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WiFi-based Human Activity Recognition (HAR) plays a crucial role in achieving device-free Human-Machine Interaction (HMI). Due to the strong dependence of WiFi Channel State Information (CSI) on the target environment, current WiFi-based activity recognition methods often require extensive training data and struggle to adapt to new environments. To enhance the generalization ability of wireless sensing, we propose a cross-domain activity recognition system based on WiFi, Wi-Cro. Wi-Cro utilizes Generative Adversarial Network (GAN) to transform source domain data into target domain data, supplemented by a small number of target domain samples to achieve cross-domain human activity recognition. We design a Subcarrier Dimension Reduction Algorithm (SDRA) to mitigate computational complexity of the system. This algorithm calculates the correlation between different CSI subcarriers and extracts principal components to reduce sample dimensions. Furthermore, we develop a data augmentation method based on GAN to transform source domain data into the target domain, addressing issues related to overfitting and domain adaptation due to insufficient samples. Finally, a metric learning approach is designed to further enhance the cross-domain sensing capability of the Wi-Cro system. Extensive experiments are performed using data collected in real-world environments and two public datasets. The results demonstrate that the Wi-Cro system consistently achieves high accuracy in human activity recognition across different datasets, proving its generalization abilities of cross-domain sensing.

Satellite edge computing, as an extension of ground edge computing, is a key technology for achieving seamless global computing coverage. However, the low earth orbit (LEO) satellites have limited computing resources and are moving at a high speed. This naturally poses a challenge to find more suitable computation offloading strategies with minimum network latency and energy consumption, especially when a large number of co-existing users are to offload their tasks. In this paper, therefore, we mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering LEO satellites’ mobility and SECN's heterogeneous resource constraints to explore more practical computation offloading strategies. We first formulate the problem of Mobility-aware Computation Offloading (MCO) in the SECN via specifying the effect of LEO satellites’ high-speed movement on the computation offloading, aiming to minimize the network latency and energy consumption. Considering the MCO problem is discrete and non-convex as the objective function and constraints are associated with the binary decision variables. We then convert the original non-convex problem into a continuous convex problem which is proved to be feasible. To avoid a high computational complexity incurred by the extensive co-existing user offloading, we design MCO-A, a distributed algorithm based on ADMM (alternating direction method of multipliers) to solve the MCO problem efficiently. Finally, the performance of MCO-A is evaluated via extensive experiments including small-scale and large-scale scenarios. The experimental results show that MCO-A can achieve a lower network latency and energy consumption in an efficient way compared with the baseline and state-of-the-art approaches.

3D tori are significant interconnection architectures in building supercomputers and parallel computing systems. Due to the rapid growth of edge faults and the crucial role of path structures in large-scale distributed systems, fault-tolerant path embedding and correlated issues have drawn widespread researches. However, existing path embedding methods are based on traditional fault models, allowing all faults to be near the same node, so they usually only focus on theoretical proof and generate linear fault-tolerance related to dimension $n$n. In order to improve the fault-tolerance of 3D torus, we first propose a novel conditional fault model called the Locally Faulty Block model (LFB model). On the basis of this model, the Hamiltonian paths with large-scale edge defects in torus are investigated. After that, we construct an Hamiltonian path embedding algorithm HP-LFB into torus with $O(N)$O(N) under the LFB model, where $N$N is the number of nodes in torus. Furthermore, we present an adaptive routing algorithm HoeFA, which is based on the method of distance vector to limit the use of virtual channels (VCs). We also make a comparison with state-of-the-art schemes, indicating that our scheme enhance other comprehensive results. The experiment indicated that HP-LFB can sustain the dynamic degradation of the batting average of establishing Hamiltonian paths, with the added faulty edges exceeding fault-tolerance.

In the Industrial Internet of Things (IIoT), Multi-access Edge Computing (MEC) emerges as a transformative paradigm for managing computation-intensive tasks, where task offloading plays an important role. However, due to the complex environment of IIoT, existing deep reinforcement learning-based schemes suffer from significant shortcomings in accuracy and convergence speed during model training when addressing the issue of task offloading. In this paper, to solve this problem, we propose an online task offloading scheme based on reinforcement learning, leveraging the double deep Q network (DQN) and dueling DQN with a prioritized experience replay mechanism, called the Prioritized experience-based Double Dueling DQN task offloading scheme (P-D3QN). P-D3QN enhances action selection accuracy using double DQN and mitigates Q-value overestimation by decomposing state and advantage using dueling DQN. Additionally, we adopt the prioritized experience replay mechanism to enhance the convergence speed of model training by selecting transitions that induce a higher training error between the evaluation network and the target network. Experimental results demonstrate that P-D3QN outperforms several state-of-the-art schemes, achieving a reduction of 21.0% in the average cost of the task and improving the completion rate of the task by 19.5%.

Integrated Sensing and Communication (ISAC) has emerged as a promising technology for next-generation mobile networks. Towards ISAC, we develop mmRipple that empowers commodity mmWave radars with communication capabilities through smartphone vibrations. In mmRipple, a smartphone (transmitter) sends messages by modulating smartphone vibrations, while a mmWave radar (receiver) receives the messages by detecting and decoding the smartphone vibrations. By doing so, a smartphone user can not only be passively sensed by a mmWave radar, but also actively send messages to the radar without any hardware modifications. Although promising, the data rate of mmRipple is limited by Morse-style communication. To address this, we present mmRipple+, which leverages the Pulse Width and Amplitude Modulation (PWAM) technique and suppresses inter-symbol interference to enable faster communication. We prototype mmRipple and mmRipple+ on commodity mmWave radars and different types of smartphones. Experimental results show that mmRipple achieves an average vibration pattern recognition accuracy of 98.60% within a $ 2$ m communication range, and 97.74% within $ 3$ m. The maximum communication range extends to $ 5$ m. Meanwhile, mmRipple+ achieves a bit rate of 100 bps with a BER of less than 3%, improving the data rate by 4× over mmRippe with the same symbol duration. This work pioneers smartphone-to-COTS mmWave radar communication via vibrations, unlocking diverse applications.

Passive human tracking plays a critical role in the field of ubiquitous sensing, offering customized services such as real-time location tracking for vital sign monitoring and motion detection. Traditional contact-free tracking systems are primarily designed for Line-of-Sight (LOS) scenarios, requiring a direct path between the radio device and the target. However, in Non-Line-of-Sight (NLOS) scenarios, where obstacles obstruct this direct path, these systems suffer from sensing failures and are unable to accurately obtain the motion trajectory of the sensing target. In this paper, we propose the UWTracking system, which utilizes the Commercial Off-the-Shelf (COTS) Impulse Radio-Ultra Wideband (IR-UWB) radars to enable precise indoor passive human tracking in both LOS and NLOS scenarios. To effectively capture the motion information of a moving target in NLOS scenarios, we present the Reconstructed Distributed-Doppler Frequency Shift (RD-DFS) features. We then binarize the RD-DFS features and design the Distance Extraction Algorithm (DEA) to obtain the target's distance in both scenarios. Subsequently, the Circle Intersection Method with Distance Stretching (CIM-DS) algorithm is developed to determine the indoor position of the sensing target, and the Scanning Angle and Velocity Particle Filter (SAV-PF) algorithm facilitates high-precision trajectory tracking. We implement a prototype of UWTracking system and conduct extensive evaluations to showcase its trajectory tracking performance under various scenarios. The results demonstrate that UWTracking achieves effective real-time tracking, with a median tracking error of 17.65 cm in the LOS scenario and 23.34 cm in the NLOS scenario, outperforming the state-of-the-art trajectory tracking systems based on COTS IR-UWB Radars.

In the paper, the Unmanned Aerial Vehicle (UAV) path planning and task offloading problem in UAV-assisted mobile edge computing (MEC) systems is investigated. A bi-criterion ant colony optimization (bi-ACO) framework is proposed for the considered problem with the objectives of minimizing the total cost and the completion time, meanwhile satisfying the energy, deadline, location, and priority constraints. In the bi-ACO framework, multiple heterogeneous colonies are introduced with different preferences of objectives. Each colony maintains five pairs of pheromone matrices for constructing feasible solutions. Besides the colony settings, three key components of bi-ACO are delicately designed: feasible solution generation method (FSGM) to construct a feasible solution, solution division method (SDM) to improve obtained solutions of good quality, and pheromone update method (PUM) to updates pheromone matrices by pheromone evaporation operation and pheromone enhancement operation based on the preferences of colonies. Four Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal outperforms the compared baseline algorithms in effectiveness and robustness.

Traditional indoor location information management methods based on centralized servers have problems such as safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads. These problems have seriously affected the development of personalized services based on indoor location information. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) information management framework for indoor positioning is presented, comprising the mobile blockchain model, the federated learning (FL) model, and the InterPlanetary file storage model. Then, we design the MBFL algorithm, establishing a robust foundation for collaborative model training, efficient block mining, and secure data storage. Moreover, we derive training and mining latency as well as the individual user rewards, and formulate latency-limited resource allocation strategies as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demonstrate that the proposed alternating iterative algorithm achieves rapid convergence and strikes an effective balance between economic and time efficiency. Furthermore, when confronted with model poisoning attacks, the MBFL algorithm exhibits superior security performance compared to the traditional FL algorithm. Future work will focus on adapting the MBFL framework for various indoor environments and enhancing consumption and computational efficiency with hybrid consensus mechanisms.

Contactless WiFi based human action recognition exhibits remarkable prospects in the fields such as human-computer interaction and smart home. However, domain dependency restricts its generalization into the real-world deployment. Since it is expensive to label enough new data for retaining a model, it is beneficial to explore few-shot learning for cross-domain sensing with limited target labels. Nevertheless, there are two challenges to be addressed. The first challenge is how to select a suitable dataset from a series of available source domains to prevent negative transfer. The second is to mine action-related characteristics by the feature learning model for the following effective knowledge transfer. In order to tackle the above challenges, we present a cross-domain sensing framework named CDFi, which consists of Nearest Neighbor based Domain Selector (NNDS) and Fine-to-Coarse-Grained Transformer Network (FCGTN). NNDS is proposed to evaluate the source-target domain similarities by measurements among local and global feature distributions. Besides, FCGTN embeds convolution map based hierarchical transformer structures and the modified linear layer into an end-to-end deep network, which can quickly adapt to the unseen domain by few samples. Comprehensive experiments show that CDFi can effectively realize cross-domain action recognition, and achieve about 4% and 10% improvement on cross-user and cross-scene cases, respectively, compared to the state-of-the-art.

This paper exploits two intrinsic hardware-specific fingerprints in terms of carrier frequency offset (CFO) and phase noise (PHN) to propose a two-dimensional physical layer authentication (PLA) scheme in the unmanned aerial vehicle (UAV)-aided communication systems. By leveraging expectation conditional maximization (ECM), extended Kalman filtering (EKF) algorithms and binary hypothesis testing, we first extract the inherent hardware impairments of UAV-aided systems including CFO and PHN as PHY-layer fingerprints to establish an authentication framework. To accurately characterize authentication performance, we examine the hybrid Cramér-Rao lower bound (HCRLB) for individual estimators of CFO and PHN, and then theoretically derive the analytical expressions for the false alarm and detection probabilities by utilizing tools from statistical signal processing. Finally, extensive numerical results are provided to validate the correctness of the developed theoretical models and to illustrate the authentication performance of the proposed scheme under various system parameters.

Multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novel Availability-aware Revenue-effective Application Deployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is $\mathcal {NP}$NP-hard. Then, we propose an approximation algorithm named ARAD-A to find the ARAD solution efficiently with a constant approximation ratio of $\frac{1}{2}$12. We extensively evaluate the performance of ARAD-A against five representative approaches. Experimental results demonstrate that our ARAD-A can achieve the best performance in securing the app vendor's overall revenue.

In the evolving environment of mobile edge computing (MEC), optimizing system performance to meet the growing demand for low-latency computing services is a top priority. Integrating fluid antenna (FA) technology into MEC networks provides a new approach to address this challenge. This letter proposes an FA-enabled MEC scheme that aims to minimize the total system delay by leveraging the mobility of FA to enhance channel conditions and improve computational offloading efficiency. By establishing an optimization problem focusing on the joint optimization of computation offloading and antenna positioning, we introduce an alternating iterative algorithm based on the interior point method and particle swarm optimization (IPPSO). Numerical results demonstrate the advantages of our proposed scheme compared to traditional fixed antenna positions, showing significant improvements in transmission rates and reductions in delays. The proposed IPPSO algorithm exhibits robust convergence properties, further validating the effectiveness of our method.

Edge-cloud networks face security threats during data collection, data routing, and service construction, resulting in data tampering, stealing, and communication interruption. Trust mechanism can predict data quality and cooperation probability of nodes before purchasing data or establishing cooperation, so as to select trusted participants for data perception and interaction. Howe ver, there are some problems with existing trust methods, such as limited evaluation scope, incomplete trust evidence, and inaccurate evaluation results. To address these issues, a Trust mechanism-based Multi-Tier Computing system (TMTC) is proposed in this article. Specifically, we propose a two-tier trust evaluation model. At the data collection layer, it conducts trust evaluation on data reporters based on data submission and communication interactions. At the network layer, it evaluates trust of routers through path backtracking verification, multi-service analysis and coincident path analysis. Then, based on evaluation results, a differentiated trust detection is initiated for normal and abnormal nodes. And high-frequency detection tasks are initiated for malicious nodes to improve accuracy, sparse detection tasks are initiated for normal nodes to reduce costs. Finally, extensive experiments conducted on the synthetic and real-world datasets demonstrate that, TMTC can resist data tampering and good-bad mouth attacks effectively. And whether in a dense or uniform scene, it outperforms two benchmark methods by increasing malicious node detection rate by 13.37%–21.87% and reducing cost by 18.8%–50.32%.

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users’ operations and thus taking full control over victim devices. Expelliarmus neutralizes users’ touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users’ commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.

CRYSTALS-Kyber, as the only public key encryption (PKE) algorithm selected by the National Institute of Standards and Technology (NIST) in the third round, is considered one of the most promising post-quantum cryptography (PQC) schemes. Lattice-based cryptography uses complex discrete algorithm problems on lattices to build secure encryption and decryption systems to resist attacks from quantum computing. Performance is an important bottleneck affecting the promotion of post quantum cryptography. In this paper, we present a High-performance Implementation of Kyber (named HI-Kyber) on the NVIDIA GPUs, which can increase the key-exchange performance of Kyber to the million-level. Firstly, we propose a lattice-based PQC implementation architecture based on kernel fusion, which can avoid redundant global-memory access operations. Secondly, We optimize and implement the core operations of CRYSTALS-Kyber, including Number Theoretic Transform (NTT), inverse NTT (INTT), pointwise multiplication, etc. Especially for the calculation bottleneck NTT operation, three novel methods are proposed to explore extreme performance: the sliced layer merging (SLM), the sliced depth-first search (SDFS-NTT) and the entire depth-first search (EDFS-NTT), which achieve a speedup of 7.5%, 28.5%, and 41.6% compared to the native implementation. Thirdly, we conduct comprehensive performance experiments with different parallel dimensions based on the above optimization. Finally, our key exchange performance reaches 1,664 kops/s. Specifically, based on the same platform, our HI-Kyber is 3.52× that of the GPU implementation based on the same instruction set and 1.78× that of the state-of-the-art one based on AI-accelerated tensor core.

Current facial authentication (FA) systems are mostly based on the images of human faces, thus suffering from privacy leakage and spoofing attacks. Mainstream systems utilize facial geometry features for spoofing mitigation, but they are still vulnerable to feature manipulation, e.g., 3D-printed human faces. In this article, we propose a novel privacy-preserving anti-spoofing FA system, named RFace, which extracts both the 3D geometry and inner biomaterial features of faces using a COTS RFID tag array. These features are difficult to obtain and forge, hence are resistant to spoofing attacks. Unlike images, RF signals are not perceptible to human eyes, so RFace protects user's privacy. We build a theoretical model to rigorously prove the feasibility of feature acquisition and the correlation between facial features and RF signals. To enhance the security of RFace, we specify the tag reading order for each authentication to defend against the signal replay attack. For practicality, we design an effective algorithm to mitigate the impact of unstable distance and angle deflection from the face to the array. Extensive experiments with 30 participants and three types of spoofing attacks show that RFace achieves an average authentication success rate of over 95.7$\%$% and an EER of 4.4$\%$%. More importantly, no replay attack or spoofing attack succeeds in deceiving RFace in the experiments.

Machine Learning (ML) techniques can facilitate the automation of malicious software (malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, lacking theoretical guarantees and defense effectiveness. In this article, we propose a new adversarial training framework, termed Principled Adversarial Malware Detection (PAD), which offers convergence guarantees for robust optimization methods. PAD lays on a learnable convex measurement that quantifies distribution-wise discrete perturbations to protect malware detectors from adversaries, whereby for smooth detectors, adversarial training can be performed with theoretical treatments. To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD to enhance deep neural network-based measurements and malware detectors. Experimental results on two Android malware datasets demonstrate: (i) the proposed method significantly outperforms the state-of-the-art defenses; (ii) it can harden ML-based malware detection against 27 evasion attacks with detection accuracies greater than 83.45%, at the price of suffering an accuracy decrease smaller than 2.16% in the absence of attacks; (iii) it matches or outperforms many anti-malware scanners in VirusTotal against realistic adversarial malware.

Cooperative jamming (CJ) is a promising technique for enhancing the physical-layer security in wireless powered sensor networks. The secrecy performance of CJ-aided wireless powered sensor networks is affected by three issues including disguised eavesdropper as cooperative node, estimation error of the channel between sink node and each sensor node, and distance-related limitation on the transmit power of cooperative nodes. To address the above issues, this paper proposes a CJ-aided secure communication scheme for wireless powered sensor networks with disguised eavesdropper and imperfect channel estimation. Since each cooperative node could be the disguised eavesdropper, the proposed scheme incorporates all possible cases of the eavesdropper disguising itself as unfixed cooperative jamming node. The maximization of secrecy rate over all possible cases is formulated for designing secrecy optimization problem. The imperfect channel estimation and distance-related jamming power limitation are both integrated as constraints into the secrecy rate maximization problem. Since the original optimization problem is complex and non-convex, a two-level optimization algorithm is proposed to solve it. Simulation results show that the proposed scheme achieves significant secrecy rate improvement over typical existing schemes.

Multi-user delay-constrained scheduling is a crucial challenge in various real-world applications, such as wireless communication, live streaming, and cloud computing. The scheduler must make real-time decisions to guarantee both delay and resource constraints simultaneously, without prior information on system dynamics that can be time-varying and challenging to estimate. Additionally, many practical scenarios suffer from partial observability issues due to sensing noise or hidden correlation. To address these challenges, we propose a deep reinforcement learning (DRL) algorithm called Recurrent Softmax Delayed Deep Double Deterministic Policy Gradient ( $\mathtt {RSD4}$ ) (https://github.com/hupihe/RSD4), which is a data-driven method based on a Partially Observed Markov Decision Process (POMDP) formulation. $\mathtt {RSD4}$ guarantees resource and delay constraints by Lagrangian dual and delay-sensitive queues, respectively. It also efficiently handles partial observability with a memory mechanism enabled by the recurrent neural network (RNN). Moreover, it introduces user-level decomposition and node-level merging to support large-scale multihop scenarios. Extensive experiments on simulated and real-world datasets demonstrate that $\mathtt {RSD4}$ is robust to system dynamics and partially observable environments and achieves superior performance over existing methods.

Recently, simplicial complexes are used in constructions of several infinite families of minimal and optimal linear codes by Hyun et al. Building upon their research, in this paper more linear codes over the ring $\mathbb {Z}_{4}$ are constructed by simplicial complexes. Specifically, the Lee weight distributions of the resulting quaternary codes are determined and two infinite families of four-Lee-weight quaternary codes are obtained. Compared to the databases of $\mathbb Z_{4}$ codes by Aydin et al., at least nine new quaternary codes are found. Thanks to the special structure of the defining sets, we have the ability to determine whether the Gray images of certain obtained quaternary codes are linear or not. This allows us to obtain two infinite families of binary nonlinear codes and one infinite family of binary minimal linear codes. Furthermore, utilizing these minimal binary codes, some secret sharing schemes as a byproduct also are established.

Wireless Power Transfer (WPT) technology has been developed rapidly in recent years. The cooperative charging model and corresponding scheduling methods have been proposed to save the charging cost in paid charging service. However, the state-of-the-art methods ignore the spatial occupation issue of rechargeable devices. Moreover, the cooperative charging scheduling in directional wireless charging has not been studied yet. This paper studies the cooperative scheduling for directional wireless charging with spatial occupation. We formulate the Cooperative Charging Scheduling with Spatial occupation (CCSS) problem of Mobile Rechargeable Sensor Devices (MRSDs) for optimizing the total cost of whole charging system. We first investigate the properties of optimal arrangement of MRSDs in charging group and calculate the tight intervals of charging angles of MRSDs. We show that it is sufficient to bound the error by conducting angle discretization for only two MRSDs in each charging group. Then, a $(\ln n+1)(1+\varepsilon)$(lnn+1)(1+ɛ)-approximation algorithm of the CCSS problem is proposed based on greedy approach, where $n$n is the number of MRSDs, and $\varepsilon$ɛ is the discretization error. The results of extensive simulations and field experiments demonstrate that our algorithm can reduce at most 42.5% total cost comparing with the benchmark algorithms.

In recent years, green concepts have been integrated into the product iterative design in the manufacturing field to address global competition and sustainability issues. However, previous efforts for green material optimal selection disregarded the interaction and fusion among physical entities, virtual models, and users, resulting in distortions and inaccuracies among user, physical entity, and virtual model such as inconsistency among the expected value, predicted simulation value, and actual performance value of evaluation indices. Therefore, this study proposes a digital twin-driven green material optimal selection and evolution method for product iterative design. Firstly, a novel framework is proposed. Subsequently, an analysis is carried out from six perspectives: the digital twin model construction for green material optimal selection, evolution mechanism of the digital twin model, multi-objective prediction and optimization, algorithm design, decision-making, and product function verification. Finally, taking the material selection of a shared bicycle frame as an example, the proposed method was verified by the prediction and iterative optimization of the carbon emission index.

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This article proposes a tag-based approach for physical (PHY)-layer authentication in a reconfigurable intelligent surface (RIS) communication system. We first extract the intrinsic PHY-layer features of RIS communication systems in terms of channel gain and background noise, and then apply these PHY-layer features, a random signal as well as the private key of the transmitter to construct a robust cover tag signal against the impersonation attack. We adopt an asymmetric cryptography technique to encrypt tagged signals and to resist against unauthorized detection and tampering attacks during the transmission process. The receiver then applies the maximum a-posteriori (MAP) ratio test to conduct authentication based on the received tag signal, a reference tag signal transmitted in training phase and the knowledge of distributions of the channel gain, background noise and the random signal. We also provide security analysis to demonstrate how the proposed scheme can resist unauthorized detection, tampering attacks, etc. With the help of tools of the MAP ratio test, maximum likelihood estimation, we further analyze the distribution of the test statistics and derive analytical models for the false alarm and detection probabilities. Finally, extensive simulations are conducted to verify the theoretical results and to illustrate the performance of the proposed scheme.

Data-driven intelligent fault root cause location is important to the reliability and safety of network operation and maintenance. However, the number of fault samples is much greater than the number of root cause samples, resulting in extremely imbalanced data and leading to overfitting problems and weak generalization capabilities. To solve these problems, a new fault root cause location method called the three-layer subnet Wasserstein Generative Adversarial Network-Gradient Penalty (TLS-WGAN-GP) is proposed. To obtain the original features of the root cause data and the potential space data’s distribution, hidden mode, we use the form of the encoder-decoder-encoder three-layer subnet in the generator to generate data. Finally, we merge the generated and original root cause data to train root cause classifiers. By performing classification training on the original dataset, the dataset processed by the typical oversampling technology, the WGAN-GP model synthetic dataset, and the TLS-WGAN-GP synthetic dataset and comparing different classification prediction models, the experimental results show that using the three-layer subnet to generate the data is applicable. TLS-WGAN-GP can increase the F1 score from 95% to 98%, which means that TLS-WGAN-GP can effectively locate the root cause of data-driven network node faults in intelligent operation and maintenance.

Although crowdsensing has emerged as a popular information collection paradigm, its security and privacy vulnerabilities have come to the forefront in recent years. However, one big limitation of previous research is that the security domain and the privacy domain are typically considered separately. Therefore, it is unclear whether the defense methods in the privacy domain will have unexpected impact on the security domain. To bridge this gap, in this paper, we propose a novel Disguise-based Data Poisoning Attack (DDPA) against the differentially private crowdsensing systems empowered with the truth discovery method. Specifically, we propose a novel stealth strategy, i.e., disguising the malicious behavior as privacy behavior, to avoid being detected by truth discovery methods. With this stealth strategy, the shortcoming of failing to maximize the attack effectiveness is avoided naturally through structuring a bi-level optimization problem, which can be solved with the alternating optimization algorithm. Moreover, we show that the differentially private crowdsensing systems are vulnerable to data poisoning attacks, and enhancing the level of privacy will bring more serious security threats. Finally, the evaluation results on the real-world dataset Emotion and the synthetic dataset SynData demonstrate that DDPA can not only achieve maximum utility damage but also remain undetected.

The Industrial Internet of Things (IIoT) is a new network paradigm emerging from the Internet of Things (IoT). It is primarily used in smart factories to promote smart manufacturing and increase productivity. Smart industrial devices collect real-time data from other domains (i.e., intelligent factories) that collaborate on the same transaction and transmit crucial data via a public internet channel. When applied to resource-constrained smart industrial communication devices, this process causes issues in communication security and privacy. Consequently, this study develops a cost-effective authentication protocol for cross-domain data exchange (AP-CDE) in IIoT. Anticollision hash functions, bitwise Exclusive-or operations, and Chebyshev polynomial operations are used in this protocol. AP-CDE security is achieved by using precise formal and nonmathematical (informal) analysis methods. The analysis results show that the developed protocol can withstand various well-known threatening attacks. Finally, a detailed comparative analysis between the AP-CDE and other interrelated protocols is used to verify that the AP-CDE outperforms other protocols in terms of security and functional characteristics, communication overheads, and computational costs.

With the wide deployment of commodity off-the-shelf (COTS) devices, Wi-Fi-based human pose estimation has attracted significant attention in the field of wireless sensing for its ubiquitous availability. Since human activity sensing is human oriented, human pose estimation has become one important infrastructure. In this article, we propose a Wi-Fi-based 2-D static human pose estimation (WiSPE), which acts like a camera to estimate human pose in skeletons. Specifically, in the device deployment, we design a vertical slide rail with receiving antennas to obtain channel state information (CSI) at different heights. Then, we utilize the multiple signal classification algorithm to compute angle of arrival (AoA) and time of flight (ToF) spectrum from the CSI. We process the AoA and ToF spectrums at different heights and construct one novel feature called 2-D AoA image, which captures multipath signal reflections caused by the human body. Furthermore, we propose the environment background filter (Env-Filter) algorithm to create one environment related noise filter from the static environment signals. The Env-Filter algorithm effectively filters out the environment background noise and enhances the human-pose-related information in the 2-D AoA image. Finally, we design a teacher–student Network to correlate skeletons with the 2-D AoA image. The experiment results show that the average accuracy of the WiSPE is up to 95.2% in percentage of correct keypoints (PCK)@50, achieving the same effect as vision-based methods. When the evaluation criterion gets stricter to PCK@10, the average accuracy of the WiSPE still achieves 73.1%, which is better than state-of-the-art Wi-Fi-based methods.

High-speed steel (HSS) rolls modified by different additions of NbC were prepared. The microstructure of as-cast and heat-treated NbC-modified HSS rolls were observed by means of optical microscope (OM), scanning electron microscope (SEM) and energy spectroscopy (EDS), combining with the result of dissolution behavior of NbC particles and interface energy of NbC/γ-Fe calculated by dissolution kinetic and first-principle, the effect of addition of NbC on microstructure of HSS rolls were discussed. Results show that NbC particles do not dissolve in melt under experimental conditions, and the NbC particles even coarsen due to the absorption of alloying elements during the subsequent cooling process. The NbC particles can act as the heterogeneous nucleuses of pre-eutectic austenite to refine the as-cast microstructure of HSS rolls. However, the added amount of NbC particles influence the distribution of NbC. With the increase of the amount of NbC particles, the distribution of NbC particles changes from austenite inter-dendrite region to dendrite. The NbC particles distributed in austenite inter-dendrite region can restrain the movement of solid-liquid interface, and refine the as-cast microstructure. The distribution of NbC within the austenite dendrites enhances the potential of NbC as a non-homogeneous phase nucleus, thereby further refining the microstructure.

BCube is a promising structure of data center network, as it can significantly improve the performance of typical applications. With the expansion of network scale and increasement of complexity, reliability and stability of networks have become more essential. In this paper, we study the fault-tolerant routings in BCube. First, we design a fault-tolerant routing algorithm based on node disjoint multi-paths. The proposed multi-path routing has stronger fault tolerance, since each path has no other common nodes except the source node and the destination node. Second, we investigate an effective fault-tolerant routing based on routing capabilities algorithm for BCube. The proposed algorithm has higher fault tolerance and success rate of finding feasible routes, since it does not limit the faults number. Third, we present an adaptive path finding algorithm for establishing virtual links between any two nodes in BCube, which can shorten the diameter of BCube. Extensive simulation results show that the proposed routing scheme outperforms the existing popular algorithms. Compared with the state-of-the-art fault-tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% improvement on both throughput and packet arrival rate. Meanwhile, it reduces the average latency of 18.6% and the maximum latency of 23.7% in networks.

This experiment investigates the effect of Nb on the oxidation resistance of high-chromium cast iron by adding different contents of Nb to high-chromium cast iron. The experimental results show that the oxidation resistance of high-chromium cast iron with the addition of 1 wt% Nb is increased by 43% compared with high-chromium cast iron without the addition of Nb, which may be related to the formation of a large area of weak oxidation area on the surface of high-chromium cast iron. When more than 2.5 wt% Nb is added to high-chromium cast iron, the oxidation resistance of high-chromium cast iron deteriorates significantly due to a large amount of Fe oxide formed around the Nb2O5. However, as the subsequent Nb content continues to increase to 5 wt%, the oxidation resistance of high-chromium cast iron is improved compared to 2.5 wt% Nb because the dispersion of NbC in the matrix decreases.

Diets comprising selenium-deficient crops have been linked to immune disorders and cardiomyopathy. Selenium nanoparticles (SeNPs) have emerged as a promising nanoplatform for selenium-biofortified agriculture. However, SeNPs fail to reach field-scale applications due to a poor understanding of the fundamental principles of its behavior. Here, we describe the transport, transformation, and bioavailability of SeNPs through a combination of in vivoand in vitroexperiments. We show synthesized amorphous SeNPs, when sprayed onto the leaves of Arabidopsis thaliana, are rapidly biotransformed into selenium(IV), nonspecifically incorporated as selenomethionine (SeMet), and specifically incorporated into two selenium-binding proteins (SBPs). The SBPs identified were linked to stress and reactive oxygen species (mainly H2O2and O2–) reduction, processes that enhance plant growth and primary root elongation. Selenium is transported both upwards and downwards in the plant when SeNPs are sprayed onto the leaves. With the application of Silwet L-77 (a common agrochemical surfactant), selenium distributed throughout the whole plant including the roots, where pristine SeNPs cannot reach. Our results demonstrate that foliar application of SeNPs promotes plant growth without causing nanomaterial accumulation, offering an efficient way to obtain selenium-fortified agriculture.

The multi-hop wireless charging technology can largely extend the charging service range of chargers, thus has promising prospect in sustainable energy replenishment for wireless rechargeable sensor network. This paper proposes a new cost criterion, termed comprehensive cost consisting of energy cost and deployment cost, to measure the actual expenditure of wireless charging. We present a multi-hop wireless charging model and formulate the problem of minimizing the comprehensive cost such that the energy demand of all sensor nodes can be fulfilled by the energy capacitated chargers. We propose a $(\ln n+1)$(lnn+1)-approximation algorithm for the optimization problem, where $n$n is the number of sensor nodes. Then, we propose a straightforward cost sharing mechanism, which ensures that no subset of sensor nodes can benefit by breaking away from the current charging tree for any fixed charger position, to realize the paid charging service of multi-hop wireless charging. Furthermore, to keep the magnetic fields of transmitters from the interfering, the conflict avoidance schemes are proposed in both central and distributed situations. Finally, we discuss the distributed scheme for minimizing the comprehensive cost without support of central server. Through extensive simulations, we demonstrate the significant superiority of the proposed algorithms in terms of comprehensive cost.

The ever-increasing demand for oil & gas exploration from land to subsea necessitates using higher-strength and high-ductility heavy-wall thickness pipes, which may increase the microstructural inhomogeneity along the steel plate sections, ultimately impacting the mechanical properties. In this work, the differences in microstructure and mechanical properties between the through-thickness layers of a heavy-wall ferrite/bainite (F/B) dual-phase (DP) X70 pipeline steel plate were studied. The results showed that the size of the ferrite grain and the volume fraction of bainite increased from the surface layer to the mid-thickness layer of the DP X70 pipeline steel plate. The surface and mid-thickness layers had a lower uniform elongation and higher strength and hardness. In comparison, the quarter layer had higher uniform elongation and lower strength and hardness. The refined grain size and the strong {001}<110> texture were responsible for enhanced in strength and hardness. The higher γ-fiber texture at the quarter layer favored the higher plasticity. The analysis results of strain-hardening behaviors based on the modified C-J model showed that the mid-thickness layer had a higher strain-hardening capacity at the beginning of the plastic strain, leading to deformation instability and reduced material plasticity. The surface layer maintained a high hardening exponent even at high plastic strains, which facilitated the deformation properties of the material. The results provide a basis for evaluating the influence of microstructural factors on the strength and plasticity of heavy-wall X70 pipeline steel and the coordination mechanism of micro deformation in DP steels.

The reaction routes for the CO2hydrogenation to methanol over a series of Cu-Mn-La-Zr catalysts prepared by different methods, viz., CMLZ-CP by co-precipitation, CMLZ-S by sol-gel method and CMLZ-H by hydrothermal method, were comparatively investigated by in-situDRIFT and H2-TPD characterization. The results indicate that the surface hydroxyl groups on these catalysts contribute to the CO2hydrogenation to methanol and the reaction may follow the formate (HCOO*) and carboxylate (COOH*) routes. The carboxylate pathway is preferred for the reaction over the CMLZ-CP and CMLZ-H catalysts, whereas the formate pathway dominates in the reaction over the CMLZ-S catalyst. The CMLZ-CP catalyst shows the strongest ability to activate H2and thus exhibits the highest CO2conversion and methanol yield. In contrast, the CMLZ-H catalyst has high percentage of medium to strong basic sites and oxygen defects, which favor the hydrogenation of intermediate species to methanol, and thus exhibits the highest selectivity to methanol.

A series of Ca-Zr catalysts modified by different transition metals were prepared by the sol-gel method and their catalytic performance in the synthesis of dimethyl carbonate (DMC) from methanol and propylene carbonate (PC) by transesterification at low temperature was investigated. The results indicate that the selectivity to DMC of various transition metal-modified Ca-Zr catalysts follows the order of Co-Ca-Zr > Cu-Ca-Zr > Ca-Zr > Fe-Ca-Zr > Ni-Ca-Zr > Zn-Ca-Zr. For the transesterification over the Co-Ca-Zr catalyst, in particular, the conversion of PC reaches 84.3% with a selectivity of 94.5% to DMC after reaction for 2 h under 35 °C, a methanol/PC molar ratio of 15, and catalyst amount of 4%. Combining with the XRD, FT-IR, XPS and CO2-TPD results, it is revealed that increasing the strength of basic sites can raise the conversion of PC, whereas increasing the density of basic sites leads to a decrease of the selectivity to DMC. As a result, the Co-modified Ca-Zr (Co-Ca-Zr) catalyst, with the lowest density of surface basic sites but the highest fraction of strong basic sites, exhibits a high conversion of PC and a high selectivity to DMC for the transesterification of PC with methanol at a low temperature.

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Blockchain can help edge IoT-enabled Maritime Transportation Systems (MTS) in solving its privacy and security problems. In this paper, a lightweight blockchain called LBlockchainE is designed for edge IoT-enabled MTS to guarantee the security of sensor data stored in an edge computing environment. To save the resources of edge servers on ship, a data placement strategy is proposed. To encourage edge servers to positively contribute to storing data generated by sensor devices, storage resource consumption is employed as an influencing parameter, and servers with abundant resources are selected for priority storage. The data placement strategy also takes care of the access delay between servers and selects the nodes with the least access and storage costs as the priority storage choice. LBlockchainE applies the low-energy-consumption characteristics of Proof of Stake to determine the ownership of bookkeeping rights through a small number of competitive calculations and the resources of the node. Experimental results indicate that compared with Ethereum, the consensus mechanism of LBlockchainE consumes less energy and occupies less storage space. On average, the new system uses 1.6% less time and consumes 78% less battery power compared with traditional blockchain systems. In comparison to the random storage, the best storage, and the optimal data storage strategies, the proposed strategy maintains the same message costs.

Human identification is essential for current intelligent applications. Traditional human identification methods usually require dedicated collection equipment or user cooperation, which limits the application scope of human identification. Recent research works have shown that Wi-Fi signals can support fine-grained sensing for human respiration. In this article, we leverage Wi-Fi signals to collect and recognize a unique physiological feature of humans, i.e., the respiration, for device-free and nonintrusive human identification. We propose a respiration sensing-based human identification system, namely BreathID. BreathID explores the nature of the amplitude and phase data in the CSI ratio model through vector analysis. By jointly using the discrete wavelet transform (DWT) and fake peak removal method, we accurately calculate the respiration rate and extract the breathing feature. We also propose a weighted multidimensional dynamic time warping (WMD-DTW) algorithm to realize the human identification based on multiple input multiple output (MIMO) technology. We implement the BreathID system on commercial Wi-Fi devices. The results show that BreathID can identify 11 users in three typical scenarios in daily life with an average accuracy of 97.52%. In particular, the average absolute error of the respiration rate in BreathID is about 0.1 bpm, which is better than the state-of-the-art algorithms.

GCN-based clustering schemes cannot interactively fuse feature information of nodes and topological structure information of graphs, leading to insufficient accuracy of clustering results. Moreover, the deep clustering model based on graph structure is vulnerable to the attack of adversarial samples leading to the reduced robustness of the model. To solve the above two problems, this paper proposes a robust clustering model based on attention mechanism and graph convolutional network (GCN), named AG-cluster. This model firstly uses graph attention network and GCN to learn the feature information of nodes and the topological structure information of graphs, respectively. Then the representation results of the above two learning modules are interactively fused by the interlayer transfer operator. Finally, the model is trained end-to-end using a self-supervised training module to optimize the clustering results of the model. In particular, an efficient graph purification defense mechanism (GPDM) is designed to resist adversarial attacks on graph data to improve the robustness of the model. Experimental results show that AG-cluster outperforms the other four benchmark methods, specifically, AG-cluster improves 7.6% in Accuracy and 11.5% in NMI compared to the best benchmark method. Besides, the new model still shows higher robustness and stronger transferability under multiple attacks.

With the widespread deployment of commercial wireless devices, researchers begin to focus on device-free sensing tasks. In the field of action perception, existing WiFi-based sensing works mostly follow the framework in which action instances of channel state information (CSI) are first extracted and then classified. As for the part of human action detection, a majority of works adopt threshold based sliding window or frame-by-frame detection methods. However, it is hard for the former approach to set a reasonable threshold for all samples. As for the latter, it costs a relatively substantial amount of labor to label each moment of the time sequences. In order to overcome the above problems, we design an end-to-end context-aware faster region-based convolutional neural networks (RCNN) framework named Wisense to simultaneously detect the temporal boundaries as well as classify the actions. More specifically, Wisense consists of backbone net, region proposal net (RPN), pooling layer, and the prediction net, which directly regresses the action location along the time axis and classifies the action types. For the sake of wireless signal temporal detection, we transform the input into 1-D feature map and extract multiscale 1-D anchors. Besides, in order to sufficiently mine the context information, we extend the boundaries of region proposals and further establish the temporal pyramid features. Experimental results conducted in three indoor scenes validate the effectiveness of our proposed Wisense.

The unmanned aerial vehicle (UAV) enabled communication technology is regarded as an efficient and effective solution to provide emergency data uploading for some special cases where existing cellular infrastructures cannot provide reliable services to large-scale ground users (GUs). In the face of large-scale dynamic networks where the number and location of GUs change all the time, to maximize the throughput of data upload tasks under the premise of meeting the requirements of fairness, QoS and energy consumption, we propose a UAV trajectory planning approach based on deep reinforcement learning in combination with the prediction of GUs' movement trend in future time slots. Simulation results validate that our proposed approach converges more quickly and achieves better performance in dynamic networks.

This paper addresses jamming attack detection issue in a millimeter Wave (mmWave) massive MIMO system under noise uncertainty. Specifically, we apply the generalized likelihood ratio test (GLRT) to develop a one-step GLRT scheme for detecting jamming attack under the homogeneous and partially homogeneous noise environments, and exploit training data to estimate the unknown noise statistical information and replace it with resulting estimation in deriving GLRT procedure to obtain adaptive GLRT detection scheme. We also design a two-step GLRT scheme, where we first assume the unknown noise statistical information is known, and derive GLRT based on test data, and then replace it by the sample covariance matrix based on training data only to achieve a fully adaptive jamming attack detector. With the help of statistical signal subspace analysis and composite hypothesis testing theories, we further examine the statistical distributions of the jamming attack detection schemes and present the closed-form expressions of false alarm and detection probabilities for the proposed schemes under different noise environments. Finally, we implement extensive simulations to validate the theoretical results and evaluate the detection efficiency under various parameters.

We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-user distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mobility level, and recognizes the group structure by applying the multi-dimensional scaling technique on the matrix of distance between pairwise users within the same group. We first validate the performance of between-user distance estimation via conducting extensive experiments in a three-floor campus building and a shopping center, and the results show that WiDE outperforms other machine learning based approaches for between-user distance estimation, with the average absolute error (AAE) of 0.69m and 1.14m for the campus building and shopping center, respectively, and the corridor identification accuracy for the campus building is over 99 percent. In addition, the experiments in the shopping center show that our approach can accurately detect groups, classify group mobility into fine-grained level and recognize the group structure.

The CAN FD emerges as a promising CAN technology inside the ACPS due to its advantages of high data-phase bit-rate and message payload. HSM based security solution is recommended by auto industry to protect CAN FD from potential security attacks, but it induces new challenges on timing analysis of CAN FD messages, which is left open in the literature. This article develops the first security-aware system model to describe the processing of CAN FD messages, and presents a new WCRT analysis to bound the interference induced by security-critical messages. We give the theoretical proof that our WCRT analysis can upper bound the response time of CAN FD messages. Using a small message set, we show that the WCRT computed by our new analysis is only 14% percent higher than the true WCRT obtained from an exhaustive search based simulator. By comparing with existing method, the number of impacted messages increases along with the increasing number of security critical messages, and for the two typical CAN FD systems, the percentage of WCRT increase varies from 12.43% to 14.57% and 7.0% to 10.89%, respectively; the percentage of WCRT decrease varies from 3.29% to 6.04% and 4.13% to 7.93%, respectively.

In-home sleep monitoring is essential for evaluating sleep quality of individuals. Although many sleep monitoring systems have been developed recently, they have limitations in achieving a good performance at low cost. To address this issue, this article proposes a new system based on channel-state information of domestic WiFi network to monitor both turnover activities and breathing rate of sleepers. Unlike recent approaches placing receiving antennas close to each other, scattered placement is adopted to fully exploit spatial diversity of receiving antennas. More importantly, a new error correction method is proposed to accurately recognize turnover activities. Based on the interrelation between consecutive activities, the proposed method can effectively correct the recognition errors of existing methods including convolutional neural network. Then, for accurately estimating breathing rate, both a new subcarrier selection method and a new peak identification method are proposed. Experiment results show that our system can significantly improve the recognition accuracy of eight typical sleep turnover activities and four typical sleep postures. We can achieve the mean accuracy of 94.59% and 95.83% on the recognition of turnover activities and sleep postures, respectively. Besides, our system can also significantly improve the estimation accuracy of breathing rate especially in tough scenarios, such as prone and side-lying positions.

With the increasing scale of parallel computer interconnection network, the possibility of processor failure or link failure between processors in the network is also increasing. In the design of supercomputers, not only link overhead and communication delay should be taken into account, but also fault-tolerant performance of networks should be emphasized. Locally exchanged twisted cube ($LeTQ$LeTQ) is a newly proposed interconnection network with lower link overhead and shorter diameter. With the increasing scale of supercomputers, fault-tolerant routing is indispensable. In this article, we propose a new load balancing fault-tolerant routing algorithm based on node contraction for $LeTQ$LeTQ networks. The proposed algorithm uses the node shrinkage method to evaluate the priority of nodes. The sending node adaptively adjusts the probability of forwarding packets to the neighbor node according to the priority of the neighbor node and the state of the network. The path can be adapted to the load state of the network. The simulation results show that the fault-tolerant routing algorithm has good performance in throughput and delay.

Aging is characterized by a progressive deterioration of physiological integrity, leading to impaired functional ability and ultimately increased susceptibility to death. It is a major risk factor for chronic human diseases, including cardiovascular disease, diabetes, neurological degeneration, and cancer. Therefore, the growing emphasis on “healthy aging” raises a series of important questions in life and social sciences. In recent years, there has been unprecedented progress in aging research, particularly the discovery that the rate of aging is at least partly controlled by evolutionarily conserved genetic pathways and biological processes. In an attempt to bring full-fledged understanding to both the aging process and age-associated diseases, we review the descriptive, conceptual, and interventive aspects of the landscape of aging composed of a number of layers at the cellular, tissue, organ, organ system, and organismal levels.

This paper investigates a simultaneous-transmitting-and-reflecting fully-connected reconfigurable intelligent surface (STAR-FC-RIS) empowered integrated sensing and multiuser communications (ISAMC) network, where a dual-functional radar-communication base station detects a malicious radar target nearby and communicates with multiple legitimate users on the other side of the STAR-RIS. We utilize an integrated architecture that combines the fully-connected (FC)-RIS, an emerging type of beyond-diagonal (BD)-RIS, with the time-switching (TS)-STAR-RIS to enhance both the sensing and communications at the cost of possible target intercepting and propose the simultaneous-transmitting-and-reflecting fully-connected RIS (STAR-FCR) schemes to strike a balance between sensing and communications performance. Thereafter, observing the security-reliability performance tradeoff of the downlink ISAMC, we conduct closed-form analyses to compare COPs of round-robin scheduling (RS) and multiuser scheduling (MS) with the aid of a TS-based STAR-FC-RIS. Furthermore, we derive closed-form expressions of the sensing outage probability, communications outage probability (COP), and communications intercept probability, where an average of the three probabilities is exploited to obtain an optimized time allocation (OTA) of the STAR-FC-RIS. Numerical results verify that the STAR-FCR-MS scheme outperforms the STAR-FCR-RS scheme in terms of sensing reliability and communications security. Moreover, an OTA remarkably enhances the overall performance of the STAR-FCR schemes of ISAMC systems.