Probabilistic Fingerprinting Based Passive Device-Free Localization from Channel State Information

Author(s):  
Shuyu Shi ◽  
Stephan Sigg ◽  
Yusheng Ji
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhengjie Wang ◽  
Wenwen Dou ◽  
Mingjing Ma ◽  
Xiaoxue Feng ◽  
Zehua Huang ◽  
...  

Recently, human behavior sensing based on WiFi channel state information has drawn more attention in the ubiquitous computing field because it can provide accurate information about the target under a device-free scheme. This paper concentrates on user authentication applications using channel state information. We investigate state-of-the-art studies and survey their characteristics. First, we introduce the concept of channel state information and outline the fundamental principle of user authentication. These systems measure the dynamic channel state information profile and implement user authentication by exploring the channel state information variation caused by users because each user generates unique channel state information fluctuations. Second, we elaborate on signal processing approaches, including signal selection and preprocessing, feature extraction, and classification methods. Third, we thoroughly investigate the latest user authentication applications. Specifically, we analyze these applications from typical human action, including gait, activity, gesture, and stillness. Finally, we provide a comprehensive discussion of user authentication and conclude the paper by presenting some open issues, research directions, and possible solutions.


Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 315
Author(s):  
Ramon F. Brena ◽  
Edgar Escudero ◽  
Cesar Vargas-Rosales ◽  
Carlos E. Galvan-Tejada ◽  
David Munoz

Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links.


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