Sensing Our World Using Wireless Signals

Chen, Xiaojiang; Chen, Lili; Feng, Chao; Fang, Dingyi; Xiong, Jie; Wang, Zheng; Cui, Yong

doi:10.1109/mic.2019.2921640

Cited by 23 publications

(7 citation statements)

References 17 publications

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“…Radio sensing has attracted great research interest recently [1], [2]. Traditional sensing often requires dedicated sensing devices or signals, such as cameras and tags, to perform the sensing tasks [3].…”

Section: A Radio Sensing Conceptmentioning

confidence: 99%

Radio Sensing Using 5G Signals: Concepts, State of the Art, and Challenges

Chen

Zhang

Feng

et al. 2022

IEEE Internet Things J.

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Section: A Radio Sensing Conceptmentioning

confidence: 99%

Radio Sensing Using 5G Signals: Concepts, State of the Art, and Challenges

Chen

Zhang

Feng

et al. 2022

IEEE Internet Things J.

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“…Among these, RSSI (which is often confused with RSS) is the RSS indicator, a relative measurement of the RSS that has arbitrary units and is mostly defined by each chip vendor [27]. CSI is not easily accessible with most commodity hardware [28]. Compared to other commonly used measurement methods (TOA, TDOA, and AOA), the RSSI measurement method does not require time synchronization or the employment of an antenna array, which makes it more effective and cost-efficient for mobile phone localization in the view of both software and hardware [29].…”

Section: Rssi-based Wi-fi Positioningmentioning

confidence: 99%

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Lin

Lyu

et al. 2022

Buildings

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With the increasing innovation and development of Wi-Fi technology, its penetration in the various fields of industry and academia is becoming more and more profound. As the core infrastructure of traffic data collection in the field of Intelligent Transportation Systems (ITS), Wi-Fi-based traffic detectors have great potential for use in traffic target positioning, perception, and pattern recognition due to their low-cost and extensive infrastructure deployment. This paper conducts a comprehensive review of three major Wi-Fi-based traffic detection applications in the field of ITS: target positioning, traffic parameter extraction, and travel mode identification. Among these, target positioning is one of the most widespread applications of Wi-Fi technology, which is also the basis for two other research aspects. Moreover, Wi-Fi-based positioning can be divided into two categories: ranging-based positioning and range-free one; in the field of transportation, it can also be categorized into pedestrian positioning and vehicle positioning based on travel mode. To further demonstrate the effectiveness of Wi-Fi-based ITS applications in practice, this study compares with the various Wi-Fi-involved models and algorithms around the world, as well as provides some ideas and inspiration along with this direction.

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“…With the rapid development of society and the rapid iteration of electronic devices, people's demand for quality of life is becoming higher, and numerous kinds of new services and demands have emerged. Location-Based Service (LBS) technology is an important support for these services, and the number of production and life activities based on LBS has increased rapidly [1]. How to accurately obtain the location of the target over a long term is the fundamental task of LBS-based services.…”

Section: Introductionmentioning

confidence: 99%

Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

Xue,

Chen,

Chi

et al. 2024

Applied Sciences

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With the widespread use of WiFi devices and the availability of channel state information (CSI), CSI-based device-free localization (DFL) has attracted lots of attention. Fingerprint-based localization methods are the primary solutions for DFL, but they are faced with the fingerprint similarity problem due to the complex environment and low bandwidth of the commercial WiFi. Meanwhile, fingerprints may change unpredictably due to multipath WiFi signal propagation in time-varying environments. To tackle these problems, this paper proposes an adaptive online learning DFL method, which adaptively updates the localization model to ensure long-term accuracy and adaptability. Specifically, the CSI signals of the target located at different reference points are first collected and transformed to discriminable fingerprints using the weights of Multilayer Online Sequence Extreme Learning Machine (ML-OSELM). After that, an online learning DFL model is built to adapt to the changes of the environment. Experimental results in a time-varying indoor environment validate the adaptability of the proposed method against environmental changes and show that our method can achieve 10% improvement over other methods.

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Sensing Our World Using Wireless Signals

Cited by 23 publications

References 17 publications

Radio Sensing Using 5G Signals: Concepts, State of the Art, and Challenges

Radio Sensing Using 5G Signals: Concepts, State of the Art, and Challenges

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

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