Abstract
With the rapid development of the Internet of Things (IoT), the demands for location-based services (LBS) are increasing day by day. At present, most of the indoor localization technologies require targets to carry terminal devices, which limits the practical application of indoor localization. In this paper, we propose a deep learning-based device-free localization system using ZigBee. The system employs ZigBee nodes as sensor nodes, which can locate the targets through measuring received signal strength (RSS) among these sensor nodes. In the off-line phase, we collect the RSS data of some specific locations and construct a localization model through training a deep learning convolutional neural network (CNN) model. In the on-line phase, we are able to calculate target location coordinates with the trained CNN model. The experimental result shows that the mean error of the proposed deep learning-based device-free localization system is 0.53 m, which could be a technical solution for human target localization in indoor environments.
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References
Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J. 1(1):22–32
Sun YL, Meng WX, Li C, Zhao N, Zhao KL, Zhang NT (2017) Human localization using multi-source heterogeneous data in indoor environments. IEEE Access 5:812–822
Sun YL, Wang XC, Zhang XZ, Zhang XG (2018) ZigBee-based device-free wireless localization in internet of things. 2018 In: 3nd EAI international conference on machine learning and intelligent communications, pp 1–8
Sun YL, Zhang XZ, Wang XC, Zhang XG (2018) Device-free wireless localization using artificial neural networks in wireless sensor networks. Wireless Commun Mobile Comput 2018:1–8
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-Based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 61701223 and the Natural Science Foundation of Jiangsu Province under Grant No. BK20171023.
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Sun, Y., Wang, X., Zhang, X. (2020). Deep Learning-Based Device-Free Localization Using ZigBee. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_247
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DOI: https://doi.org/10.1007/978-981-13-9409-6_247
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