期刊
JOURNAL OF SUPERCOMPUTING
卷 76, 期 11, 页码 8432-8449出版社
SPRINGER
DOI: 10.1007/s11227-019-03110-2
关键词
Device-free localization; Internet of things; Image; Classification; Convolutional neural network; Location information service; Indoor
类别
资金
- JSPS, Japan [18H03240, 18K11298]
- National Natural Science Foundation of China [61822202, 61872089]
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.
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