card are likely to be automatically connected. This can lead to lots of safety problems, such as information interception of PIN numbers or bank account. In addition, in many special departments that prohibit the coverage of wireless signals there still have private Wi-Fi. The study of indoor localization techniques of Wi-Fi AP is of great significance for safety inspectors to discover illegal Wi-Fi AP in security work.

There are two kinds of researches of indoor equipment localization related to Wi-Fi access point. One is to locate wireless terminal based on the Wi-Fi signal, and the other is to locate the indoor Wi-Fi AP. In the former aspect, the earlier proposed localization algorithms are mainly based on AOA (Angle of Arrival) (Gu, 2009; Niculescu et al., 2003; Brida, 2009), TOA (Time of Arrival) (Patwari et al., 2003; Thomas, 2000), TDOA (Time Difference of Arrival) (Reza, 2000; Gezici et al., 2005; Ma, 2003), RSSI (Received Signal Strength Indication) distance measurement (Madhan, 2014; Ash et al., 2004; Bshara et al., 2010; Park et al., 2011), etc. Later some scholars improve the above methods. Bahl et al. (2000) present RADAR (radio-frequency based system) algorithm to combine signal strength with the signal propagation model. The experimental results show that the localization accuracy is about 3 to 5 meters. Roberto Battiti et al. (2005, 2002) put forward a localization method based on Wi-Fi fingerprint, and the neural network model is used to locate the target. The localization accuracy is about 3 meters. The RTLS (Real Time Locate System) (Veen et al., 1988; Yong et al., 2010) of Ekahau periodically collects signal strength information by positioning tags, and uses the signal attenuation formula to locate target AP on the accuracy of 1 meters. In the latter aspect, there are two ways to detect Wi-Fi signal - outdoor and indoor. The main flow of detecting indoor Wi-Fi signal from outdoor is to set detectors around the building where the Wi-Fi AP is erected. Then, the coordinates of the detectors and the corresponding Wi-Fi signal is simultaneously collected to locate the location of Wi-Fi AP. Le T M et al. (2012) presents a distance-based localization algorithm to obtain the location of the AP utilizing the location of the detectors and the measured angles. Subramanian A P et al. (2008) exploit a DrivebyLoc algorithm regarding the angle of the strongest signal as the direction of the target AP, whose average error is about 50 meters. Chen et al. (2015) proposed the PDAPL (Probability Density algorithm for Access Point Localization) based on probability density, which obtains an increase (30 meters) in localization accuracy and reduces the number of required detectors to half of the DrivebyLoc algorithm.

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