@inproceedings{24d6d85297d949e8890d72075e7646e5,
title = "CrowdFi: A Communication Efficient Multi-device Wi-Fi Sensing System",
abstract = "In this paper, we propose a novel multi-device wireless sensing system, called CrowdFi, to balance the sensing performance and the transmission cost. In the CrowdFi, from the perspectives of devices, data, and bits, we propose the adaptive priority based transmission scheme for the heterogeneous data importance and time-varying channel of each device. Moreover, we design a two-stage training procedure and the loss functions to achieve a good tradeoff between the sensing accuracy and the transmission delay. We develop a prototype of the CrowdFi, and validate its performance by employing gait recognition as the application case. Experimental results demonstrate that the proposed CrowdFi system can reduce the transmission delay by , while achieving the comparable or even improved recognition accuracy.",
keywords = "channel state information, deep learning, multi-device wireless sensing",
author = "Shoujie Lei and Zhuo Sun and Zhiwen Yu and Zhu Wang and Bin Guo",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 25th International Conference on Mobile Human-Computer Interaction, MobileHCI 2023 Companion ; Conference date: 26-09-2023 Through 29-09-2023",
year = "2023",
month = sep,
day = "26",
doi = "10.1145/3565066.3608697",
language = "英语",
series = "Proceedings of the 25th International Conference on Mobile Human-Computer Interaction, MobileHCI 2023 Companion",
publisher = "Association for Computing Machinery, Inc",
editor = "Andreas Komninos and Carmen Santoro and Damianos Gavalas and Johannes Schoening and Maristella Matera and Leiva, \{Luis A.\}",
booktitle = "Proceedings of the 25th International Conference on Mobile Human-Computer Interaction, MobileHCI 2023 Companion",
}