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

Bing Jia, Zhaopeng Zong, Baoqi Huang, Thar Baker

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review

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.

Original languageEnglish
Title of host publicationCommunications and Networking - 15th EAI International Conference, ChinaCom 2020, Proceedings
EditorsHonghao Gao, Pingyi Fan, Jun Wun, Xue Xiaoping, Jun Yu, Yi Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages200-211
Number of pages12
ISBN (Print)9783030677190
DOIs
Publication statusPublished - 2 Feb 2021
Event15th EAI International Conference on Communications and Networking, ChinaCom 2020 - Shanghai, China
Duration: 20 Nov 202021 Nov 2020

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume352
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference15th EAI International Conference on Communications and Networking, ChinaCom 2020
Country/TerritoryChina
CityShanghai
Period20/11/2021/11/20

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Keywords

  • Deep neural networks
  • Indoor localization
  • WiFi-RSSI

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