WiFi Sensing with Channel State Information

Ma, Yongsen; Zhou, Gang; Wang, Shuangquan

doi:10.1145/3310194

Cited by 609 publications

(363 citation statements)

References 125 publications

Supporting

Mentioning

353

Contrasting

Unclassified

Order By: Relevance

“…In the emerging 802.11a/g/n standard, channel state information (CSI) can be extracted from off-the-shelf orthogonal frequency division multiplexing (OFDM) receivers, which provide the subcarrier amplitudes and phases [26], [29]. Taking advantage of the high time/frequency resolution and stability of the CSI, the most recent research on CSI-based localization shows an achievable accuracy of 0.38 m [30], [31] with only one multiple input multiple output (MIMO) transmitter and two MIMO receiver nodes.…”

Section: A Rf-based Passive Localizationmentioning

confidence: 99%

Cooperative Passive Pedestrian Detection and Localization Using a Visible Light Communication Access Network

Hosseinianfar

Brandt-Pearce

2020

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

Visible light communication (VLC) systems are promising candidates for future indoor access and peer-to-peer networks. The performance of these systems, however, is vulnerable to line of sight (LOS) link blockage due to objects inside the room. Considering pedestrians as the most common VLC links blocking obstacles, we develop a probabilistic passive pedestrian detection and localization method. Our method takes advantage of the blockage status of VLC LOS links between the user equipment (UE) and transceivers on the ceiling to passively detect a single pedestrian, modeled as a cylinder with a random radius. The VLC network gathers the blockage status and computes the geometry of the LOS link graph through a cooperative scheme between VLC device-equipped users inside the room. We also develop a mathematical framework to obtain an optimum solution for estimating the location and size of the object and conclude with a sub-optimum estimation by simplifying the problem to a quadratic programming approach. Simulation results show that using a 5 × 5 grid of transceivers on the ceiling and as few as eight UEs, the root-mean-squared error in estimating the center and radius of the object can be less than 5 cm and 3 cm, respectively.

show abstract

Section: A Rf-based Passive Localizationmentioning

confidence: 99%

Cooperative Passive Pedestrian Detection and Localization Using a Visible Light Communication Access Network

Hosseinianfar

Brandt-Pearce

2020

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…The recently developed indoor localization techniques are essentially based on radio frequency (RF) technologies (e.g. Wi-Fi [9] [10], Bluetooth [11], radio frequency identification (RFID) [12], ultra wideband (UWB) [13]) and emerging technologies (e.g. visible light [14], ultrasound [15], magnetic field [16]).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Data Recovery for Localization

et al. 2020

View full text Add to dashboard Cite

In this paper, we study the problem of euclidean distance matrix (EDM) recovery aiming to tackle the problem of received signal strength indicator sparsity and fluctuations in indoor environments for localization purposes. This problem is addressed under the constraints required by the internet of things communications ensuring low energy consumption and reduced online complexity compared to classical completion schemes. We propose EDM completion methods based on neural networks that allow an efficient distance recovery and denoising. A trilateration process is then applied to recovered distances to estimate the target's position. The performance of different deep neural networks (DNN) and convolutional neural networks schemes proposed for matrix reconstruction are evaluated in a simulated indoor environment, using a realistic propagation model, and compared with traditional completion method based on the adaptative moment estimation algorithm. Obtained results show the superiority of the proposed DNN based completion systems in terms of localization mean error and online complexity compared to the classical completion.

show abstract

“…This platform can be used for multiple health applications due to flexibility and scalability of the software-defined hardware [ 7 ]. The device-free sensing approach is becoming very popular because patients are not required to carry any device, but instead the wireless channel is tagged with devices to capture the required information [ 8 ]. Wi-Fi-based devices have grown very rapidly because they are easy to deploy and are cost effective.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Khan

Zhang

et al. 2020

Micromachines

View full text Add to dashboard Cite

The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.

show abstract

WiFi Sensing with Channel State Information

Cited by 609 publications

References 125 publications

Cooperative Passive Pedestrian Detection and Localization Using a Visible Light Communication Access Network

Cooperative Passive Pedestrian Detection and Localization Using a Visible Light Communication Access Network

Deep Learning Based Data Recovery for Localization

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Contact Info

Product

Resources

About