Smartphones-Based Crowdsourcing Approach for Installing Indoor Wi-Fi Access Points

Abstract

This study provides a new Crowdsourcing-based approach to identify the most crowded places in an indoor environment. The Crowdsourcing Indoor Localization system (CSI) has been one of the most used techniques in locationbased applications. However, many applications suffer from the inability to locate the most crowded locations for various purposes such as advertising. These applications usually need to perform a survey before identifying target places, which require additional cost and time consuming. For example, Access Points (APs) installation can rely on an automated system to identify the best places where these APs should be placed without the need to use primitive ways to determine the best locations. In this work, we present a new approach for Wi-Fi designers and advertising companies to recognize the proper positions for placing APs and advertisement activities in indoor buildings. The recorded data of the accelerometer sensors are analyzed and processed to detect user's steps and thereby predict the most crowded places in a building. Our experiments show promising results in terms of the most widely used metrics in the subject as the accuracy for detecting users' steps reaches 95.8% and the accuracy for detecting the crowded places is 90.4%.

Smartphone-Based Indoor Localization Systems: A Systematic Literature Review

“…Both Bluetooth and Wi-Fi data offer positions that are subject to some level of noise [86]. However, PDR displacement estimation can be used to improve the real-time heading prediction in PDR, identify Wi-Fi FTM outliers [68], and recognize the appropriate locations for placing APs and making announcements for indoor buildings [62]. Acoustic-based indoor pedestrian tracking (IPT) has been proven superior in localization and direction-finding [102].…”

“…The Wi-Fi fingerprint-based techniques are capable of excellent localization accuracy, but they cannot offer users semantic information about the object of interest [90]. Another issue with Wi-Fi is installing the APs and their locations [2,62]. Despite mobile devices staying stationary in some areas, gaining perfect positions is difficult [59].…”

“…Time consumption affects the application of internal localization, mainly because some applications need to identify the target places, which incurs additional cost and takes time. For instance, AP installation depends on an automated system to discover the optimal sites for the AP, thus eliminating the need to apply conventional methods to determine optimal placements [62]. In addition, providing AP locations, propagation parameters (PPs), and radio maps would require the prior acquisition of Wi-Fi positioning systems, which can be very time-consuming and labor-intensive [79].…”

“…In addition, providing AP locations, propagation parameters (PPs), and radio maps would require the prior acquisition of Wi-Fi positioning systems, which can be very time-consuming and labor-intensive [79]. Instead of depending on crowded regions to decide on the ideal spots for installing them, Wi-Fi access points (WAP) are deployed geographically, which requires more human resources and higher costs [62]. However, the increasing density of fingerprints affects real-time performance, as additional time is required for fingerprint matching and probability distribution positioning [84].…”