Research and application exploration of WiFi-based identification technology in the context of next-generation communication

Research Article
Open access

Research and application exploration of WiFi-based identification technology in the context of next-generation communication

Wenxu Han 1*
  • 1 College of Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore    
  • *corresponding author HANW0018@e.ntu.edu.sg
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/32/20230189
ACE Vol.32
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-289-3
ISBN (Online): 978-1-83558-290-9

Abstract

With the rapid advancement of wireless networks and the widespread use of WiFi technology, there is a growing interest in utilizing WiFi information for identification purposes. These emerging identification technologies in the new generation have had a profound impact on various aspects of modern life, such as smart furniture research, intelligent security systems, and human-computer interaction. This paper delves into the research and application exploration of WiFi-based identification technology within the context of the new generation. It introduces the knowledge and working principles of Channel State Information (CSI), and discusses the fundamental technologies of Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) in detail. Additionally, it explores current applications and presents a promising future for identification technology from a developmental perspective. By examining the advantages and challenges associated with WiFi-based identification technology, this essay sheds light on its potential impact in various domains. Understanding and exploring this technology are crucial as it has the potential to enhance user experiences, optimize resource allocation, and facilitate intelligent and adaptive systems in the new generation.

Keywords:

channel state information, identify technology, Wi-Fi-sensing

Han,W. (2024). Research and application exploration of WiFi-based identification technology in the context of next-generation communication. Applied and Computational Engineering,32,93-98.
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References

[1]. Hoefel, R. P. F. (2012). IEEE 802.11n: On the Performance of Channel Estimation Schemes over OFDM MIMO Spatially-Correlated Frequency Selective Fading TGn Channels. In Brazilian Symposium on Telecommunications, Brasilia, Brazil.

[2]. Faheem, M., Shah, S. B. H., Butt, R. A., Raza, B., Anwar, M., Ashraf, M. W., ... & Gungor, V. C. (2018). Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges. Computer Science Review, 30, 1-30.

[3]. Bianchi, V., Ciampolini, P., & De Munari, I. (2019). RSSI-Based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes. IEEE Transactions on Instrumentation and Measurement, 68(2), 566-575..

[4]. Lv, J., Man, D., Yang, W., Du, X., & Yu, M. (2017). Robust WLAN-based indoor intrusion detection using PHY layer information. IEEE Access, 6, 30117-30127.

[5]. Wulfmeier, M., Ondruska, P., & Posner, I. (2017). Maximum entropy deep inverse reinforcement learning [EB/OL]. Retrieved November 16.

[6]. Ahmed, T., Ahmad, H. F., & C.V., A. (2020). Device-free human gesture recognition using Wi-Fi CSI: A survey. Engineering Applications of Artificial Intelligence, 87, 103281.

[7]. Shahzad, M., & Zhang, S. (2018). Augmenting user identification with WiFi-based gesture recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3), 1-27.

[8]. Zheng, R. Y. (2018). Research on identification technology based on CSI wireless sensing [Doctoral dissertation, Nanjing University of Aeronautics and Astronautics].

[9]. Kong, H., Lu, L., Yu, J., et al. (2020). Continuous Authentication through Finger Gesture Interaction for Smart Homes Using WiFi. IEEE Transactions on Mobile Computing.

[10]. Li, C., Liu, M., & Cao, Z. (2020). WiHF: Enable User Identified Gesture Recognition with WiFi. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications (pp. 586-595). IEEE.

Cite this article

Han,W. (2024). Research and application exploration of WiFi-based identification technology in the context of next-generation communication. Applied and Computational Engineering,32,93-98.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-289-3(Print) / 978-1-83558-290-9(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.32
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Hoefel, R. P. F. (2012). IEEE 802.11n: On the Performance of Channel Estimation Schemes over OFDM MIMO Spatially-Correlated Frequency Selective Fading TGn Channels. In Brazilian Symposium on Telecommunications, Brasilia, Brazil.

[2]. Faheem, M., Shah, S. B. H., Butt, R. A., Raza, B., Anwar, M., Ashraf, M. W., ... & Gungor, V. C. (2018). Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges. Computer Science Review, 30, 1-30.

[3]. Bianchi, V., Ciampolini, P., & De Munari, I. (2019). RSSI-Based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes. IEEE Transactions on Instrumentation and Measurement, 68(2), 566-575..

[4]. Lv, J., Man, D., Yang, W., Du, X., & Yu, M. (2017). Robust WLAN-based indoor intrusion detection using PHY layer information. IEEE Access, 6, 30117-30127.

[5]. Wulfmeier, M., Ondruska, P., & Posner, I. (2017). Maximum entropy deep inverse reinforcement learning [EB/OL]. Retrieved November 16.

[6]. Ahmed, T., Ahmad, H. F., & C.V., A. (2020). Device-free human gesture recognition using Wi-Fi CSI: A survey. Engineering Applications of Artificial Intelligence, 87, 103281.

[7]. Shahzad, M., & Zhang, S. (2018). Augmenting user identification with WiFi-based gesture recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3), 1-27.

[8]. Zheng, R. Y. (2018). Research on identification technology based on CSI wireless sensing [Doctoral dissertation, Nanjing University of Aeronautics and Astronautics].

[9]. Kong, H., Lu, L., Yu, J., et al. (2020). Continuous Authentication through Finger Gesture Interaction for Smart Homes Using WiFi. IEEE Transactions on Mobile Computing.

[10]. Li, C., Liu, M., & Cao, Z. (2020). WiHF: Enable User Identified Gesture Recognition with WiFi. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications (pp. 586-595). IEEE.

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