CSI-based human sensing using model-based approaches: a survey

Abstract Currently, human sensing draws much attention in the field of ubiquitous computing, and human sensing based on WiFi CSI (channel state information) becomes a hot research topic due to the easy deployment and availability of WiFi devices. Although various human sensing applications based on the CSI signal model are emerging, the model-based approach has not been studied thoroughly. This paper provides a comprehensive survey of the latest model-based human sensing methods and their applications. First, the CSI signal and framework of model-based human sensing methods are introduced. Then, related models and fundamental signal preprocessing techniques are described. Next, typical human sensing applications are investigated, and the crucial characteristics are summarized. Finally, the advantages, limitations, and future research trends of model-based human sensing methods are concluded in this paper.

Download Full-text

Multi-functionalization Strategies Using Nanomaterials: A Review and Case Study in Sensing Applications

International Journal of Precision Engineering and Manufacturing-Green Technology ◽

10.1007/s40684-021-00356-1 ◽

2021 ◽

Author(s):

Ji-Hyeon Song ◽

Soo-Hong Min ◽

Seung-Gi Kim ◽

Younggyun Cho ◽

Sung-Hoon Ahn

Keyword(s):

Solar Cells ◽

Scale Effects ◽

Research Trends ◽

Future Research ◽

Functional Applications ◽

Energy Harvesters ◽

Sensing Applications

AbstractRemarkable advances in nanomaterials and nanotechnology have led researchers in various fields. The scale effects imparted by nanomaterials are associated with unexpected macroscale phenomena and properties that find many applications. However, multi-functionalization may be accompanied by physical and commercial limitations. Therefore, research must proceed in several different directions. Here, we define multi-functionalization and the electrical applications thereof in terms of increasing performance, addition of new and valuable properties, and multi-physics in play. We deal with sensors, actuators, energy harvesters, and solar cells and explore research that seeks to increase sensitivity, append “stretchability”, and facilitate untethered communication. Furthermore, we analyze research trends in materials use and manufacturing, and highlight useful fabrication methods. With the aim of predicting future research trends, our review presents a roadmap that will aid research on sensing and multi-functional applications.

Download Full-text

Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information

Sensors ◽

10.3390/s19214783 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4783

Author(s):

Gao ◽

Zhang ◽

Xiao ◽

Keyword(s):

Channel State Information ◽

Indoor Localization ◽

Probabilistic Approach ◽

Indoor Environments ◽

Channel State ◽

State Information ◽

Localization Accuracy ◽

Sensing Applications ◽

Leibler Divergence ◽

Device Free

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.

Download Full-text