Design and implementation of a real time locating system utilizing Wi-Fi signals from iPhones

Real time locating system (RTLS) becomes increasingly popular due to its wide application and fast growth as indicated by the number of mobile devices. Instead of the conventional received signal strength indication (RSSI) fingerprinting method that performs wireless localization by collecting the r...

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Bibliographic Details
Main Authors: Bharanidharan, Muthusubramanian, Li, Xue Jun, Jin, Yunye, Pathmasuntharam, Jaya Shankar, Xiao, Gaoxi
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/102002
http://hdl.handle.net/10220/16347
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Institution: Nanyang Technological University
Language: English
Description
Summary:Real time locating system (RTLS) becomes increasingly popular due to its wide application and fast growth as indicated by the number of mobile devices. Instead of the conventional received signal strength indication (RSSI) fingerprinting method that performs wireless localization by collecting the received signal strength (RSS) for beacons from Wi-Fi access points (APs), in this paper we studied and implemented an alternative approach of utilizing APs in monitoring mode to collect the RSSIs for Wi-Fi signals from a mobile device. We focused on two parameters, location of the mobile users with the RSSI of the incoming packets to Wi-Fi networks, to a RTLS in indoor environment. Wi-Fi networks are chosen due to their well-known features like cost effectiveness and existing infrastructure in all domains as in industries, hospitals and various educations institutions. We implemented the packet capture library, through which we obtained the received signal strength value of the incoming packets sent from the mobile station at the wireless access points. By applying a localization algorithm similar to the well-know k-nearest neighbor (kNN), we have calculated the location coordinates of the user. Experimental results show that this approach can achieve better localization accuracy than the conventional approach.