With the evolution of wireless sensing technology, WiFi-based human identification has demonstrated tremendous potential in human-computer interaction and home security. However, most existing research operates in controlled environments, overlooking the complexities of real-world scenarios, such as signal fading, signal interference and uncertainty, and the diversity of application requirements. This article presents a comprehensive analysis of a series of representative research articles on WiFi-based human identification and summarizes five major challenges in achieving high-precision identification in complex application scenarios. Non-line-of-sight (NLOS) user sensing, coexistence user sensing, dynamic group user sensing, cross-domain user sensing, and multi-task sensing. Additionally, this article proposes a series of current solutions, including improving the signal-to-noise ratio (SNR) of NLOS sensing from the perspective of communication signals and sensing models, addressing coexistence sensing issues, designing adaptable tasks for dynamic user groups, leveraging techniques like adversarial learning and transfer learning for cross-domain problems, and employing modular deep learning architectures. By providing a comprehensive overview of WiFi-based human identification, this survey not only offers insights into current research but also charts a roadmap for future investigations. It is anticipated that this survey will stimulate innovative research endeavors and foster the expansion of wireless sensing technology across diverse application domains.

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.

The named entity recognition based on the epidemiological investigation of information on COVID-19 can help analyze the source and route of transmission of the epidemic to control the spread of the epidemic better. Therefore, this paper proposes a Chinese named entity recognition model BERT-BiLSTM-IDCNN-ELU-CRF (BBIEC) based on the epidemiological investigation of information on COVID-19 of the BERT pre-training model. The model first processes the unlabeled epidemiological investigation of information on COVID-19 into the character-level corpus and annotates it with artificial entities according to the BIOES character-level labeling system and then uses the BERT pre-training model to obtain the word vector with position information; then, through the bidirectional long-short term memory neural network (BiLSTM) and the improved iterated dilated convolutional neural network (IDCNN) extract global context and local features from the generated word vectors and concatenate them serially; output all possible label sequences to the conditional random field (CRF); finally pass the condition random The airport decodes and generates the entity tag sequence. The experimental results show that the model is better than other traditional models in recognizing the entity of the epidemiological investigation of information on COVID-19.

With the increasing demand and expanding scale of laboratories in colleges and universities, laboratory accidents frequently occur, seriously impacting personal health, schools, society, and the environment. Scientific and effective risk assessment is crucial to prevent accidents. Therefore, in order to effectively reduce the risk of chemical laboratories and minimize the frequency of accidents. This study employs the combination of the SHELL model and HACCP system to analyze the potential sources of hazards in hazardous chemical laboratories and establish a risk assessment index system. Based on the Cloud model, a dynamic risk assessment model for the laboratory is established to quantitatively evaluate the risk level of the evaluation results. In order to ensure the rationality of the assessment results, the subjective and objective weights are combined by the principle of minimizing information entropy. Case analysis proves the scientific validity of the evaluation results of the model, which can assist laboratory managers in formulating emergency plans and risk management mechanisms to reduce or eliminate the occurrence of experimental accidents. This approach ensures the safe and sustainable development of schools and laboratories, which is conducive to the progress of researchers’ scientific research results.

With the proliferation of video surveillance system deployment and related applications, real-time video analysis is very critical to achieving intelligent monitoring, autonomous driving, etc. Analyzing video stream with high accuracy and low latency through the traditional cloud computing represents a non-trivial problem. In this paper, we propose a non-orthogonal multiple access (NOMA)-based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the quality of experience (QoE) of the UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimensionality, which, respectively, select the offloading and resource allocation action, as well as the resolution scaling action. The experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of edge computing and has better performance in terms of convergence compared to other baseline schemes.