As an important long-range transportation carrier in the future Internet of Aerospace Things (IoAT), hypersonic flight vehicles (HFVs) will play a significant role in intelligent transportation systems (ITS). In IoAT architectures, HFVs function as intelligent edge nodes that must operate autonomously under severe uncertainties with limited onboard computational resources. However, strong coupling effects and unmodeled dynamics pose considerable challenges for the precise modeling and efficient control of HFVs. This paper proposes a novel framework for designing high-precision modeling strategies and efficient control algorithms for HFVs. The framework is named the Online-Enhanced Koopman (OE Koopman) Model Predictive Static Programming (MPSP) framework and is developed utilizing autoencoder neural networks. By leveraging the Koopman operator to lift nonlinear systems into high-dimensional linear spaces, the precise modeling associated with HFVs can be significantly simplified. Both the offline pre-training and online correction mechanisms are integrated into the OE Koopman-MPSP framework, which overcomes the issues of low precision in analytical modeling methods and insufficient data in deep learning methods. In the offline phase, autoencoder neural networks are utilized to construct the basic Koopman operator model. In the online phase, a few-shot-based Extended Dynamic Mode Decomposition (EDMD) method is employed to build compensatory operators for adapting to environmental changes. The proposed framework is specifically designed for resource-constrained IoAT edge devices, where computational efficiency and autonomous adaptation are critical. Experimental results demonstrate the effectiveness of the developed framework and specific algorithms.

Traditional traffic anomaly-based attack detection methods in Zero-trust Networks (ZTN) suffer from inherent security vulnerabilities, as they neglect considerations regarding their security defenses. Compromising the attack detection model itself can result in the breakdown of normal attack detection capabilities. Ensuring the security of the attack detection model during runtime presents a novel challenge. To address these shortcomings, we propose a novel attack detection model, termed Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-Trust Networks (SWS), aimed at enhancing the security of ZTN. SWS focuses on achieving attack detection in non-secure detection environments, to maintain its detection capability even when under attack. By employing a soft thresholding method, SWS adapts to the dynamic changes in network traffic, thus reducing the interference of attack signals. The incorporation of an attention mechanism enables SWS to concentrate on analyzing the most indicative traffic features of attack behavior. Additionally, we integrate Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the robustness of identifying complex network attack behaviors. The effectiveness of the SWS is validated through ablation studies, model comparisons, experiments conducted over different training epochs, and experiments conducted on various components of the dataset. Experimental results demonstrate that compared to existing attack detection models, SWS achieves improvements in detection accuracy and recall rate by 13.4% and 10.6%, respectively, while reducing the False Positive Rate (FPR) by 16.9%.

Given the severity of air pollution, air quality monitoring has become a crucial aspect of Artificial Intelligence of Things (AIoT) applications, providing essential information for forecasting air pollution. However, the training process for air quality monitoring models heavily relies on the high-performance computing resources, leading to significant energy consumption and associated carbon emissions. This contradicts the objectives of low-carbon and sustainable computing. This article proposes a new hybrid PM2.5 prediction model (NHPPM) for air quality monitoring to address the above challenges. NHPPM prioritizes energy efficiency while maintaining high prediction accuracy by integrating several power-efficient strategies. First, Wiener filtering is used to denoise the multisource air quality data enhancing the efficiency of the multisource data fusion. Second, variational mode decomposition (VMD) decomposes different components of the multisource air quality data, helping to identify and separate the most important factors affecting pollutants. This reduces the data needed for model training and leads to lower resource consumption. Kernel principal component analysis (KPCA) transforms the high-dimensional data into a lower-dimensional representation while retaining the critical information, further minimizing computational demands. Additionally, this article utilizes the informer deep learning model to analyse the trends in air quality data. The model’s effectiveness is validated through the ablation studies, performance evaluation experiments, and short- and long-term prediction experiments. The experimental results show that our model reduces the mean absolute error (MAE) and root mean-square error (RMSE) by 16.2% and 14.9%, respectively, compared to the existing PM2.5 prediction models. Furthermore, it reduces the energy consumption of the model training by 33.8%.

Benefiting from the progresses of sensing and sustainable computing technologies, recent years have witnessed the dramatic progresses of artificial intelligence of things (AIoT). As a typical AIoT application, WiFi-based human activity recognition has increasing popularities in smart homes. However, WiFibased action recognition often has unstable performance due to environmental interference. To this end, a robust deep learning framework called MSF-Net is proposed for coarse and fine activity recognition using channel state information (CSI) information. First, a dual-stream structure incorporating short-time Fourier transform and discrete wavelet transform is developed to highlight abnormal information in the CSI data. Then, a Transformer is employed as the backbone to effectively extract high-level features. In addition, an attention-based fusion branch is designed to enhance cross-model fusion. Experimental results show that MSF-Net achieves Cohens Kappa scores of 91.82%, 69.76%, 85.91%, and 75.66% on the SignFi, Widar3.0, UT-HAR, and NTU-HAR datasets, respectively. These performance records demonstrate the advantages of MSF-Net over existing methods for coarse and fine activity recognition based on WiFi data.

In the context of the booming Internet of Vehicles, predicting vehicle trajectories is crucial for intelligent transportation systems. Existing methods, reliant on sensor data and behavior models, struggle with intricate relationships between vehicles and dynamic road networks. To overcome these challenges, we propose the Vehicle Interaction-based Dynamic Graph Neural Network (VI-DGNN) model. This model constructs a vehicle interaction graph to capture temporal and spatial dependencies among vehicles. A spatiotemporal attention network is employed to discern patterns in vehicle movements, addressing high-speed changes. Our model introduces a vehicle interaction mechanism for dynamic movement, leveraging proximity timestamp graph structures. By incorporating vehicle behavioral features and road network topology, our model minimizes distribution prediction variance, enhancing stability. Experimental results on real datasets demonstrate superior long-term prediction performance compared to state-of-the-art baselines.

The security of the Internet of Vehicles (IoV) has always been a concern. The constantly changing IoV data under varying traffic conditions made it unsuitable for the IoV to adopt traditional anti-attack techniques. In the absence of protections, attackers can use in-car communication as a target to compromise the safety of passengers, hence the instancy to detect the security state of the IoV. However, currently available solutions require modifications to the original hardware of the IoV and are therefore very limited in applicability. In this paper, we propose a security assessment method for IoV based on Microcontroller Unit (MCU) chip temperature, called SAMCT. Specifically, we first record the MCU chip temperatures of IoV device in different security states and analyze the relationship between them. Second, the fingerprint dataset is built using the temperature residuals. Third, to forecast the security standing of IoV devices, an integration regression model based on Self-Encoders is suggested. Lastly, in order to facilitate the effectiveness of the SAMCT, a Cloud-Edge-End framework is designed with the technology of model adaptive partitioning. Results from the experiments, which were carried out on the Raspberry Pi 4B and Stm32 hardware platforms, demonstrate that the Mean Squared Error (MSE) of the SAMCT is only 0.00104 and that the execution efficiency improvement under the Cloud-Edge-End framework is significant.Note to Practitioners—This paper was inspired by security concerns in Internet of Vehicles communication systems. The core of this work is to provide a novel security assessment method for IoV devices based on MCU temperature, which can detect the security status of IoV devices in real-time without modifying the original hardware. To this end, the different skills from scheme design to detection and validation are explained. One crucial part of this work is to regard the MCU temperature of the IoV device as a security reference and fully integrate the critical techniques in deep learning. In addition, the proposed scheme is universal and can be applied to various scenarios such as the autonomous driving and the industrial internet of things.

Multimodal emotion recognition with EEG-based have become mainstream in affective computing. However, previous studies mainly focus on perceived emotions (including posture, speech or face expression et al.) of different subjects, while the lack of research on induced emotions (including video or music et al.) limited the development of two-ways emotions. To solve this problem, we propose a multimodal domain adaptive method based on EEG and music called the DAST, which uses spatio-temporal adaptive attention (STA-attention) to globally model the EEG and maps all embeddings dynamically into high-dimensionally space by adaptive space encoder (ASE). Then, adversarial training is performed with domain discriminator and ASE to learn invariant emotion representations. Furthermore, we conduct extensive experiments on the DEAP dataset, and the results show that our method can further explore the relationship between induced and perceived emotions, and provide a reliable reference for exploring the potential correlation between EEG and music stimulation.

With the spread of COVID-19 in recent years, wearing masks has increased the difficulty of driver mental fatigue recognition. Electroencephalogram (EEG) signal has become an important physiological signal index to reflect the driver’s mental state. However, the drivers’ EEG data is plagued by inadequate labels and multi-view data, which makes classification difficult. To solve this problem, this study proposes asemi-supervisedmulti-viewsparse regularization andgraph embedding learning (SMSG) model. To obtain discriminative feature representations of semi-supervised EEG data, SMSG fully mines diverse information from multiple views based on sparse regularization embedding and graph embedding technology. SMSG employs the graph embedding to capture the discriminative structure and local manifold structure on multi-view data. Furthermore, SMSG learns the common shared regularization embedding and private regularization embedding factors to preserve the consistency and diversity of the multi-view data. Through self-adaptive learning, the weights of each view can be directly solved adaptively. This works also introduces kernel trick to project the SMSG model into the nonlinear reproducing kernel Hilbert space (RKHS), which can obtain more approximate EEG feature representation. Experiments on the real dataset verify the effectiveness of the SMSG model for EEG-based driver mental fatigue recognition.

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.

Medical image segmentation has long suffered from the problem of expensive labels. Acquiring pixel-level annotations is time-consuming, labor-intensive, and relies on extensive expert knowledge. Bounding box annotations, in contrast, are relatively easy to acquire. Thus, in this paper, we explore to segment images through a novel Dual-path Feature Transfer design with only bounding box annotations. Specifically, a Target-aware Reconstructor is proposed to extract target-related features by reconstructing the pixels within the bounding box through the channel and spatial attention module. Then, a sliding Feature Fusion and Transfer Module (FFTM) fuses the extracted features from Reconstructor and transfers them to guide the Segmentor for segmentation. Finally, we present the Confidence Ranking Loss (CRLoss) which dynamically assigns weights to the loss of each pixel based on the network's confidence. CRLoss mitigates the impact of inaccurate pseudo-labels on performance. Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance on the Medical Segmentation Decathlon (MSD) Brain Tumour and PROMISE12 datasets.

Physics-informed learning for industrial Internet is essential especially to safety issues. Consequently, various methods have been developed to conduct Industrial Internet of Things (IIoT) intrusion detection. However, the conventional methods usually require the help of auxiliary equipment (e.g., spectrum analyzers, log-periodic antennas), which proves to be unsuitable for general IIoT systems due to their poor versatility. Facing the dilemma mentioned above, this article proposes a microcontroller unit (MCU) chip temperature fingerprint informed machine learning method, called MTID, for IIoT intrusion detection. Specifically, first, the node's MCU temperature sequence is recorded and the relationship between the temperature sequence and the computational complexity of the node is analyzed. Then, we calculate the temperature residuals and construct a temperature residuals dataset. Finally, to identify the security status of the nodes, a self-encoder-based intrusion detection model is constructed. Furthermore, to ensure the model's applicability under the diversified deployment environment of IIoT systems, an online incremental training method is developed and applied. In the end, we use the Raspberry Pi 4B for experimental analysis when testing the performance of MTID. The results show that the accuracy of MTID for intrusion detection reaches 89%, which also demonstrates the feasibility of the intrusion detection method based on MCU temperature.

Due to the limited computing power of Industrial Internet of Things (IIoT), it is impossible to port traditional attack resistance methods to run in IIoT systems. Currently, various methods have been developed to conduct security assessment for IIoT systems. However, these methods require modification of the original hardware of the IIoT system, so they are not universally applicable. In this paper, we propose a Non-intrusive Security Estimation Method (NSEM) for IIoT systems based on common attribute of IIoT devices. In the NSEM, we firstly record the common attribute (i.e. MCU chip temperatures) in different security states, and construct the temperature fingerprint dataset. Then, a Self-Encoder-based integration regression model is proposed to predict the security status of IIoT devices. Finally, we design a Cloud-Edge-End framework with model adaptive partitioning technology to support the efficient execution of the NSEM method. The experiments are conducted on Raspberry Pi 4B platforms. The results show that the Mean Squared Error (MSE) of the NSEM is only 0.001, and the Cloud-Edge-End framework can effectively improve execution efficiency.

Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a wireless sensor network. When the position of a beacon node is drifted or is spoofed, it becomes an unreliable beacon node, which would lead to a large relocalization deviation of unknown nodes in its neighbor. However, when selecting reliable beacon nodes, most relocalization algorithms only screen either drifting beacon nodes or malicious beacon nodes whose position is drifted or spoofed. This article proposes an algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes. The algorithm is divided into four steps. First, three indicators are introduced, where two are for describing position drifting and one is for describing position spoofing. Second, the entropy method is used to weight the contributions of three indicators. Third, a technique for order preference by similarity to an ideal solution is used to construct a reliability evaluation model. Finally, using the reliability evaluation model select reliable beacon nodes. Experimental results illustrate that the detection accuracy of drifting beacon nodes and malicious beacon nodes of the proposed algorithm is 7.5% and 8.2% higher than that of the state-of-the-art algorithms, respectively.

With the popularity of the wireless body sensor network, real-time and continuous collection of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data mining from the collected single-lead ECG waves has therefore aroused extensive attention worldwide, where early detection of atrial fibrillation (AF) is a hot research topic. In this paper, a two-channel convolutional neural network combined with a data augmentation method is proposed to detect AF from single-lead short ECG recordings. It consists of three modules, the first module denoises the raw ECG signals and produces 9-s ECG signals and heart rate (HR) values. Then, the ECG signals and HR rate values are fed into the convolutional layers for feature extraction, followed by three fully connected layers to perform the classification. The data augmentation method is used to generate synthetic signals to enlarge the training set and increase the diversity of the single-lead ECG signals. Validation experiments and the comparison with state-of-the-art studies demonstrate the effectiveness and advantages of the proposed method.