A Lightweight Deep Learning Algorithm for WiFi-Based Identity Recognition

Yangjie Cao, Zhiyi Zhou, Chenxi Zhu, Pengsong Duan, Xianfu Chen, Jie Li

Research output: Contribution to journalArticleScientificpeer-review

Abstract

WiFi-based identity recognition is predominant because of its noninvasive and ubiquitous advantages. However, existing approaches show slow training speed and limited applicability. In this paper, we propose a lightweight deep learning model, named as LightWeight WiFi-based Identification (LW-WiID), to address these technical challenges. LW-WiID reconstructs original data of channel state information into frequency energy graph, which contains not only the temporal feature of the gait but also the spatial feature among subcarriers, ensuring the accuracy of identity recognition. Furthermore, a novel Balloon mechanism is designed to achieve the lightweight. Through information integration crossing both layers and channels, the Balloon mechanism effectively reduces the number of model parameters. Experimental results demonstrate that LW-WiID achieves an accuracy of 99.7% on a 50-person gait dataset while the model size is compressed to 5.53% of the existing identity recognition approaches with the same accuracy.

Original languageEnglish
Number of pages11
JournalIEEE Internet of Things Journal
DOIs
Publication statusE-pub ahead of print - 10 May 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Band-pass filters
  • channel state information
  • Deep learning
  • deep learning.
  • Feature extraction
  • Filtering
  • Identity recognition
  • Internet of Things
  • lightweight
  • Support vector machines
  • Wireless fidelity

Fingerprint Dive into the research topics of 'A Lightweight Deep Learning Algorithm for WiFi-Based Identity Recognition'. Together they form a unique fingerprint.

Cite this