Paper Info
Title
A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting
Abstract
With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSlndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2933921
IEEE ACCESS
Keywords
DocType
Volume
Indoor localization,deep learning,convolutional neural network,WiFi fingerprinting
Journal
7
ISSN
Citations 
PageRank 
2169-3536
1
0.35
References 
Authors
0
9
Name
Order
Citations
PageRank
Xudong Song120.70
Xiaochen Fan2403.74
Chaocan Xiang34910.76
Qianwen Ye451.45
Leyu Liu510.35
Zumin Wang6162.98
Xiangjian He7932132.03
Ning Yang8191.91
Gengfa Fang912824.24