Respiration rate is an essential vital indicator for health monitoring. While traditional sensor-based methods support acceptable sensing performance, the recent advance in wireless sensing could enable sensor-free and contact-free respiration sensing, which is particularly important during the practice of social distancing against a pandemic like COVID-19. Among a variety of wireless technologies employed for respiration sensing, Wi-Fi-based solutions are most popular due to the pervasive development of infrastructure. However, the existing Wi-Fi-based approaches need to retrieve Wi-Fi readings from access points, which are not often accessible for the end users. In this article, we propose a novel system, MoBreath, in which we utilize the Wi-Fi channel state information (CSI) readings extracted from the end-user device, a smartphone, to monitor the respiration rate for the first time. We introduce and address unique technical challenges, such as selecting the optimum CSI subcarriers from many noisy candidates and providing smartphone placement strategies for both single and multiple human target scenarios based on the Fresnel zone model to support highly accurate respiration sensing. Our evaluation of MoBreath using commodity smartphones in different environments shows that it can accurately estimate the respiration rate at a low error rate of 0.34 breaths per minute and support the sensing range of up to 3–4 m. Even for challenging scenarios such as the target is covered by a quilt and multiple targets are in the sensing area, MoBreath can still support highly accurate results.

The prevalence of illumination equipment and the inherent advantages of the Visible Light Communication (VLC) technique have resulted in a growing interest in Visible Light Positioning (VLP). There exist many excellent VLP techniques over the past several years. However, one limitation of most VLP survey works is that they mainly focus on the analysis from the perspective of techniques but ignore the equally important hardware aspect, since the hardware part directly affects the performance and cost of VLP systems and also determines whether it can be put into practical use. Different from most surveys concentrating on a single perspective, we provide an intensive overview of VLP systems from software algorithms to hardware devices. A novel‐innovative classification method is used in the software algorithms, while the hardware aspect is introduced in terms of transmitters, modems, and receivers, making up for the deficiencies of the previous works. Massive papers including pioneering papers and the state‐of‐the‐art ones in related areas are gathered and categorized. These solutions have also been evaluated in terms of accuracy, cost, range, and complexity. Furthermore, current open issues and tendencies regarding VLP are also illustrated in this paper.

Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

Security has always been a hot issue of concern. Most of today's home security systems are based on cameras or sensors. These systems have challenges, such as involving in users privacy and high initial cost, so they are not highly popular. In order to resolve these challenges, a single commodity Wi-Fi device is utilized to design a device-free intelligent alarm system. The proposed system relies on the cooperation of active and passive alarm modules so that the user is not required to wear any sensor. In the active alarm module, a novel algorithm is proposed to identify the user's tiny help-seeking action (e.g.special breath) using channel state information (CSI). Moreover, the passive alarm module employs the CSI-based prospect detection to identify the physical attack behavior. The proposed system uses the invisible Wi-Fi signal to identify the people's motion. Then, it uses the results of the activity recognition to identify the dangerous event, which has the characteristic of the concealment. a large number of experiments is carried out in the present study to investigate the accuracy and robustness of the proposed system. It is found that the average accuracy of the system identification is over 95%.

This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.