Paper Info
Title
Indoor device-free passive localization with DCNN for location-based services
Abstract
With the increasing demand of indoor location-based services, such as tracking targets in a smart building, device-free localization technique has attracted great attentions because it can locate the targets without employing any attached devices. Due to the limited space and complexity of the indoor environment, there still exist challenges in terms of high localization accuracy and high efficiency for indoor localization. In this paper, for addressing such issues, we first convert the received signal strength (RSS) signals into image pixels. The localization problem is then formulated as an image classification problem. To well handle the variant RSS images, a deep convolutional neural network is then structured for classification. Finally, for validating the proposed scheme, two real testbeds are built in the indoor environments, including a living room and a corridor of an apartment. Experimental results show that the proposed scheme achieves good localization performance. For example, the localization accuracy can reach up to 100% in the scenario of living room and 97.6% in the corridor. Moreover, the proposed approach outperforms the methods of the K-nearest-neighbor and the support vector machines in both the noiseless and noisy environments.
Year
DOI
Venue
2020
10.1007/s11227-019-03110-2
The Journal of Supercomputing
Keywords
DocType
Volume
Device-free localization, Internet of things, Image, Classification, Convolutional neural network, Location information service, Indoor
Journal
76
Issue
ISSN
Citations 
11
0920-8542
2
PageRank 
References 
Authors
0.36
0
7
Name
Order
Citations
PageRank
Lingjun Zhao117622.10
Chunhua Su217441.11
Zeyang Dai321.38
Huakun Huang481.80
Shuxue Ding523533.84
Xinyi Huang62245129.63
Zhaoyang Han721.72