Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

Abstract

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

An Adaptive Fingerprint Database Updating Method for Room Localization

“…Some researchers [6]- [17] have demonstrated the feasibility of the fingerprint database updating by crowd-sourcing and thus reduce fingerprint calibration effort. In this participatory sensing way, the crowd-sourcing technique can not only help saving costs, but also enable adaptive database update when environment changes.…”

“…Meanwhile now mobile phones possess powerful computing, communicating, and sensing capability, and act as an increasingly important information interface between humans and environments [6]. Therefore as a new collaborative paradigm that leverages the power of tens of thousands of participants to accomplish one specific task, the adoption of crowd-sourcing technique is a rising trend among the recent efforts in indoor localization [6]- [17]. In these methods, the expert survey is replaced by some individual end-users, and a large amount of newly collected data, distributed at different points, can be naturally gathered into one or multiple stationary hotspots, which offer more robust fingerprints.…”

An efficient crowd-sourcing-based approach for fingerprint database updating

“…With the growing deployment of WLAN in indoor environments and the widespread use of mobile devices such as smart phones, WLAN-based fingerprinting indoor location methods are getting popular due to their low deployment cost and relatively high localization accuracy. 19 Scholars have put forward many alternatives to realize fingerprint database construction and maintenance. Generally, they witness two phases of development: static fingerprint-based techniques and adaptive updating techniques.…”

“…8 Meanwhile, the crowd-sourcing method has been shown to be a promising approach to solving these problems. [8][9][10][11][12][13][14][15][16][17][18][19] These advances lay solid foundations of breakthrough technology for fingerprint database updating in WLANbased location systems. In a crowd-sourcing-based system, each user can contribute to the updating of the fingerprint database; 19 where the expert surveying is replaced by the individual end-user, these findings significantly reduce the recalibration cost.…”

Localization in Low Power Wide Area Networks using Wi-Fi Fingerprints

“…Another consideration in Wi-Fi fingerprinting is the challenge of constructing the fingerprint database. While the traditional approach is to collect the fingerprints during a labor intensive site survey, some researchers have looked into crowd sourcing the database construction [17].…”