Paper Info
Title
WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach
Abstract
Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.
Year
DOI
Venue
2022
10.3390/s22145236
SENSORS
Keywords
DocType
Volume
indoor positioning system, machine learning, WLAN fingerprinting, higher education, learning environment
Journal
22
Issue
ISSN
Citations 
14
1424-8220
0
PageRank 
References 
Authors
0.34
21
6
Name
Order
Citations
PageRank
Sohaib Bin Altaf Khattak110.69
Fawad200.34
Moustafa M Nasralla300.34
Maged Abdullah Esmail400.34
Hala Mostafa55011.88
Min Jia615839.37