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International Conference on Communications and Networking in China

ChinaCom 2020: Communications and Networking pp 200–211Cite as

A DNN-based WiFi-RSSI Indoor Localization Method in IoT

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 352)

Abstract

Indoor automatic localization technology is very important for the Internet of Things. With the development of wireless technology and the diversification of location service requirements, especially in complex indoor scenarios, users are increasingly demanding location-based services. Traditional Global Positioning System (GPS) location technology is difficult to solve some positioning problems in indoor environments, and WiFi is now available in most indoor environments. Therefore, using WiFi for positioning does not require additional deployment of hardware devices, which is a very cost-effective method. However, WiFi-based indoor positioning requires a large amount of data, so we can use artificial intelligence methods to analyze the data and obtain a positioning model. The traditional indoor positioning methods based on WiFi signals have some problems such as long positioning time and poor accuracy. In order to solve the above problems, this paper proposes an indoor localization method based on Deep Neural Networks (DNN) for WiFi fingerprint. In particular, a DNN-based WiFi-RSSI positioning method is proposed for indoor automatic localization. Besides, in the process of DNN training, a joint training method based on unsupervised learning and supervised learning is adopted and the special loss function is defined. Extensive experiments are carried out in both the UJIIndoorLoc public database and a real scenario, and a thorough comparison with several existing approaches indicates that the proposed scheme improves the localization accuracy on average.

Keywords

  • Indoor localization
  • Deep neural networks
  • WiFi-RSSI

Thanks to the National Natural Science Foundation of China (Grants No. 41761086 and 41871363,), the Natural Science Foundation of Inner Mongolia Autonomous Region of China (Grant No. 2017JQ09), and the Grassland Elite Project of the Inner Mongolia Autonomous Region (Grant No. CYYC5016).

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Author information

Authors and Affiliations

  1. Inner Mongolia University, Hohhot, 010021, China

    Bing Jia, Zhaopeng Zong & Baoqi Huang

  2. SLiverpool John Moores University, Liverpool, UK

    Thar Baker

Authors
  1. Bing Jia
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  2. Zhaopeng Zong
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  3. Baoqi Huang
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  4. Thar Baker
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Corresponding author

Correspondence to Baoqi Huang .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Tsinghua University, Beijing, China

    Pingyi Fan

  3. Fudan University, Shanghai, China

    Jun Wun

  4. Tongji University, Shanghai, China

    Xue Xiaoping

  5. Hangzhou Dianzi University, Hangzhou, China

    Prof. Jun Yu

  6. Huawei Technologies Co Ltd, Shanghai, China

    Dr. Yi Wang

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Jia, B., Zong, Z., Huang, B., Baker, T. (2021). A DNN-based WiFi-RSSI Indoor Localization Method in IoT. In: Gao, H., Fan, P., Wun, J., Xiaoping, X., Yu, J., Wang, Y. (eds) Communications and Networking. ChinaCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-67720-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-67720-6_14

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