The industrial control anomaly detection algorithm effectively monitors and identifies anomalies in industrial control systems, ensuring timely issue detection to maintain system reliability, safety, and regular operation. Researchers often integrate a large number of high-precision, high-sampling-frequency sensors into the system to obtain comprehensive real-time equipment operating data. However, deploying consumer-grade high-performance computing devices in large-scale industrial control systems is often impractical, rendering existing anomaly detection systems unusable in practical environments. Most existing sensor feature selection algorithms are unsuitable for industrial control time series datasets, and some suffer from issues such as a limited reduction in the number of sensors and a decrease in anomaly detection accuracy after reduction. We propose an offline industrial control anomaly detection algorithm based on the Maximum Information Coefficient (MICHC-NN) to address these challenges. We utilize the Maximum Information Coefficient to compute correlations among sensors, addressing the issue of existing feature selection algorithms relying on anomaly labels. Additionally, we introduce a key sensor selection algorithm based on correlation, identifying interrelated sensors in an industrial control system and determining the key sensors most susceptible to system anomalies. These key sensors allow the anomaly detection algorithm to be deployed with a smaller footprint in real industrial control environments. Finally, we employ an autoencoder-decoder model with attention mechanisms to train the anomaly detection network. Experimental results demonstrate that our proposed anomaly detection algorithm achieves performance comparable to using all sensors, with a 75% reduction in the number of sensors employed.

Deep learning (DL)-based physical-layer authentication (PLA) has shown promising performance in Industrial Internet of Things (IIoT) scenarios. Existing methods formulate PLA as a single multi-classification task with a static training dataset and implicitly assume the training and authentication data are drawn from the same distribution. However, user mobility over time in mobile IIoT leads to temporal distribution shift in Channel Impulse Response (CIR), severely degrading authentication performance. To address this challenge, we propose TMML-PLA, a Temporal Meta-Metric Learning method that reformulates multi-user PLA as a sequence of temporal few-shot classification tasks based on the intrinsic temporal correlation characteristic of CIR. When new CIR samples require authentication, the model dynamically constructs a task using a small set of the most recent CIR samples of each user as the support set to ensure distributional consistency. A temporal meta-metric learning scheme based on the Prototypical Network is introduced to tackle these tasks under evolving CIR distributions, and a temporal decay mechanism is designed to emphasize recent training samples. Experiments on a real-world IIoT dataset demonstrate that TMML-PLA significantly improves both accuracy and robustness. For instance, when the number of users is 15 and the authenticating CIR samples increase over time, TMML-PLA maintains stable accuracy from 88.20% to 85.75% while the DL-based ResNet drops sharply from 83.93% to 46.95%, confirming its robustness and accuracy in mobile IIoT scenarios.

Secure authentication and key agreement across heterogeneous trust domains is essential for reliable collaboration in the Industrial Internet. However, existing cross-domain AKA schemes still face certificate-management overhead, key-escrow risk, insufficient identity privacy, and high computational cost. To address these issues, this paper proposes LBCAK, a lightweight blockchain-assisted anonymous cross-domain AKA scheme. LBCAK combines a certificateless-style key construction with AuthLedger-based public-state coordination, where the blockchain maintains current registration states for cross-domain credential validation while online authentication and session-key establishment remain off-chain. Credential-state-specific protected identifiers are further used to reduce on-chain linkability without exposing raw identities or private credential materials. Security is analyzed under an eCK-style leakage model in the random-oracle setting and further examined using ProVerif, showing that LBCAK provides mutual authentication, identity privacy, untraceability, forward secrecy, and resistance to major active and key-leakage attacks. Performance evaluation shows that LBCAK reduces computational and communication overheads by 14.3% and 4.2%, respectively, demonstrating its practicality for resource-constrained Industrial Internet deployments.

Blockchain has been increasingly adopted in the Industrial Internet of Things (IIoT) for cross domain authentication, enabling trusted collaboration among multiple management domains and supporting decentralized trust management. However, existing authentication schemes, particularly those based on classical public-key cryptography such as RSA and ECC, are vulnerable to quantum attacks and still face significant challenges in computation overhead. These limitations make them insufficient for the practical demands of complex, multi-domain environments, especially in the context of future quantum threats. To address these challenges, this article proposes PQEdgeAuth, an edge-assisted authentication scheme for IIoT that integrates post-quantum secure authentication with an optimized consensus protocol. The scheme incorporates post-quantum cryptographic techniques to ensure long-term security against quantum adversaries, while employing location-based grouping and aggregation signatures to reduce consensus communication overhead and enhance scalability. Experimental results show that PQEdgeAuth reduces computation time by up to 35.23% and improves throughput by up to 54.38% compared to existing schemes, demonstrating its potential to offer a secure, scalable, and efficient solution for cross domain authentication in IIoT environments.

CSI-based physical-layer authentication (PLA) is a promising solution for securing access in wireless communication systems. However, distribution shifts caused by time-varying CSI and computational limitations due to constrained edge resources jointly degrade authentication accuracy. Existing methods lack effective modeling of the temporal dynamics and long-range dependencies in time-varying CSI and high computational costs, resulting in limited robustness and generalization in dynamic environments. To address these challenges, we propose LRTV-PLA, a lightweight and robust PLA with time-varying distribution shift in dynamic wireless environments. Specifically, we propose temporal interpolation with contrastive regularization (TICR) to address sparse CSI sequences by interpolating missing data and constraining cross-time variation. We also adopt a shallow autoencoder for nonlinear dimensionality reduction (SAE-NLDR) to reduce cost while preserving discriminative features. We further introduce LinA-BiLSTM, which combines bidirectional LSTM for short-term dependencies with linformer-based mulit-head attention for efficient long-range modeling, reducing attention complexity from$O(n^{2})$to$O(n)$and enabling lower latency and memory usage on edge devices. Finally, extensive experiments conducted in real-world scenarios demonstrate that the proposed method outperforms existing approaches in accuracy, robustness, and resource efficiency. For example, in the street scenario, our method improves authentication accuracy by 1.47% compared to the best baseline, shortens authentication time by approximately 60%,decreases memory usage by around 36%, and reduces the standard deviation from 1.52 to 0.95.

Modified Polar Representation (MPR) has shown its superiority in integration of near and far field localization for a stationary source. This paper extends the researches and applications of the MPR to moving source, where time difference of arrival (TDOA) and frequency difference of arrival (FDOA) exist due to the relative motion between sensors and source. Theoretical analysis through hybrid Bhattacharyya-Barankin (HBB) bound is provided for evaluating the performance tighter than the Cramér-Rao lower bound (CRLB). We propose a Maximum Likelihood Estimator (MLE) implemented by Gauss-Newton (GN) iteration based on the extended MPR to estimate the position and velocity of source and test the performance under the new MPR. Simulation results confirm that MPR can eliminate the thresholding effect of source position as the source moves far away.

Medical image segmentation based on neural networks is pivotal in promoting digital health equity. The attention mechanism increasingly serves as a key component in modern neural networks, as it enables the network to focus on regions of interest, thus improving the segmentation accuracy in medical images. However, current attention mechanisms confront an accuracy-complexity trade-off paradox: accuracy gains demand higher computational costs, while reducing complexity sacrifices model accuracy. Such a contradiction inherently restricts the real-world deployment of attention mechanisms in resource-limited settings, thus exacerbating healthcare disparities. To overcome this dilemma, we propose a parameter-free Neighborhood Self-Dissimilarity Attention (NSDA), inspired by radiologists' diagnostic patterns of prioritizing regions exhibiting substantial differences during clinical image interpretation. Unlike pairwise-similarity-based self-attention mechanisms, NSDA constructs a size-adaptive local dissimilarity measure that quantifies element-neighborhood differences. By assigning higher attention weights to regions with larger feature differences, NSDA directs the neural network to focus on high-discrepancy regions, thus improving segmentation accuracy without adding trainable parameters directly related to computational complexity. The experimental results demonstrate the effectiveness and generalization of our method. This study presents a parameter-free attention paradigm, designed with clinical prior knowledge, to improve neural network performance for medical image analysis and contribute to digital health equity in low-resource settings. The code is available at [https://github.com/ChenJunren-Lab/Neighborhood-Self-Dissimilarity-Attention](https://github.com/ChenJunren-Lab/Neighborhood-Self-Dissimilarity-Attention).

Localizing a three-dimensional (3D) source using linear arrays (LAs) is a promising new localization technology. Existing solutions are either designed for specific LA deployments, are computationally intensive, or rely on iterative methods that do not guarantee convergence. This paper presents a novel algebraic solution algorithm for 3D source localization using space angle (SA) measurements from LAs. We propose a new formulation of the SA measurement equation, which leads to a constrained weighted least squares (CWLS) problem. Solving it by Lagrangian multipliers, the optimal estimation is obtained with an error correction. The solution does not require specific arrangement and placement of LAs and effectively balances accuracy with computational efficiency. We analyze the performance and complexity of the proposed solution, demonstrating its ability to achieve the Cramér-Rao Lower Bound (CRLB) in the small error region under Gaussian noise with a low computational load. Simulations validate the analysis and confirm the superiority of the proposed solution compared to existing ones.

With the rapid advancement of industrial structures, many factories are deploying anomaly detection systems. However, most existing anomaly detection algorithms are unsuitable for high-noise industrial control system (ICS) environments and constantly changing sensor patterns, resulting in low detection accuracy. To address these challenges, we propose a hierarchical temporal memory-based online anomaly detection algorithm for ICS, incorporating cumulative distribution functions and Gaussian convolution kernels. We enhanced the encoding process of hierarchical temporal memory, enabling it to quickly fit the physical processes of ICS and remember the system’s previous operating states for anomaly recognition. Additionally, our anomaly calculation algorithm, based on cumulative distribution functions and Gaussian kernel convolution, effectively addresses the adaptability and accuracy challenges posed by high-noise ICS environments. Experimental results demonstrate that our method outperforms the best baseline algorithms. While maintaining good time stability, our method achieves a 9$\%$improvement in detection accuracy, highlighting its significant advantage in balancing detection precision and time performance.

The convenience of wireless communication has led to its widespread adoption across various application scenarios. However, existing Physical Layer Authentication (PLA) schemes still have limitations in terms of adaptability. This paper introduces a novel method based on key-less channel-based authentication framework designed to enhance PLA performance under different mobility patterns. Our method combines the sliding window with a model based on a siamese neural network, enabling a fully connected neural network classifier to effectively distinguish between legitimate transmitters and potential attackers, achieving an accuracy of 97.91%. Experiments conducted in various environments demonstrate the robustness and high performance of the proposed method, making it well-suited for deployment in time-varying environments with high-security demands and resource constraints.

The emergence of digital twin technology offers a promising solution to address the limitations of traditional methods on early diagnosis and accurate propagation analysis of flight ground service delays. However, the application of digital twin technology in the civil aviation domain still stays at the lower maturity of the L2 level, which focuses on physical assets, operational data, and maintenance planning at airports, and failed to achieve the integration of flight ground operation mechanism and real‐time data, making it difficult to realize timely delay diagnosis. The simulation model is also limited to the offline simulation technology, which cannot connect to real‐time data for simulation from intermediate processes. In this work, we developed an advanced L3‐level airport digital twin system for flight ground service processes delay diagnosis and propagation, which focused on real‐time data‐driven simulation models and machine learning applications to meet the timely and precision requirements. First, we used the Unity3D platform to construct static three‐dimensional models of flight ground service objects on the airport cloud server. By parsing these behavioral state interfaces and mapping real‐time dynamic data from the airport sensing and business systems, we achieved accurate visualization of the airport’s dynamic operational processes. Then, a vehicle delay tree–based Bayesian diagnostic model was proposed in the digital twin system to analyze the relationships between multiple flights and service processes, which enables proactive diagnosis of the operation status and provides delay warning information. To improve the accuracy of propagation analysis, we proposed a “breakpoint” simulation method that enables real‐time simulation starting from an intermediate moment, facilitating the inference of flight ground service delays since the early warning moment. In addition, two delay tracing and propagation algorithms were proposed to identify delays and investigate propagation paths. Leveraging real‐time operational information, our approach provides valuable feedback for decision‐making, empowering the airport manager to formulate precise optimization strategies. Experiments on real‐world airport data have validated the effectiveness of our proposed method and provided practical recommendations for airport managers to reduce aircraft delays and improve airport operation efficiency.

Reliable and robust airport flight ground service resources collaborative scheduling has emerged as an issue of major concern at congested airports, which has great significance on airport service quality and operation efficiency. Due to the uncertainty of the service process of multiple vehicles in airport ground operations, the combination of simulation and optimization (sim-opt) techniques has been proven to be an effective means to solve the collaborative scheduling problem. However, existing models still have limitations on the reliability of feasible solutions and the robustness of evaluations. In this work, we developed a conflict resolution based sim-opt framework that combines the search capability of mathematical optimization models with the ability of simulation models to describe uncertainty. The dynamic window is introduced to divide the flight zone into multiple sub-intervals, which allows for iterative search and evaluation of the optimal schedule solution while reducing model calculation time. Additionally, a matching algorithm based on slack and tight constraints is proposed to optimize the possible resource requirements of each vehicle, thereby improving the initial schedule solution’s search depth and reliability. Furthermore, a conflict resolution feedback mechanism between adjacent windows is constructed to optimize the scheduling plan, reducing misjudgment and omission of the optimal solution and enhancing the sim-opt framework’s robustness. Finally, experiments on real airport datasets of three different scales demonstrate that our proposed method efficiently allocates resources with a smaller flight delay time as well as reducing total vehicle cost time during the flight ground service process.

Industrial control systems rely on wireless devices and sensors, necessitating critical security. Physical layer authentication (PLA) is a promising mechanism for device authentication, utilizing its unique spatiotemporal characteristics and channel state randomness, which offers unforgeability and high informatics security with low computational overhead and efficiency in resource-constrained scenarios. However, existing PLA mechanisms face challenges in complex industrial wireless environments, including insufficient accuracy, computational complexity, inadequate noise consideration, and poor performance. To address these challenges, we propose a convolutional denoising autoencoder (CDAE) model that reduces feature dimensions, eliminates noise, and extracts key vectors. The weighted$k$-nearest neighbor algorithm classifies the extracted vectors for comprehensive authentication in control system networks. Accurate authentication enables efficient detection of malicious attacks. Simulation experiments show that using CDAE-extracted feature vectors achieves over 95% accuracy with only 1% training samples, surpassing channel state information-based authentication by 46.15%, validating the proposed mechanism’s effectiveness.

Unlike conventional IT systems, industrial control systems (ICSs) requires tailored attack detection methods due to its unique communication protocols. Existing attack detection methods lack the ability to consider both detection accuracy and time performance, particularly for highly stealthy fake data injection attacks (FDIAs). To address these challenges, this work proposes an online parallel attack detection method for ICS based on multibandpass filter. By building multiple adaptive filters based on energy equilibrium and time–frequency domain data transformation, we implement multifrequency band data segmentation. Hierarchical temporal memory (HTM) models are employed to parallelly fit the segmented data and detect anomalies. Simulation experiments demonstrate that our method outperforms the state-of-the-art Numenta method, achieving a 9% higher detection accuracy while reducing detection time to just 1/14 of Numenta’s. These results highlight the significant advantages of our method in striking a balance between detection accuracy and time performance. Our proposed method fills the gap in ICS attack detection and offers substantial improvements over existing techniques.

In recent years, industrial control systems (ICSs) security incidents have revealed vulnerabilities in the system hardware, user programs, and communication protocols. The various components of the ICS are connected by the Industrial Internet of Things (IIoT) protocol. Nevertheless, malicious attackers can exploit vulnerabilities in IIoT protocol to manipulate the ICS, potentially causing damage to the associated ICS equipment. This work focuses on the challenge of identifying vulnerabilities in IIoT protocols, aiming to enhance the system security through advanced fuzz testing techniques. To address the limitations of current fuzz testing in IIoT protocols, such as short prediction sequence lengths and low recognition rates, this work proposes a novel fuzz testing model based on the long attention mechanism, named TXL-Fuzz. This model is capable of handling longer protocol sequences and improving the diversity of the generated test cases. Experimental results demonstrate that the model outperforms the existing fuzz testers in test case recognition rate (TCRR) for the protocols of different lengths. Notably, TXL-Fuzz achieves a bits-per-character (BPC) of approximately 0.5, significantly lower by nearly 0.3 compared to the Anti-Sample Fuzzer, the long short-term memory network (LSTM)-based model, and GRU-based model. Furthermore, it exhibits a TCRR that is 5% to 15% higher than Peach Fuzzer, Anti-Sample Fuzzer, and BLSTM-DCNNFuzz under similar conditions.

Mobile edge computing (MEC) can sufficiently meet the computing demands of complex application consists of multiple interdependent tasks which can be represented by a directed acyclic graph (DAG). For tasks in a DAG, different scheduling orders and offloading decisions will generate different completion time, which further affects the Quality of Experiences (QoEs). So it is important to study the scheduling and offloading schemes for tasks in MEC scenarios. To this end, we first designed a scheme that schedules tasks with the highest response ratio and offloads tasks to the optimal processor with the optimization method for a DAG, which is termed as HRRO algorithm. Then, considering the complexity of the reality, we extended the HRRO to the ultradense MEC system and achieved the optimal joint scheduling and offloading scheme for multi-DAG based on the genetic algorithm, which can be concluded as HRRO based on the genetic algorithm (HRRO-GA). Subsequently, to evaluate the performance of the algorithms, we conducted amounts of the simulation experiments and compared the results with several state-of-the-art algorithms, including distributed earliest finish-time offloading (DEFO), potential game-based offloading algorithm (PGOA), and GA-based multiuser earliest finish time (GA-MEFT). Meanwhile, we selected some random strategies to verify the schemes of HRRO-GA are the best. Finally, we concluded that HRRO-GA is more suitable for the ultradense MEC system.

To make the video more attractive, original video materials usually need postprocessing by video editors, especially to eliminate low-quality abnormal clips, which seriously affect the visual effect. One of the main reasons for the low-quality abnormal clips is that there are occluders that accidentally break into the shot to occlude the protagonist, resulting in the loss of the video protagonist’s information. However, it is time-consuming and laborious to manually find shot occlusion clips, so computer vision technology can be used to assist editors in completing this work. The previous solutions directly utilize neural networks to detect shot occlusion, so their performance is affected by the size and quality of the dataset. In contrast, inspired by the change of depth information in the frame caused by the occluder breaking into the shot, we propose an algorithm for video shot occlusion detection based on the fluctuation of depth information. This algorithm does not need occlusion data training and can detect shot occlusion well only by capturing the abnormal fluctuations of the frame depth information. Additionally, to overcome the defect in that the first video shot occlusion detection (VSOD) dataset released in our conference publication can only verify the sensitivity of detection methods, we expand the VSOD dataset to evaluate the comprehensive performance of detection algorithms. The plentiful experimental results show that, compared with state-of-the-art occlusion detection methods and self-designed baseline methods, our algorithm significantly improves the comprehensive performance of video shot occlusion detection. Furthermore, through verification on datasets with different data types and distributions, our shot occlusion detection algorithm can maintain an occlusion event recall of over 95%, while the false positive rate does not exceed 3%, demonstrating good generalization ability. To promote reproducible research, the code and dataset are available athttps://github.com/Junhua-Liao/VSOD.

Industrial control technology faces serious communication security threats. Authenticated key agreement(AKA) protocols are essential to secure communication between industrial control nodes, but they must be efficient and robust due to resource constraints. Existing AKA protocols based on encryption algorithms have limitations such as vulnerability to clone attacks. Also, there is a lack of protocol’s research that fully integrate advantages of physical and upper layer. We propose a novel cross-layer AKA protocol that leverages channel state information(CSI), which has uniqueness and real randomness, and is unforgeable, to enhance security and reduce computational overhead. Our protocol only requires simple operations such as algebraic, Hash and XOR. We introduce a new security verification model, superCK, which extends the well-known eCK and relaxes assumptions on attackers. Our protocol achieves 12 fully provable key security capabilities, and has 75.91% less computational overhead and 6.99% less communication overhead than the best ones, achieving an optimal trade-off between security and efficiency.

Current wireless cross-domain solutions are limited to cross-one-factor tasks, requiring target domain data participation for training or position-independent feature extraction using multiple transceivers. Therefore, this paper aims to demonstrate cross-domain wireless sensing in a more challenging domain generalization (DG) setting without multiple transceivers or target domain data. Specifically, we propose a style-randomized cross-domain wireless sensing model called WiSR, which extracts domain-invariant features from multiple source domains. It quantifies Channel State Information (CSI) differences in the subcarrier dimensions as subcarrier-domain styles and instructs the feature extractor to gradually bias the gesture signals by randomizing the subcarrier-domain styles at the feature level. Meanwhile, a domain classifier that shares the same feature extractor is instructed to gradually bias the domain signals by randomizing the gesture features. Then, the adversarial training framework enables the domain classifier to reduce the influence of domain signals on the feature extractor. Extensive experiments have been performed on three gesture datasets with varying amounts of subcarriers from devices with different NICs, including cross-one-factor (such as room, user, location, and orientation) and cross-multi-factor sensing tasks. The results demonstrate that our method considerably increases performance on wireless DG tasks. Our code is available at:https://github.com/LiuSjia/WiSR.

Cross-domain wireless sensing has always been challenging due to the sensitivity of wireless signals to various environmental factors, which we refer to as subdomains. However, current efforts are limited to cross-one-subdomain tasks requiring target domain data for model training or multiple receivers for data collection. Taking common gesture recognition as an application example, we attempt to demonstrate the feasibility of cross-multiple-subdomain wireless sensing in the more challenging domain generalization (DG) setting. It is possible to extract domain-invariant features from one or several source domain(s), thereby avoiding the need for multiple receivers or target domain data. We also propose an intelligent wireless data augmentation technique based on subdomain-guided perturbations, named WiSGP. Specifically, the independent domain model generates perturbations in the direction of the largest subdomain variations. Then, these subdomain-guided perturbations augment the gesture model's input to enable better domain-invariant feature extraction, even when various subdomains interact. Similarly, gesture-guided perturbations augment the domain model's input, resulting in more accurate subdomain-guided perturbations and minimal gesture label changes. Extensive experiments have been conducted on three datasets collected from various NICs. In terms of room, location, orientation, and user subdomains, WiSGP exhibits excellent accuracy, generalizability, and portability for both cross-one-subdomain and cross-multiple-subdomain tasks.

Active speaker detection is a challenging task in audiovisual scenarios, with the aim to detect who is speaking in one or more speaker scenarios. This task has received considerable attention because it is crucial in many applications. Existing studies have attempted to improve the performance by inputting multiple candidate information and designing complex models. Although these methods have achieved excellent performance, their high memory and computational power consumption render their application to resource-limited scenarios difficult. Therefore, in this study, a lightweight active speaker detection architecture is constructed by reducing the number of input candidates, splitting 2D and 3D convolutions for audio-visual feature extraction, and applying gated recurrent units with low computational complexity for cross-modal modeling. Experimental results on the AVA-ActiveSpeaker dataset reveal that the proposed framework achieves competitive mAP performance (94.1% vs. 94.2%), while the resource costs are significantly lower than the state-of-the-art method, particularly in model parameters (1.0M vs. 22.5M, approximately 23×) and FLOPs (0.6G vs. 2.6G, approximately 4×). Additionally, the proposed framework also performs well on the Columbia dataset, thus demonstrating good robustness. The code and model weights are available at https://github.com/Junhua-Liao/Light-ASD.

Recent research has shown that using the 1-D signal arrival angles observed by linear arrays can locate a 3-D source in unique co-ordinates. Current methods to solve this localization problem are based on semidefinite programming (SDP) or gradient-based iteration, which are either computationally demanding or facing divergence or local convergence issues. This paper reformulates the maxi-mum likelihood (ML) estimation of the 3-D localization problem using the factor graph model, where an effective algorithm is designed through message passing. Although iterative, the proposed solution is more robust to measurement noise than the Gauss-Newton (GN) iterative solution, and the complexity is lower than the SDP solution without the need to introduce semidefinite relaxation error. Simulations validate the analytical performance and complexity, and con-firm the superiority on the convergence of the proposed solution.

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This article proposes a physical layer key generation scheme for wireless LAN MIMO systems based on feature fusion autoencoder (FFAEncoder) to address the issue of a high key disagreement rate (KDR). Our approach involves extracting amplitude and phase features separately, fusing them through multiplication operator in a neural network, and using an autoencoder to extract common features. The proposed scheme was evaluated on multiple data sets in different real-world scenarios, and it was found that the transmitter and receiver codeword’s mean squared error (MSE) and mean absolute error (MAE) were smaller than those of the current models, indicating better key generation performance. Additionally, the proposed scheme’s KDR was smaller, with a decay rate faster than that of the other two models in the same environment, and the primary key bits were 1/2 and 1/3 that of the other models, respectively, as the signal-to-noise ratio (SNR) increased.

Physical-layer secret key generation (PLKG) is a new research area that has emerged in recent years. It is aimed at scenarios where legitimate IoT devices communicate directly, interacting with confidential information for lower overhead and higher security by using wireless channel. When applying it to wireless feature extraction, noise removal is not taken into account in current deep learning networks. To address these problems, the PLKG scheme based on bidirectional convergence feature learning convolutional network (BCFL-based scheme) is proposed, which consists of neural network called BCFL and a new quantization method to achieve better secret key generation. Unlike existing PLKG schemes that enabling both parties to communicate for obtaining higher channel feature similarities, when training it, channel state information (CSI) obtained by channel estimation for two legitimate devices during coherent time is used as inputs; and mean square error (MSE) between two outputs is used as a result of loss function for iterative training. Thus, it can obtain better denoising ability with guaranteed low computational resource consumption, and two legitimate devices can obtain highly correlated channel features. Multiple quantization method is also proposed to address low secret key generation rate (KGR) and low-secret key randomness (KR). The results show that the proposed BCFL-based scheme has a lower MSE than other schemes in different scenarios, indicating that it has better capability to learn channel reciprocity; and secret key error rate (KER) and time consumption are only about 50% of other schemes, which is a significant performance improvement.

With the development of big data and blockchain, an increasing number of scholars have begun to study blockchain for data sharing. By studying data sharing models that are based on blockchain, we find that almost all of them have the following problems: 1) it is difficult to protect the privacy and integrity of users’ data, along with users’ data ownership; 2) the storage burden of blockchain is heavy, and blockchain lacks a mechanism for dealing with data with diverse types and inconsistent formats; and 3) the consensus mechanism has low fairness or low efficiency. Therefore, we propose a data-sharing privacy protection model (DS2PM) that is based on blockchain and a federated learning mechanism for solving these problems. The safety analysis and experimental results show that the DS2PM outperforms the previously established schemes.

Nowadays, Industrial Internet of Things (IIoT) technology has made a great progress and the industrial control systems (ICSs) have been used extensively, which has brought more and more serious information security threats to the ICS at the same time. The authenticated key agreement (AKA) protocol is a common method to ensure the communication security. This work proposes a lightweight AKA protocol based on the elliptic curve cryptography (ECC) algorithm to adapt to the resource-constrained environment. We only employ hash operation, XOR operation, and ECC algorithm to encrypt the data in the authentication and key agreement phase, and avoid involving the register center while proceeding the key agreement, to give consideration to both performance and security. The security analyses indicate that our protocol can meet nine critical security requirements, more than all of the existing protocols, and the performance analysis carried out indicates that our protocol has less computational and communication overheads in contrast to other corelative protocols.

We study the deployment of an unmanned aerial vehicle (UAV) network to provide urgent communications to people trapped in a disaster zone, where each UAV is an aerial base station in the air. Unlike most existing studies that assumed that each user communicates with a UAV directly, we introduce Device-to-Device (D2D) communications, in which a user within the communication range of a UAV can serve as a hotspot (e.g., WiFi hotspot), and provide communication services to his nearby users who are out of the communication range of any UAV. More users thus can have the communication service provided by the UAV network. To ensure that the users within and out of the communication ranges of deployed UAVs havefaircommunication quality, we study a novel UAV deployment and resource allocation problem under the D2D communication model, which is to deploy$K$given UAVs in the top of a disaster zone, allocate the bandwidth of each UAV to its served users, allocate the bandwidth of each hotspot to his served users, determine the data rate of each user, and find the routing paths for data transmissions, such that the accumulative utility of all users is maximized. We also propose a novel$(1-1/e-\epsilon)$-approximation algorithmalgMaxUtilityfor the problem, where$e$is the base of the natural logarithm, and$\epsilon $is a given constant with$0 < \epsilon < 1-1/e$. We finally evaluate the performance of the algorithm. Experimental results show that accumulative utility by the algorithm is up to 18% larger than those by existing algorithms. In addition, more than 16% users are served in the deployed UAV network by the proposed algorithm.

The signal emitted by an acoustic source may be propagating in an environment in which the speed is not known, such as in solid or ocean. Localization of such a source through observing the signal by a number of sensors requires joint estimation with the propagation speed. This work applies the nullspace projection approach to the pseudo-linear formulation for the localization problem to obtain a closed-form solution, which is refined by error-compensation to reach the final estimation. In contrast to the methods from the literature that are either suboptimal or computationally demanding, the proposed method is both statistically and computationally efficient, and is shown analytically to achieve the Cramér-Rao Lower Bound accuracy.

Advanced metering infrastructure (AMI) is a vital component of the smart grid (SG) for real-time data access and bidirectional communication. An authentication and key agreement (AKA) protocol is needed for AMI systems to ensure the confidentiality and integrity of communication data. Since the devices are connected to the open network and generally deployed outdoors with limited computation, communication, and storage, designing a suitable AKA protocol is a challenging task. Researchers are still looking for good ways to make the SG secure and efficient simultaneously. To remedy the situation, in this article, we advance a security-enhanced elliptic-curve-cryptography-based AKA protocol, and the security has been proven rigorously under the random oracle model and verified with the ProVerif tool. Furthermore, performance comparison validates the proposed protocol in affording improved security features with lower computation and communication cost. In addition, the proposed scheme is implemented practically on a testbed, which is deployed usingRaspberry Pi 3 Model B+for smart meters.

The popularity of video social platforms (TikTok, etc.) shows that video is a popular information carrier at present. However, shot occlusion frequently occurs when people are shooting videos to record information. Since the shot occlusion seriously affects the viewers’ experience, the video editors need to find and delete such segments from the video material during post-processing. However, finding the shot occlusion from the video is a time-consuming and laborious task. To reduce the workload of editors, previous researchers proposed a shot occlusion detection algorithm using deep learning technology, which has promotion space in both recognition accuracy and computational efficiency. In this paper, we propose a neural network module, named SAT module, which can effectively extract spatio-temporal information with fewer parameters. We apply SAT module to construct a novel occlusion detection model, and improve the existing occlusion detection loss function for model training. The experimental results on the public dataset show that our method achieves the state-of-the-art performance of 88.25% accuracy and FPS of 130 with the least parameters. Code and models will be available at https://github.com/Junhua-Liao/ICASSP22-OcclusionDetection.

Optical camera communication (OCC) enabled by LED and embedded cameras has attracted extensive attention, thanks to its rich spectrum availability and ready deployability. However, the close interactions between OCC and the indoor spaces have created two major challenges. On one hand, the stripe pattern incurred by OCC may greatly damage the accuracy of image-based object recognition. On the other hand, the patterns inherent to indoor spaces can significantly degrade the decoding performance of reflected OCC. To this end, we propose CORE-Lens as a pipeline to make the mutual interference transparent to existing OR and OCC algorithms. Essentially, CORE-Lens treats the two challenges as two sides of a signal mixture issue: the signals transmitted by OCC get mixed with background images so well that their features become entangled. Consequently, CORE-Lens exploits the idea of disentangled representation learning to separate the mixed signals in the feature space: while the GAN-reconstructed clean background images are used to perform object recognition, OCC decoding is conducted on the residual of the original image after subtracting the reconstructed background. Our extensive experiments on evaluating the real-life performance of CORE-Lens evidently demonstrate its superiority over conventional approaches.

With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data. First, we integrate the incentives based on reputation and payment and introduce “credibility coins” as a cryptocurrency for data-sharing transactions, to encourage users to participate honestly in the data-sharing process based on federated learning. Second, we propose a dynamic incentive model based on the evolutionary game theory to model the game process of users in data sharing and analyze the stability of their strategies. Finally, based on the results of this analysis, we use the blockchain-based smart contract technology to dynamically adjust the participation benefits of users under different conditions in order to promote users to join consortium blockchains more often and steadily to participate in model training for federated learning and obtain better model accuracy. Our work is the first to apply the evolutionary game theory to the study of incentives in federated learning, and plays a leading role in the study of incentives in federated learning. Experimental simulation validation shows that our DIM-DS model can adequately motivate users to participate in the collaborative task of data sharing and maintain stability. The model can maximize the effectiveness of the federated learning model.

Mobile-edge computing (MEC) has been garnering considerable level of interests by processing computation tasks nearby mobile devices (MDs). With limited computation and communication resources and strict task deadline, balancing the energy consumption and time delay of computational tasks will be highly focused. MDs deployed energy harvesting (EH) modules can always provide service to continuous task requests, and finer-grained offloading schemes of the MEC system will significantly affect the time delay of computation tasks. However, when combined them together, the energy causal constraint and the coupling between offloading ratios and resources allocation will cause new challenges for the computation offloading problem. To address these issues, we investigate the partial computation offloading schemes for multiple MDs enabled by harvested energy in MEC. Specifically, we build models for two computing modes and EH process. Subsequently, we formulate a nonconvex optimization problem by minimizing the energy consumption of all the MDs while satisfying the constraint of time delay. Furthermore, we propose and design a novel algorithm based on the Lyapunov optimization to achieve optimal solution, that is, Lyapunov-optimization-based partial computation offloading for multiuser (LOMUCO). Then, we take the long-term average energy consumption and the discarding ratio of computation tasks as the quantitative metrics and conduct extended simulation experiments to confirm the performance of LOMUCO. Finally, compared to several baseline or state-of-the-art algorithms, including local computing all (LCA), offloading computing all (OCA), randomly partial computation offloading (RPCO), and Lyapunov-optimization-based dynamic computation offloading (LODCO), we can demonstrate the superiority of LOMUCO.

Locating a device is a basic element for many Internet of Things (IoT) applications. In particular, it often demands an algorithm having low complexity to limit the energy consumption and most important, sufficient robustness without knowing the device in the near-field for point localization or in the far-field for direction of arrival (DOA) estimation. This article proposes a new localization algorithm that can achieve the two purposes, with the theoretical analysis to validate the optimal accuracy and the real data experiment to support the promising performance. The first objective is achieved by a closed-form solution and the second is accomplished by using the modified polar representation (MPR) of the source position, based on a new formulation for the localization problem. While the MPR localization method has been introduced before, it is not sufficiently robust for IoT application to handle the large equal radius (LER) scenario or the presence of sensor position errors. The proposed algorithm uses a different MPR formulation, which is able to handle the LER scenario, sensor position errors, and has low computational complexity.

Currently, available contactless continuous authentication (CA) techniques depend on physiological biometrics to identify individuals at long intervals through complex feature extraction, resulting in poor accuracy, high computation costs, and security vacuums during lengthy intervals. To address these issues, we propose WiPT, a WiFi-based contactless CA system that utilizes contextual features and behavioral biometrics to optimize contactless CA technology. Specifically, we designed a low-computation two-step user state detection (TUSD) mechanism that continuously monitors user states in real-time. It locks the system when the registered user leaves and allows user authentication only when the departing user returns. Therefore, it eliminates pointless periodic re-authentication and results in considerably shorter monitoring intervals while significantly reducing computation. Subsequently, benefiting from contextual features, WiPT can identify individuals using more detectable behavioral biometrics. We built a one-class classification model based on the Convolutional Autoencoder to automatically extract rich representations of WiFi signals associated with postural transition movements, resulting in lower authentication delay, higher accuracy, and anti-interference. WiPT was implemented by the widely available 802.11n devices and has been extensively evaluated with typical sit-to-stand postural transitions. WiPT achieves an average accuracy of 96.63% in authentication and 99.78% in defense across 30 subjects with an authentication delay of 5.59 milliseconds and a monitoring interval of 2 seconds. They are 4.87% and 5.03% more accurate and dozens of times less time-consuming than existing WiFi-based CA solutions.

Visible light communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer CoLight aiming to boost the data rate of the VLC system purely built upon COTS devices. CoLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, CoLight interfaces a COTS PD with a smartphone through the audio jack, and it also has an alternative MCU-driven circuit to emulate a future integration into the phone. The evaluations on CoLight are both promising and informative: they demonstrate a throughput up to 80 kbps at a distance of 2 m, while suggesting various potentials to further enhance the performance.

Videos have become the new preference comparing with images in recent years. However, during the recording of videos, the cameras are inevitably occluded by some objects or persons that pass through the cameras, which would highly increase the workload of video editors for searching out such occlusions. In this paper, for releasing the burden of video editors, a frame-level video occlusion detection method is proposed, which is a fundamental component of automatic video editing. The proposed method enhances the extraction of spatial-temporal information based on C3D yet only using around half amount of parameters, with an occlusion correction algorithm for correcting the prediction results. In addition, a novel loss function is proposed to better extract the characterization of occlusion and improve the detection performance. For performance evaluation, this paper builds a new large scale dataset, containing 1,000 video segments from seven different real-world scenarios, which could be available at: https://junhua-liao.github.io/Occlusion-Detection/. All occlusions in video segments are annotated frame by frame with bounding-boxes so that the dataset could be utilized in both frame-level occlusion detection and precise occlusion location. The experimental results illustrate that the proposed method could achieve good performance on video occlusion detection compared with the state-of-the-art approaches. To the best of our knowledge, this is the first study which focuses on occlusion detection for automatic video editing.

The Internet of Things (IoT) has become more popular over the past decade. For the IoT to be successful, it is vital to track the location of these things (sensors or actuators). In this article, based on a new IoT underlying architecture, a narrowband-IoT (NB-IoT)-aided, bounding-based iterative and range-free method, named BBIL, is proposed to localize things. We make use of the location information of all anchor regular nodes that can help improve the localization accuracy as much as possible. Specifically, not only single-hop and multihop anchor things but also single-hop and multihop regular things are used for localization. In addition, the communication and computational loads of the local network are greatly decreased because the anchor things can directly access the Internet using the NB-IoT modules; hence, data can be sent to the Internet through a small number of hops in the local network and processed in the cloud/edge computing facilities in a centralized way. To balance the location accuracy and energy consumption of BBIL, we propose a theoretical model to obtain the optimal number of the anchor things. BBIL is evaluated and compared with the existing methods. The simulation results indicate that the average localization error of BBIL is less than 11.6%. Also, it performs well in anisotropic networks. In addition, we verified the validity of our method in real-world scenario.

Neighbor discovery is a prerequisite procedure in communication among energy-limited Internet-of-Things (IoT) devices. Discovering neighbors should be achieved in an energy-efficient way. Communication between heterogeneous IoT devices is a common phenomenon for achieving extensive connections among IoT devices. Generally, existing asymmetric neighbor discovery methods are evolved from the symmetric methods and lack specialized designs for heterogeneous IoT devices which led to relatively poor energy-efficient performance. In this article, we use a generalized slot model using pure sending (PS) slot and pure listening (PL) slot, called PSPL, to convert existing neighbor discovery algorithms (symmetric and asymmetric) to highly energy-efficient asymmetric ones. The core idea of PSPL is that to achieve highly energy-efficient one-way discovery, PS slot and PL slot are only used in devices with smaller and larger energy budgets, respectively, and the length of PL slot is much larger than that of PS slot. In this way, the energy efficiency of asymmetric neighbor discovery can be substantially improved. After implementing one-way discovery, two-way discovery can be easily achieved with mutual assistance. Two examples of how to convert existing algorithms into highly efficient asymmetric ones are presented, the one is to convert Disco to Disco+PSPL and the other one is to convert Griassdi to Griassdi+PSPL. The theoretical analysis results indicate that with the same energy budget, Disco+PSPL and Griassdi+PSPL reduce the worst case latency bounds by up to 82.28% and 87.69%, respectively, compared with the best known asymmetric solutions. The simulation evaluation verifies the effectiveness of our designs.

Visible Light Communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer SynLight aiming to significantly improve the data rate of a practical VLC system. SynLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, SynLight interfaces a COTS Photo-Diode with a smartphone through the audio jack. The evaluations on SynLight are both promising and informative: they demonstrate a throughput up to 60 kbps, more than 50× of that achieved by state-of-the-art systems, while suggesting various potentials to further enhance the performance.

Neighbor discovery, meaning that a node receives other nodes' radio frequency (RF) signals to be aware of their existence, is an indispensable procedure in Internet of Things (IoT)-oriented peer-to-peer (P2P) networks with energy-limited nodes. The main objective of neighbor discovery methods is to improve the energy efficiency, since node energy is usually very limited. As the neighborship maintaining time is very short in the mobile networks, neighbor discovery should be achieved in a very energy-efficient manner. The existing neighbor discovery methods only consider the received RF signals from other nodes (or neighbor table information derived from the received RF signals) as the basis of neighborship evaluation. The neighborship evaluation is inaccurate when the single source of neighbor information is employed. Inaccurate neighborship causes incorrect active slot scheduling and low energy efficiency of neighbor discovery. This paper proposes a generalized and probabilistic neighborship evaluation model to unify a variety of neighborship information in IoT-oriented P2P networks into neighborship probability values. Based on the model, we propose an energy-efficient neighbor discovery middle-ware algorithm by carefully replanning active slots of nodes according to the neighborship probability. The simulation evaluation results show that our proposed method decreases the average discovery delay by up to 11.46%, approximately, compared with other methods at the same energy budget.

Wireless sensor networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pairwise based, in which discovery is passively achieved when two nodes are prescheduled to wake up at the same time. In contrast, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Since proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pairwise one. Our testbed experiments using 40 sensor nodes and extensive simulations confirm the theoretical analysis, showing one order of magnitude reduction in discovery delay compared with legacy pairwise methods in dense, uniformly distributed sensor networks with at most 8.8 percent increase in energy consumption.

In recent years, microblog systems such as Twitter and Sina Weibo have averaged multimillion active users. On the other hand, the microblog system has become a new means of rumor-spreading platform. In this paper, we investigate the machine-learning-based rumor identification approaches. We observed that feature design and selection has a stronger impact on the rumor identification accuracy than the selection of machine-learning algorithms. Meanwhile, the rumor publishers' behavior may diverge from normal users', and a rumor post may have different responses from a normal post. However, mass behavior on rumor posts has not been explored adequately. Hence, we investigate rumor identification schemes by applying five new features based on users' behaviors, and combine the new features with the existing well-proved effective user behavior-based features, such as followers' comments and reposting, to predict whether a microblog post is a rumor. Experiment results on real-world data from Sina Weibo demonstrate the efficacy and efficiency of our proposed method and features. From the experiments, we conclude that the rumor detection based on mass behaviors is more effective than the detection based on microblogs' inherent features.

For the purpose of combination of the basic and academic frontier knowledge by the application of the theory of constructivist learning in order to trace the rapid development of microelectronics, Academic frontier-based approach (AFA) is presented. The learner-centered instructional approach is valuable for promoting active learning by involving learners in learning academic frontier topics in an open-ended and collaborative environment. The design and implementation enrich the teaching modes and the content of the key curriculums in depth. It is effective in achieving positive and higher cognitive goals.

Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5%~8.8% increase in energy consumption.

Based on spatiotemporal properties of neighborhood and mobile properties of nodes in the networks, we propose Selective Reference Mechanism to trade off between the delay and overhead of neighbor discovery in low-duty-cycle WSNs. Extensive simulation and test-bed experiment confirm our theoretical analysis, showing as much as 35.4% increase in discovery probability, 38.6% reduction in discovery delay and 27.7% reduction in total energy consumption.