Abstract
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.
Highlights
Human behavior sensing has drawn considerable concern and achieved important research progress in the field of ubiquitous computing
This paper investigates and presents a comprehensive survey of the latest user authentication applications based on CSI variation
User authentication technology based on the WiFi CSI signal brings great convenience due to its nonintrusive characteristics and wide availability of the WiFi signal
Summary
Human behavior sensing has drawn considerable concern and achieved important research progress in the field of ubiquitous computing. With these behavior recognition applications, we can acquire users’ behavior states and infer their daily movement regularity, which provides us with an effective means to understand users’ actions. Human behavior recognition applications can employ many types of signals, including vision [3,4,5], sound [6,7,8,9], light [10, 11], and RF (radio frequency) signals. This paper focuses on human behavior with the WiFi signal. Many studies have confirmed the feasibility of utilizing the WiFi signal to realize behavior sensing and have achieved many attractive research results. Some surveys have summarized state-of-the-art studies and presented insight into further research trends [24,25,26]
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