The evolution of Artificial Intelligence (AI) has reached a critical point, where agentic AI systems demonstrate strong capabilities in goal formulation and planning but remain difficult to deploy in real-world settings due to their limited grounding in physical environments. These limitations arise from the challenges of partial observability, actuation uncertainty, and strict resource constraints that characterize the physical world. This survey argues that the Artificial Intelligence of Things (AIoT) provides the necessary foundation to embed agentic intelligence into such environments by enabling continuous interaction between sensing, reasoning, and action. We analyze the synergy between goal-driven agentic AI and distributed AIoT infrastructures and present a unified taxonomy of AIoT-enabled agentic architectures, highlighting trade-offs across centralized, edge-native, and hybrid deployment models. The survey further examines key enabling technologies, including edge intelligence, semantic communication, digital twins, and trust mechanisms, and discusses how they integrate into cognitive control loops. Through representative applications in smart cities, industrial automation, healthcare, and energy systems, we show how this convergence moves automation beyond rule-based behavior toward context-aware autonomy. Finally, we identify open challenges related to long-horizon safety, resource-aware intelligence, and ethical governance, and outline research directions toward robust, trustworthy, and socially embedded autonomous systems.

The proliferation of IoT devices has limited the efficiency of heterogeneous data communication in distributed environments and increased security risks. Balancing scalability, efficiency and data privacy in IoT transaction systems becomes critical, and intent-based networks enable optimal configuration with minimal intervention. To optimize the network management environment, we propose a three-stage execution scheme for blockchain cross-shard transactions, which combined with a timeout rollback mechanism ensures atomicity and reduces latency. In addition, we design a fragment-based consensus protocol utilizing a verifiable random function, which improves the consensus efficiency through the randomness of committee member selection. In order to enhance system security, we introduce a reputation evaluation mechanism and a malicious node detection method based on normalized entropy. The mechanism dynamically adjusts the reputation value of a node according to its performance in the consensus process, so that high-reputation nodes can play a greater role in the consensus and detect malicious nodes in the network accordingly. By embedding this mechanism into a network management framework based on users’ intention, it can accurately realize users’ expectations for network performance optimization, security enhancement and efficient operation. Experiments show that our scheme not only improves communication efficiency, but also enhances the security of sharded transactions, effectively matching users’ high-level intentions for network scalability, efficiency, and data privacy.

Intelligent fault diagnosis for drilling equipment is often compromised by non-stationary noise and domain discrepancies. To address this, we propose TWE-DANN, a novel framework integrating Wavelet Threshold Denoising (WTD), Empirical Mode Decomposition (EMD), and Domain-Adversarial Neural Networks (DANN). The model features a hybrid filtering architecture to suppress heterogeneous noise and employs adversarial adaptation to align cross-domain feature distributions, ensuring robust performance under complex operating conditions. Experiments on real-world drilling datasets demonstrate the effectiveness of the proposed method. On the hydraulic source-domain dataset, under the most severe composite noise conditions, TWE-DANN enables the model to reach an F1-score of 98.64%. On the source (hydraulic) dataset, the model achieves an F1-score of 91.32%. In the cross-domain adaptation task from hydraulic to pneumatic equipment, TWE-DANN attains an F1-score of 86.98% after only five fine-tuning epochs, while outperforming baseline transfer methods by 3.71%.

Implementing internal tree hollow detection using IoT technology is a crucial method for forestry conservation. Current methodologies primarily rely on stress wave sensors for such inspections. However, the inherently low signal acquisition density of these sensors leads to significant discrepancies between reconstructed wave velocity tomography and conventional optical imaging principles. This sparse signal distribution severely limits the ability of existing image processing algorithms to resolve internal hollow features, creating a bottleneck of insufficient precision in hollow localization and dimensional estimation for detection systems. To address the aforementioned problems, the research team independently developed a sensor for detecting internal tree defects and proposed a transformer-based model (RCE-DETR) for internal tree hollow detection. The sensor comprises three core modules: stress wave signal detection probes, a signal processor, and a display module. The proposed model in this study is deployed in the signal processor. By replacing the original convolution in the transformer framework with receptive-field attention convolution, the model retains important feature information, effectively fuses signal features, and further improves the accuracy of defect detection. Additionally, the model incorporates Cascaded Group Attention (CGA) and Efficient Multi-scale Attention (EMA) to address the difficulty that existing methods have in precisely determining the size of internal tree defects. Experimental results demonstrate that compared with existing models, the RCE-DETR model increases the mean average precision (mAP) by 10.5%, 10.9%, 5.2%, and 6.3% respectively, when compared to the commonly used YOLOv11, YOLOv8, EfficientDet, and CenterNet++ models.

The meteorological communication networks provide critical data support for agriculture and environmental monitoring. However, covert gradient-based attacks persistently inject subtle perturbations, threatening data integrity and increasing the operational overhead for network operators. To achieve proactive service assurance and security-aware network management, this paper proposes a data integrity monitoring mechanism as a managed network function, named EK-IGNN. Unlike traditional passive detection, EK-IGNN functions as an active security service. It first employs the Empirical Mode Decomposition Kalman Filter (EMD-KF) to extract high-fidelity attack fingerprints, which are then analyzed by an Intrinsic Graph Neural Network (IGNN). The IGNN model captures complex dependencies and adaptively amplifies weak attack features, enabling closed-loop network security management. Experimental results demonstrate that the proposed algorithm achieving an average improvement of 16.07% in accuracy and 15.27% in F1-score over state-of-the-art benchmarks.

Taihu Lake, as a typical large shallow lake in China, contains sediments rich in nutrients such as nitrogen and phosphorus. These nutrients are prone to re-release under wind-wave disturbances, triggering eutrophication and posing a serious threat to water environmental safety. Existing sediment accumulation prediction methods mainly rely on manual sampling and physical modeling, which are costly and lack timeliness, making them insufficient to meet the needs of dynamic lake management. To address this, we propose a BC-LSTM model that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with a Causal Attention Mechanism for time-series prediction of sediment accumulation in Taihu Lake. The Bi-LSTM structure captures both historical and future sequence features, enabling the model to learn long-term and latent dependencies in sediment deposition comprehensively. Meanwhile, the introduction of a causal attention mechanism effectively identifies critical temporal changes, enhancing the model's responsiveness to nonlinear disturbances. Compared with traditional machine learning and existing deep learning models, the BC-LSTM demonstrates superior performance in both prediction accuracy and stability. Supported by empirical data, the model achieves an outstanding performance with a Mean Squared Error (MSE) of 0.01 and a coefficient of determination (R2) of 95.40% in the sediment accumulation prediction task for Taihu Lake. The results indicate that the BC-LSTM model not only accurately captures the nonlinear variation trends of sediment in Taihu Lake but also provides an efficient and reliable technical foundation for dynamic sediment monitoring and ecological management of lakes.

Understanding the distribution patterns and dynamic mechanisms of marine surface sediments is crucial for predicting the evolution of coastal environments. This study investigates the sedimentary characteristics in the Xiangshan Sea area based on comprehensive field surveys conducted in winter and summer 2023. Surface sediment samples were collected, and tidal current observation stations were established in both nearshore and offshore areas. Using integrated analytical methods, including grain size analysis, tidal ellipse analysis, and two-dimensional Grain Size Trend Analysis (GSTA), we identified distinct seasonal and spatial variations in sediment distribution. Results showed that muddy sediments predominated in both areas, with the offshore deep water area exhibiting higher proportions of muddy sediments (92.3%) and better sorting compared to the nearshore shallow area (82.2%). Tidal current analysis revealed strong vertical shear in the nearshore regions and pronounced spatial variability offshore. Sediment transport patterns displayed distinct directional trends, with southward and southeastward transport offshore and a counterclockwise circulation nearshore. The interplay of water depth, local topography, and seasonal wind fields primarily influences these transport patterns. These findings provide valuable insights into the modern sedimentation processes of the East China Sea shelf region.

Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). MoCFL enhances feature aggregation by introducing an affinity matrix that quantifies the similarity between local feature extractors from different clients, addressing dynamic data distribution changes caused by frequent client churn and topology changes. Additionally, MoCFL integrates historical and current feature information when training the global classifier, effectively mitigating the catastrophic forgetting problem frequently encountered in mobile scenarios. This synergistic combination ensures that MoCFL maintains high performance and stability in dynamically changing mobile environments. Experimental results on the UNSW-NB15 dataset show that MoCFL excels in dynamic environments, demonstrating superior robustness and accuracy while maintaining reasonable training costs.

The dynamic environment of UAV swarms in forest management is characterized by communication instability, heterogeneous nodes, and frequent topology changes due to challenging terrain. These systems are vulnerable to network attacks, requiring advanced intrusion detection technologies. Traditional methods struggle with rapid changes due to data privacy concerns and centralized computational limits, while existing federated learning (FL) algorithms lack robustness against client heterogeneity and dynamic data distribution, especially in complex forest environments. To address these challenges, we propose federated intrusion detection for securing UAV swarms (FIDSUS). FIDSUS improves intrusion detection systems by leveraging collaborative sensing among UAVs, enabling better monitoring and response to security threats in forestry. By quantifying the similarity between UAVs’ local feature extractors through an affinity matrix, FIDSUS guides the aggregation of feature extractors, improving detection capabilities. It also uses AI-driven aerial and distributed computing to enhance data processing efficiency and decision-making speed. The framework addresses data heterogeneity by cross-round feature fusion, improving detection in dynamic environments. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that FIDSUS outperforms existing FL methods with a 4%–34% accuracy improvement. FIDSUS shows robustness and accuracy in dynamic environments, providing an effective solution for securing UAV swarms in forestry.

As sustainable development gains attention, integrating carbon-intelligent computing into fault diagnosis systems has emerged as a critical strategy to reduce energy consumption and carbon footprints. This approach uses artificial intelligence (AI) and the Internet of Things (IoT) to optimize task scheduling, aligning it with low-carbon energy sources based on time and location. In fault diagnosis, energy-intensive tasks, such as data processing and model inference, can be scheduled during periods of abundant renewable energy, thereby minimizing emissions. However, drilling machines operate under complex conditions that generate nonstationary noise, which distorts signals and complicates fault diagnosis. Therefore, this article combines bidirectional long short-term memory (BiLSTM) with the Kolmogorov-Arnold network (KAN) and integrates Wavelet Transform and Convolutional Autoencoder, proposing a highly robust fault diagnosis model for drilling machines, named WCBK. The Wavelet Transform converts pressure time-series data, which contains fault information, into time-frequency images, facilitating the detection of fault frequency components. The Convolutional Autoencoder preserves essential features while removing noise by learning low-dimensional representations of the signal, effectively capturing local features in time-frequency images through local connections to enhance denoising performance. Finally, the composite deep learning network, which combines BiLSTM and KAN, achieves highly robust fault diagnosis under complex working conditions. The effectiveness of the proposed WCBK model was validated through ablation experiments, experiments on different individuals, experiments on different parts, and model adaptability evaluations. In experiments involving different individuals and parts, the WCBK model improved fault diagnosis accuracy by 10.9% and 8.8%, respectively, compared to existing models.

As a key economic crop, jujube’s external morphology directly affects quality grading and market value. However, traditional inspection methods relying on manual sampling or 2D image analysis suffer from inefficiency and limited feature characterization, particularly in quantifying complex geometric traits such as irregular wrinkles and localized depressions on jujube surfaces. Existing 3D reconstruction techniques have been applied in agricultural product inspection but face challenges in widespread adoption due to high costs and low resolution. This study proposes a single-view high-resolution RGB 3D reconstruction method for jujube based on generative artificial intelligence. Specifically, we designed a two-stage single-view 3D reconstruction framework and modified the cross-attention layers in the U-shaped network architecture of a stable video diffusion mode to meet the requirements of high-resolution and clear texture reconstruction for jujube. Additionally, we improved training and inference efficiency through parallel computing and achieved automation integration with Unmanned Agents. The proposed method successfully reconstructed 3D models from single-view 1,024 1,024 RGB images of jujube. Our model achieves a PSNR of 23.52 and an SSIM of 0.86 on the public dataset.This approach provides a low-cost, high-precision 3D digital solution for non-destructive phenotyping of jujube, offering practical value for advancing intelligent sorting and quality evaluation in agricultural production.

With the deepening integration of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent warehousing systems are increasingly confronted with the challenges of achieving high-precision navigation and task scheduling in dynamic environments. Although existing research has made notable strides in simultaneous localization and mapping (SLAM) and task scheduling, persistent issues–such as point cloud noise interference in dynamic scenes, inefficiencies in computational resource allocation, and insufficient multi-sensor collaboration–continue to constrain system performance. To address these challenges, this study proposes an AI-driven task scheduling SLAM framework named KORS designed to enhance navigational robustness and scheduling efficiency in dynamic warehousing environments. This study proposes the KCPoint model to achieve precise segmentation and elimination of dynamic point clouds. Building upon the PointNet++ architecture, KCPoint integrates K-Nearest Neighbors Enhanced Farthest Point Sampling (KFPS) and a Convolutional Block Attention Module (CBAM) to enhance feature extraction. In addition, a task scheduling mechanism is introduced to address the dynamic allocation of computational resources within vehicular networks. Relative to FAST-LIO2, the KORS system reduces absolute pose error (APE) by 31.32% and improves computational efficiency by 17.88% on the NCD and NCLT datasets. Furthermore, compared to conventional methods based on particle swarm optimization and genetic algorithms, the task scheduling algorithm achieves comparable decision-making benefits while reducing single-decision latency by over 42

The advent of 6G-enabled Internet of Everything(IoE) technologies is set to revolutionize urban infrastructures by providing fast, consistent, and low-delay capabilities for communication. 6G connectivity will integrate traffic and power grids for adaptive urban management. However, current traffic networks and power grids face critical challenges such as fragmented data processing, delayed responses, and outdated resource management leading to inefficiencies like traffic congestion and power outages. In 6G-enabled smart grid cities, system complexity and interdependence demand dynamic, real-time solutions, further exacerbating inefficiencies. To address these issues, this study introduces Digital Twin-enabled Real-time Optimization System (DT-ROS), a dynamic framework designed to optimize urban traffic and power grid systems. DT-ROS integrates a dual-tier Digital Twin (DT) and an advanced scheduling framework based on Priority Age of Information Deep Q Scheduler (PAoI-QS). The dual-tier framework builds an elementary and Integrated Digital Twin (IDT) with Auto-Regressive Integrated Moving Average (ARIMA)-based forecasting for accurate real-time traffic and energy demand predictions. The advanced scheduling framework minimizes the Age of Information (AoI), ensuring decision-making relies on the most current and relevant data. By continuously monitoring and processing real-time data, DT-ROS creates virtual models to simulate system behavior and dynamically allocate resources. Simulation results demonstrate the effectiveness of DT-ROS, achieving a 30% reduction in traffic congestion and a 25% improvement in power grid stability compared to existing methods. To create effective, robust, and sustainable urban systems for future smart cities, DT-ROS addresses traffic and electricity problems.

With the rise of the Internet of Things, wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns interlead relationships to improve multilabel ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a temporal graph construction module to convert ecg signals into temporal graphs and uses a residual pyramid graph convolution module for multilevel graph embeddings, utilizing a graph convolutional network with independently learnable adjacency matrices. Combined with a temporal context convolution module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on seven tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available athttps://github.com/rosemary333/TGLLnet.

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%.

Gait recognition aims to identify users by their walking patterns. Compared with appearance-based methods, skeleton-based methods exhibit well robustness to cluttered backgrounds, carried items, and clothing variations. However, skeleton extraction faces the wrong human tracking and keypoints missing problems, especially under multiperson scenarios. To address above issues, this article proposes a novel gait recognition method using deep neural network specifically designed for multiperson scenarios. The method consists of individual gait separate module (IGSM) and fuzzy skeleton completion network (FU-SCN). To achieve effective human tracking, IGSM employs root–skeleton keypoints predictions and object keypoint similarity (OKS)-based skeleton calculation to separate individual gait sets when multiple persons exist. In addition, keypoints missing renders human poses estimation fuzzy. We propose FU-SCN, a deep neuro-fuzzy network, to enhances the interpretability of the fuzzy pose estimation via generating fine-grained gait representation. FU-SCN utilizes fuzzy bottleneck structure to extract features on low-dimension keypoints, and multiscale fusion to extract dissimilar relations of human body during walking on each scale. Extensive experiments are conducted on the CASIA-B dataset and our multigait dataset. The results show that our method is one of the SOTA methods and shows outperformance under complex scenarios. Compared with PTSN, PoseMapGait, JointsGait, GaitGraph2, and CycleGait, our method achieves an average accuracy improvement of 53.77%, 42.07%, 25.3%, 13.47%, and 9.5%, respectively, and it keeps low time cost with average 180 ms using edge devices.

As the electromagnetic spectrum becomes increasingly crowded in recent years, synthetic aperture radar (SAR) is confronted with an escalating amount of radio-frequency interference (RFI). In civilian SAR satellite data, narrowband pulsed RFI (PRFI) is a prevalent interference type that significantly degrades the interpretability of SAR images. Among most approaches for suppressing PRFI, notch filtering methods face significant challenges in threshold selection of signal intensity. Conversely, low-rank and sparse decomposition (LRSD) algorithms, though free of threshold selection, often struggle to satisfy the required low-rank conditions. These limitations underscore the necessity of developing more robust interference suppression methods. In this article, we propose a joint framework of wavelet filtering and fast generalized singular value thresholding-based LRSD (FGSVT-LRSD) method to suppress narrowband PRFI in range–frequency and azimuth–time domain of SAR single-look complex (SLC) data. First, a wavelet domain notch filtering (WNF) method is employed to extract the strong spectral components that are primarily composed of PRFI in the 2-D range spectrum of SAR SLC data, while simultaneously protecting the spectrum of low-energy useful signals. Then, the FGSVT-LRSD method is applied to the extracted strong spectral components to efficiently separate the PRFI from the useful signals. By strategically integrating these two approaches, the proposed framework simultaneously exploits the high-intensity and low-rank properties of PRFI for more precise separation, significantly reduces the sensitivity of threshold selection in the WNF process and facilitates the low-rank conditions required by the FGSVT-LRSD method. Finally, the separated PRFI spectrum is subtracted from the original 2-D range spectrum, resulting in the RFI-suppressed SAR image. Experimental results based on both simulated and measured spaceborne SAR data will be presented to demonstrate that, compared with existing methods, the proposed approach exhibits superior PRFI suppression capabilities and effectively preserves useful signals.

Session-based recommendation (SBR) aims to predict the next item a user might click within an ongoing session, without relying on user profiles or historical data. Modern approaches typically use graph networks to learn item embeddings in Euclidean space via graph convolution operations. However, they often struggle to capture the diversity of user interactions within short, hierarchically structured sessions, which is essential for accurate predictions in SBR. To tackle these challenges, we propose a multi-interest hyperbolic representation network (MIHRN) to enhance the performance of SBR by adeptly modeling both intricate high-order spatial structures and sequence relationships among items in hyperbolic geometry space. Specifically, we use a hyperbolic hypergraph neural network to exploit the high-order spatial relationships and local clustering structures inherent within sessions. Subsequently, a multiaspect interest representation module is designed to articulate the diversity of user interests. Extensive experiments on three real-world datasets demonstrate that the proposed method achieves performance improvements of 23.81%, 14.81%, and 36.84%, respectively, under the P@10 metric.

Predicting vehicle trajectories is a crucial component of intelligent transportation systems, bearing significant research significance. Leveraging the latest advancements in deep learning and data processing technologies enables us to model intricate interactions among multiple vehicles in complex traffic scenarios. Traditional trajectory prediction methods typically rely on sensor data and vehicle behavior models, which may struggle to capture the intricate relationships between vehicles in dynamic, high-traffic situations, as well as the topological complexities of road networks. To tackle these challenges, we introduce a novel approach: the Vehicle-Driven Dynamic Graph Neural Network (V-DGNN) model. This model starts by constructing an interaction graph among vehicles, enabling the simultaneous capture of both temporal and spatial dependencies between them. Moreover, it incorporates a spatiotemporal attention network for extracting vehicle motion patterns. We also propose a unique mechanism to address the challenges posed by high-speed spatiotemporal changes. This mechanism involves the sensitive sampling of nearby timestamps to effectively learn the dynamic distribution of vehicles. Additionally, the model incorporates vehicle behavior features and road network topology information as supplementary inputs while minimizing prediction variances. This equips our model with the ability to make robust predictions even in the face of distribution changes. Experimental results on two real-world datasets convincingly demonstrate that our approach outperforms current state-of-the-art models, delivering superior long-term predictive performance.

With the rapid development of Internet of Vehicles technology, mobile communications are gradually integrated with various fields, and more and more Internet of Vehicles equipment are connected to the Internet. However, existing online information transmission methods mainly rely on the original network infrastructure. Once the communication infrastructure fails, it is likely that information transmission will fail or even be lost. In this paper, we utilize the optical modules that come with sensor nodes to implement a hybrid communication debugging system based on Visible Light Communication (VLC). To enhance uplink reliability, we’ve devised an optical camera-compatible frame synchronization method. Leveraging the Transformer algorithm, we predict frame header positions, thereby bolstering data collection reliability. Additionally, for efficient debugging information uploading, we logically group and organize the initial data, and use the Snappy compression algorithm to decrease empty time slot count to complete the data compression, saving time. Finally, confidentiality enhancement technology is introduced in the physical layer, and a new security enhancement optimization scheme based on Artificial Noise is proposed. The Artificial Noise (AN) sent by the sender enables the sender to counter eavesdropping interference, and the authorized recipient can cancel the Artificial Noise (AN). The results show that the proposed scheme is more secure.

In recent years, the issue of children's height has garnered widespread attention on social media. Social media platforms serve as pivotal communication channels for parents, doctors, educators, and researchers, with children's height, a crucial health indicator, becoming one of the hot topics. Inaccurate prediction methods might mislead the public, resulting in parents harboring erroneous expectations regarding their children's future height. Such misplaced expectations may culminate in unwarranted worries or pressure. In pursuit of devising an accurate height prediction model, this paper thoroughly leverages a substantial data sample obtained from the physical health examinations of primary and secondary school students in Zhejiang Province, as well as continuous observation samples provided by the Zhejiang Provincial Bone Age Research Center, to delve deeply into the issue of height prediction in children and adolescents. In this research, we have developed a lightweight neural net-work model suitable for particle swarm optimization to predict children’s stage-wise height. When the difference between the actual and predicted values is within ±2cm, the prediction accuracy for boys reached 86.67%, and for girls, it was 85.32%, with an RMSE of 1.3503.

In this paper, UAVs are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable, and the localization problem is highly nonlinear to the unknown parameters. To this end, the semidefinite relaxation (SDR) technique is proposed to solve the localization problem. Since the constrained relationship among the variables is difficult to be fully included in the stage-one SDR problem, we develop a novel two-stage SDR solution for this localization problem. The performance of the two-stage SDR solution is proven to be close to the Cramér-Rae Lower Bound (CRLB) accuracy at the small noise levels. The simulated results show that the two-stage SDR solution performs better than the closed-form solution, especially at high noise levels.

The millimeter-wave(mm-Wave) radar based on frequency-modulated continuous wave (FMCW) owns the advantages of non-contact, privacy protection, high resolution, and anti-interference, and it became the hot point that applying the radar in monitoring vital signs for smart home. However, most of current studies focus on how to improve the detection performance under the steady scenarios and give little consideration on the unsteady scenarios with physical interference. In this paper, we propose a method to detect vital signs under unsteady scenarios by a best-effort way. This method automatically differentiates between the steady state and motion state (unsteady) by identifying the motion type, and extracts the vital sign under steady state without physical motion interference. For this end, we first figure out feature spectrograms with range-main velocity information from motion features. And then, employ a sliding windows sampling method to construct data set, and apply ResNet-18 network model in the motion type identification (including steady state). Based on the motion type, the phase signal during steady state and leverage the variational mode decomposition (VMD) algorithm to analyze respiration/heart rate. Experiment results show that using ResNet-18, the recognition accuracy of the motion state and motion type is close to 97%, and the recognition delay is less than 1.1s. Meanwhile, the mean absolute errors of the respiration rate and heart rate drop to 1.7bpm and 3.4bpm respectively.

UAV technology has been developing rapidly in recent years, and UAV remote sensing image target detection plays an important role in military, agriculture, forestry and marine fields. Currently there is a lack of consideration for the safety aspects of UAV image target detection, so we propose an intelligent interference information fusion method to expand the dataset. By adding interference to the original dataset images and generating the affected dataset images to train the model, the anti-interference ability and robustness of the target detection model are improved. We also propose a highly robust target detection framework (RA-RTDETR) with strong anti-interference capability to improve the detection accuracy of disturbed images. The framework fuses three methods, deep residual contraction network and cascading attention mechanism, and is experimentally compared with other methods (yolov5s, yolov8s, RTDETR) on a dataset. The experimental results demonstrate that the dataset with fused interference information has a significant improvement in model robustness, and the accuracy of our proposed RA-RTDETR achieves the best results.

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%.

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.

As The cornerstone of human health, physical and mental well-being are intricately linked, influencing both an individual's physical condition and their emotional state [1]. Chronic diseases such as hypertension and diabetes can have a significant impact on mental health, leading to anxiety and depression [2]. Similarly, psychological problems such as stress, anxiety, and depression can weaken the immune system, making individuals more susceptible to physical illnesses. In recent years, the rapid development of technology has brought exciting new possibilities to the field of physical and psychological health. The Internet of Things (IoT) and artificial intelligence (AI) have shown great potential in building a comprehensive health management system that empowers individuals to take a more proactive role in their well-being.

Bone Age (BA) is reckoned to be closely associated with the growth and development of teenagers, whose assessment highly depends on the accurate extraction of the reference bone from the carpal bone. Being uncertain in its proportion and irregular in its shape, wrong judgment and poor average extraction accuracy of the reference bone will no doubt lower the accuracy of Bone Age Assessment (BAA). In recent years, machine learning and data mining are widely embraced in smart healthcare systems. Using these two instruments, this article aims to tackle the aforementioned problems by proposing a Region of Interest (ROI) extraction method for wrist X-ray images based on optimized YOLO model. The method combines Deformable convolution-focus (Dc-focus), Coordinate attention (Ca) module, Feature level expansion, and Efficient Intersection over Union (EIoU) loss all together as YOLO-DCFE. With the improvement, the model can better extract the features of irregular reference bone and reduce the potential misdiscrimination between the reference bone and other similarly shaped reference bones, improving the detection accuracy. We select 10041 images taken by professional medical cameras as the dataset to test the performance of YOLO-DCFE. Statistics show the advantages of YOLO-DCFE in detection speed and high accuracy. The detection accuracy of all ROIs is 99.8%, which is higher than other models. Meanwhile, YOLO-DCFE is the fastest of all comparison models, with the Frames Per Second (FPS) reaching 16.

Upon the spreading of intelligent transportation technology, reconfigurable intelligent surface (RIS) can be applied in traditional vehicular networks to assist autonomous driving, so as to deal with blind spots and high energy consumption of long-distance communication in vehicles communication system. However, achieving bidirectional reliable communication for vehicular networks is challenging because of the high mobility of vehicles. The existing solution relies on constantly reconstructing end-to-end communication links for bidirectional communication in vehicular networks. Moreover, waiting timers on the data transmission path can double the transmission delay for data return, not to mention the potential communication delays and overheads triggered by the rapid movement of the vehicle. In order to avoid those tricky situations, this article proposes a novel two-way reliable forwarding strategy (TRFS) for vehicular named data networks and adopts the RIS symbiotic communication scheme to further reduce the transmission overhead, which has the ability to establish and maintain a reliable end-to-end communication link. On the one hand, the crucial insight of the proposed TRFS is to achieve low delay by eliminating wait timers on the data return path. Specifically, a topology-free policy is used to reduce transmission overhead and to accommodate sudden changes in network topology. On the other hand, the proposed RIS-assisted symbiotic communication scheme expands the coverage of vehicle communication and acts as a relay to compensate for the energy consumption from the long-distance transmission. Moreover, we also investigate the reliability of the proposed TRFS in vehicle communication under various vehicle densities, vehicle communication distances, and speed variations. The experiments show that the proposed TRFS scheme outperforms the classic listen first broadcast last (LFBL) fast-forwarding strategy and the novel reinforcement-based lightweight forwarding (R-LF) strategy in terms of transmission overhead, satisfaction rate, and average delay. The results reiterates the proposed RIS-assisted scheme’s ability to further reduce the transmission overhead of vehicles communication.

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.

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.

Bone age assessment based on X-ray Images can accurately determine the actual bone age of adolescents. Accurate extraction of the key regions of interest (ROIs) in X-ray images is required to accurately assess bone age. However, existing ROI extraction methods can only extract a few targets and have poor extraction accuracy. Thus, the strict demands for imaging in the medical field via these methods are difficult. In this article, we propose a multitarget interested region extraction method for wrist X-ray images based on optimized AlexNet and two-class combined model, named OATC, which can simultaneously extract multiple ROIs with high accuracy. Specifically, the square-wave scanning algorithm was implemented to obtain the bounding box size of each bone ROI according to the shape information of the wrist. Then, the optimized AlexNet was used to obtain the key point coordinates of each bone ROI. Bone ROIs could be extracted by combining key point coordinates with the bone bounding box size. Finally, the two-classification model was combined to improve the accuracy of ROI extraction. Experiments conducted on our wrist X-ray image dataset showed that OTAC has a fast convergence speed and small deviation. The average accuracy of extracting 14 ROIs reached 95.57%, which are 7.76% and 4.68% higher than that of VGG16 and AlexNet, respectively.

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.

Theabnormal 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%.