Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information Visualization
In this paper, we propose a convolutional neural network (CNN) model for device-free fingerprinting indoor localization based on Wi-Fi channel state information (CSI). Besides, we develop an interpretation framework to understand the representations learned by the model. By quantifying and visualizi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8915770/ |
| _version_ | 1819295735901847552 |
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| author | Kevin M. Chen Ronald Y. Chang Shing-Jiuan Liu |
| author_facet | Kevin M. Chen Ronald Y. Chang Shing-Jiuan Liu |
| author_sort | Kevin M. Chen |
| collection | DOAJ |
| description | In this paper, we propose a convolutional neural network (CNN) model for device-free fingerprinting indoor localization based on Wi-Fi channel state information (CSI). Besides, we develop an interpretation framework to understand the representations learned by the model. By quantifying and visualizing CNN in comparison with the fully-connected feedforward deep neural network (DNN) (or multilayer perceptron), we observe that each model can automatically identify location-specific patterns, which are however different across models and are linked to the respective performance of each model. Furthermore, we quantify how features, relevant or otherwise, as deemed by the adopted quantifying metrics (i.e., relevance scores, calculated by relevance propagation techniques), determine or affect the performance results. Interpretation of learning models for wireless applications is challenging due to the lack of human sensory intuition and reference. The results presented in this paper provide visually perceivable evidence and plausible explanations for the performance advantages of CNN in this important application. |
| first_indexed | 2024-12-24T04:46:57Z |
| format | Article |
| id | doaj.art-758ec246dd7f4b758c3cae967c4cf0a1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:46:57Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-758ec246dd7f4b758c3cae967c4cf0a12022-12-21T17:14:40ZengIEEEIEEE Access2169-35362019-01-01717215617216610.1109/ACCESS.2019.29561878915770Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information VisualizationKevin M. Chen0https://orcid.org/0000-0002-6890-2233Ronald Y. Chang1https://orcid.org/0000-0003-4620-6824Shing-Jiuan Liu2https://orcid.org/0000-0002-6530-000XResearch Center for Information Technology Innovation, Academia Sinica, Taipei, TaiwanResearch Center for Information Technology Innovation, Academia Sinica, Taipei, TaiwanDepartment of Electrical and Computer Engineering, University of California at Davis, Davis, CA, USAIn this paper, we propose a convolutional neural network (CNN) model for device-free fingerprinting indoor localization based on Wi-Fi channel state information (CSI). Besides, we develop an interpretation framework to understand the representations learned by the model. By quantifying and visualizing CNN in comparison with the fully-connected feedforward deep neural network (DNN) (or multilayer perceptron), we observe that each model can automatically identify location-specific patterns, which are however different across models and are linked to the respective performance of each model. Furthermore, we quantify how features, relevant or otherwise, as deemed by the adopted quantifying metrics (i.e., relevance scores, calculated by relevance propagation techniques), determine or affect the performance results. Interpretation of learning models for wireless applications is challenging due to the lack of human sensory intuition and reference. The results presented in this paper provide visually perceivable evidence and plausible explanations for the performance advantages of CNN in this important application.https://ieeexplore.ieee.org/document/8915770/Wireless indoor localizationconvolutional neural networks (CNN)fingerprintingWi-Fichannel state information (CSI)Internet of Things (IoT) |
| spellingShingle | Kevin M. Chen Ronald Y. Chang Shing-Jiuan Liu Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information Visualization IEEE Access Wireless indoor localization convolutional neural networks (CNN) fingerprinting Wi-Fi channel state information (CSI) Internet of Things (IoT) |
| title | Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information Visualization |
| title_full | Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information Visualization |
| title_fullStr | Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information Visualization |
| title_full_unstemmed | Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information Visualization |
| title_short | Interpreting Convolutional Neural Networks for Device-Free Wi-Fi Fingerprinting Indoor Localization via Information Visualization |
| title_sort | interpreting convolutional neural networks for device free wi fi fingerprinting indoor localization via information visualization |
| topic | Wireless indoor localization convolutional neural networks (CNN) fingerprinting Wi-Fi channel state information (CSI) Internet of Things (IoT) |
| url | https://ieeexplore.ieee.org/document/8915770/ |
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