Hands-on Wireless Sensing with Wi-Fi: A Tutorial
- URL: http://arxiv.org/abs/2206.09532v1
- Date: Mon, 20 Jun 2022 01:53:35 GMT
- Title: Hands-on Wireless Sensing with Wi-Fi: A Tutorial
- Authors: Zheng Yang, Yi Zhang, Guoxuan Chi, Guidong Zhang
- Abstract summary: This tutorial takes Wi-Fi sensing as an example.
It introduces both the theoretical principles and the code implementation of data collection, signal processing, features extraction, and model design.
- Score: 7.8774878397748065
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the rapid development of wireless communication technology, wireless
access points (AP) and internet of things (IoT) devices have been widely
deployed in our surroundings. Various types of wireless signals (e.g., Wi-Fi,
LoRa, LTE) are filling out our living and working spaces. Previous researches
reveal the fact that radio waves are modulated by the spatial structure during
the propagation process (e.g., reflection, diffraction, and scattering) and
superimposed on the receiver. This observation allows us to reconstruct the
surrounding environment based on received wireless signals, called "wireless
sensing". Wireless sensing is an emerging technology that enables a wide range
of applications, such as gesture recognition for human-computer interaction,
vital signs monitoring for health care, and intrusion detection for security
management. Compared with other sensing paradigms, such as vision-based and
IMU-based sensing, wireless sensing solutions have unique advantages such as
high coverage, pervasiveness, low cost, and robustness under adverse light and
texture scenarios. Besides, wireless sensing solutions are generally
lightweight in terms of both computation overhead and device size. This
tutorial takes Wi-Fi sensing as an example. It introduces both the theoretical
principles and the code implementation of data collection, signal processing,
features extraction, and model design. In addition, this tutorial highlights
state-of-the-art deep learning models (e.g., CNN, RNN, and adversarial learning
models) and their applications in wireless sensing systems. We hope this
tutorial will help people in other research fields to break into wireless
sensing research and learn more about its theories, designs, and implementation
skills, promoting prosperity in the wireless sensing research field.
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