As a vital metric of information timeliness, Age of Information (AoI) is important for real-time applications in Internet of Things (IoT), such as health monitoring. To satisfy these requirements, we study a wireless-powered IoT (WPIoT), where a static hybrid access point (HAP) coordinates wireless energy transfer to mobile IoT nodes, and mobile IoT nodes transmit data to the HAP or static IoT nodes. We minimize the AoI of mobile IoT nodes by optimizing the selection of the HAP or static IoT node for transmission, the channel selection, the duration of data transmission, and the transmit power, and prove the AoI minimization problem as nondeterministic polynomial hard. To tackle it, we propose a deep deterministic policy gradient (DDPG)-based distributed multinode resource allocation (DDMRA) algorithm, which combines the advantages of the distributed algorithms and centralized algorithms, and combines the selection of discrete actions in the deep Q-network algorithm into the DDPG algorithm. In the DDMRA algorithm, mobile IoT nodes save the energy consumption of transmitting state information to the HAP. Numerical results validate the superior performance of the DDMRA algorithm compared with the baseline algorithms.

The abnormal behavior detection is the vital for evaluation of daily-life health status of the patient with cognitive impairment. Previous studies about abnormal behavior detection indicate that convolution neural network (CNN)-based computer vision owns the high robustness and accuracy for detection. However, executing CNN model on the cloud possible incurs a privacy disclosure problem during data transmission, and the high computation overhead makes difficult to execute the model on edge-end IoT devices with a well real-time performance. In this paper, we realize a skeleton-based abnormal behavior detection, and propose a secure partitioned CNN model (SP-CNN) to extract human skeleton keypoints and achieve safely collaborative computing by deploying different CNN model layers on the cloud and the IoT device. Because, the data outputted from the IoT device are processed by the several CNN layers instead of transmitting the sensitive video data, objectively it reduces the risk of privacy disclosure. Moreover, we also design an encryption method based on channel state information (CSI) to guarantee the sensitive data security. At last, we apply SP-CNN in abnormal behavior detection to evaluate its effectiveness. The experiment results illustrate that the efficiency of the abnormal behavior detection based on SP-CNN is at least 33.2% higher than the state-of-the-art methods, and its detection accuracy arrives to 97.54%.

Currently, powerful and ubiquitous mobile devices provide an opportunity to map physical conditions to cyberspace and realize Digital Twins enabled Healthcare (DTeH). Especially, the impact of the COVID-19 epidemic renders it necessary to keep an eye on the changing trend of respiration. Long-term respiration monitoring helps to assess personal health status and thus becomes an important issue in DTeH. However, previous mobile device-assistant methods mostly implement the monitoring via short-time detection in a best-effort way and with less consideration of identity recognition, the only mean to bind physical vital signs into personal profiles in digital twins space. Thus, it is necessary to introduce the identification to complete string multiple short-time detections and form long-term personal monitoring. To this end, we propose IDRes, an identity-based respiration monitoring system for DTeH. This system employs mobile devices to generate a high-frequency sonar signal to complete respiration detection and identity recognition. As well as it also estimates the respiration rate by tracking the phase change of the sonar signal and recognizes identity via the Doppler frequency shift of the signal to capture characteristics of chest movement. Moreover, via band-pass filtering to remove the low-frequency voice component of the received signals, the usage of the high-frequency sonar signal also enhances security at the physical level. At last, we conduct a series of experiments under different conditions. Experimental results illustrate that IDRes achieves the mean detection error of 0.49bpm with over 93.3% recognition accuracy, and manifest that IDRes can satisfy the requirements of mapping the accurate vital sign data to the personal profile of DTeH.

As one widely applied wireless technique, WiFi has the potential to execute noncontact monitoring of vital signs based on channel state information (CSI). However, due to the dynamic of the surrounding environment, the bandwidth of the WiFi channel is not enough to identify the respiration-induced path from other movement-induced paths and this seriously limits the accuracy of respiration rate detection. In this article, we propose ExRadio, a system that can monitor respiration in high-dynamic environments via combining multiple WiFi channels. Specifically, the receiver synchronously switches the channels with the transmitter and samples CSI at multiple channels, and then the CSI data are combined and regarded as CSI data of one extended-bandwidth channel. However, the hardware-related noises from multiple channels are also accumulated. Eliminating these noises causes too heavy computation overhead to be afforded by the embedded devices and affects the real-time performance of respiration monitoring. To address this problem, we propose an effective approach that employs the ratio of CSI readings from the wireless channel and wire direct connection channel to shorten the time of eliminating the hardware-related noise. We deploy the ExRadio in commercial off-the-shelf embedded devices and conduct a series of experiments. The experimental results demonstrate that reducing the execution time is beneficial to respiration rate detection under high-dynamic environments, and the overall detection error of ExRadio is less than 0.5 bpm even when multiple persons who are 1.5-m away from the monitored person are fast walking.

Text classification is a major task of NLP (Natural Language Processing) and has been the focus of attention for years. News classification as a branch of text classification is characterized by complex structure, large amounts of information and long text length, which in turn leads to a decrease in the accuracy of classification. To improve the classification accuracy of Chinese news texts, we present a text classification model based on multi-level semantic features. First, we add the category correlation coefficient to TF-IDF (Term Frequency-Inverse Document Frequency) and the frequency concentration coefficient to CHI (Chi-Square), and extract the keyword semantic features with the improved algorithm. Then, we extract local semantic features with TextCNN with symmetric-channel and global semantic information from a BiLSTM with attention. Finally, we fuse the three semantic features for the prediction of text categories. The results of experiments on THUCNews, LTNews and MCNews show that our presented method is highly accurate, with 98.01%, 90.95% and 94.24% accuracy, respectively. With model parameters two magnitudes smaller than Bert, the improvements relative to the baseline Bert+FC are 1.27%, 1.2%, and 2.81%, respectively.