The development of Large AI Models (LAMs) for wireless communications, particularly for complex tasks like spectrum sensing, is critically dependent on the availability of vast, diverse, and realistic datasets. Addressing this need, this paper introduces the ChangShuoRadioData (CSRD) framework, an open-source, modular simulation platform designed for generating large-scale synthetic radio frequency (RF) data. CSRD simulates the end-to-end transmission and reception process, incorporating an extensive range of modulation schemes (100 types, including analog, digital, OFDM, and OTFS), configurable channel models featuring both statistical fading and site-specific ray tracing using OpenStreetMap data, and detailed modeling of realistic RF front-end impairments for various antenna configurations (SISO/MISO/MIMO). Using this framework, we characterize CSRD2025, a substantial dataset benchmark comprising over 25,000,000 frames (approx. 200TB), which is approximately 10,000 times larger than the widely used RML2018 dataset. CSRD2025 offers unprecedented signal diversity and complexity, specifically engineered to bridge the Sim2Real gap. Furthermore, we provide processing pipelines to convert IQ data into spectrograms annotated in COCO format, facilitating object detection approaches for time-frequency signal analysis. The dataset specification includes standardized 8:1:1 training, validation, and test splits (via frame indices) to ensure reproducible research. The CSRD framework is released at https://github.com/Singingkettle/ChangShuoRadioData1The dataset is designed to be fully reproducible using the provided framework, configurations, and configurable fixed random seeds to accelerate the advancement of AI-driven spectrum sensing and management.

The growing demands for high-precision sensing and ultra-reliable low-latency communications in Internet of Vehicles (IoV) networks, coupled with increasingly congested spectrum resources, have driven integrated sensing and communication (ISAC) technologies toward millimeter-wave (mmWave) frequency bands. However, apart from the inherent openness of wireless channels, this transition exposes critical security gaps that the conventional physical layer security methods featured by communication interference struggle to mitigate: the cross-domain coupling interference between communication and sensing subsystems among vehicles, forming an emergent threat landscape in ISAC-IoV networks. Based on the fact that conventional forward-facing vehicular mmWave radars are typically equipped with horizontally oriented narrow beam, and have limitations in vertical resolution due to the utilization of 1D horizontally arranged antenna arrays, in this paper, we propose a 3D communication-2D sensing (Com3DSen2D) enabled spatial separation and interference decoupling framework. Furthermore, under the proposed Com3DSen2Dframework, to make a balance between sensing accuracy, communication reliability and security, we formulate a non-convex optimization problem of secrecy rate maximization through jointly optimizing 3D-BF design of communication subsystems and radar sensing power allocation of sensing subsystems, subject to the expected levels of sensing accuracy and communication reliability constraints. Experimental evaluations demonstrate that our joint optimization approach achieves significant performance gains over individual optimization benchmarks, yielding 25.3% and 57.8% improvements in secrecy rate compared to isolated 3D-BF optimization and radar sensing power allocation, respectively. Moreover, experimental results obtained from the hardware platform further validate the effectiveness of our proposed framework. This work establishes a comprehensive solution for coupling interference management and security enhancement in next-generation ISAC-IoV networks.

Integrated sensing and communication (ISAC) has been recognized as a key technology in sixth-generation wireless networks, and the additional spatial degrees of freedom obtained by movable antenna (MA) technology can significantly improve the performance of ISAC systems. This paper considers an ISAC-assisted vehicle-to-infrastructure (V2I) network, where extended kalman filter-based prediction is combined with real-time optimization to jointly optimize transmit antenna positions and beamforming and power allocation vectors in dynamic environments. We propose two algorithms: a preprocessing-schur complement-projected gradient ascent algorithm for scenarios without sensing quality of service (QoS) constraints, which explores the potential range of sensing performance to provide reference and warm-starting for subsequent constrained optimization; and a heuristic reflective projected dynamic particle swarm optimization algorithm for sensing QoS-constrained scenarios, which achieves substantial performance gains under non-convex constraints with a small number of iterations. Simulation results demonstrate that these approaches enhance both the communication sum-rate and the lower of the Cram´er-Rao lower bound of motion parameter estimation, validating the effectiveness of MA-assisted beamforming in dynamic V2I ISAC networks.

Movable antenna (MA) technology provides a promising avenue for actively shaping wireless channels through dynamic antenna positioning, thereby enabling electromagnetic radiation reconstruction to enhance physical layer security (PLS). However, its practical deployment is hindered by two major challenges: the high computational complexity of real-time optimization and acritical temporal mismatch between slow mechanical movement and rapid channel variations. Although data-driven methods have been introduced to alleviate online optimization burdens, they are still constrained by suboptimal training labels derived from conventional solvers or high sample complexity in reinforcement learning. More importantly, existing learning-based approaches often overlook communication-specific domain knowledge—particularly the asymmetric roles and adversarial interactions between legitimate users and eavesdroppers, which are fundamental to PLS. To address these issues, this paper reformulates the MA positioning problem as a predictive task and introduces RoleAware-MAPP, a novel Transformer-based framework that incorporates domain knowledge through three key components: role-aware embeddings that model user-specific intentions, physics-informed semantic features that encapsulate channel propagation characteristics, and a composite loss function that strategically prioritizes secrecy performance over mere geometric accuracy. Extensive simulations under 3GPP-compliant scenarios show that RoleAware-MAPP achieves an average secrecy rate of 0.3606 bps/Hz and a Secrecy Performance Coverage Probability (SPSC) of 79.26%,outperforming the state-of-the-art predictive baseline by 35.5% and 6.73 percentage points, respectively, while maintaining robust performance across diverse user velocities and noise conditions.

Mobile Edge CrowdSensing (MECS) based on Federated Learning (FL) has attracted widespread attention as an intelligent data collection and processing approach. FL trains the global model through aggregating local models of participants without requiring the exchange of raw data. However, directly sharing local models is vulnerable to backdoor attacks launched by adversaries. What's worse, malicious server may collude with participants to manipulate the parameter updating of models and even compromise the accuracy of whole system. To address these challenges, this paper proposes a Privacy-Enhanced Federated Learning (PEFL) framework for MECS with the aim of resisting both backdoor and collusion attacks. In PEFL, a Backdoor Resistant Privacy-Enhanced Aggregation (BRPEA) mechanism with the Differential Privacy-Enhanced Exponential (DPEE) method is developed to perturb local models of participants. Clustering and clipping techniques are also designed in BRPEA to effectively distinguish backdoor models from the benign local models, which eliminate the influence of local models deviation and optimize the noise introduced by differential privacy. Furthermore, a Collusion Resistant Privacy-Preserving Aggregation (CRPEA) mechanism is studied. CRPEA can avoid the collusion between servers and participants and prevent the privacy of local models from being leaked. The theoretical analysis proves the security of proposed PEFL framework and the simulation experiments demonstrate that PEFL can not only ensure the aggregation accuracy of encrypted models but provide robustness against both backdoor and collusion attacks.

The task offloading technology plays a vital role in the Internet of Vehicles (IoV) by satisfying diversified vehicular demands, such as energy consumption and processing delay of computing tasks. Unlike the current related works, which not only ignored wireless interference when making information exchange, but also overlooked the available resources of parked and moving vehicles, this paper proposes a comprehensive solution. First, we model vehicle speed using a truncated Gaussian distri bution, replacing simplistic average speed models in prior studies. Wireless interference in V2V/V2I communications significantly impacts communication quality and reliability, leading to packet loss, increased latency, and reduced throughput. For instance, in high-density traffic scenarios, interference can disrupt com munication links, hindering effective task offloading. Next, by incorporating wireless interference and effective communication duration in V2V and RSUs, we propose an analytical framework for task offloading that jointly optimizes energy consumption and processing delay, leveraging resources from parked/moving vehicles and RSUs. Furthermore, inspired by the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), we design an Interference-Aware Multi-Agent Deep Deterministic Policy Gradient (IA-MADDPG) algorithm. The algorithm ensures resource load balancing while reducing energy consumption and latency, and improves the task offloading completion rate. Simulations validate the effectiveness of IA-MADDPG, demonstrating supe rior convergence speed, energy efficiency, and latency reduction compared to existing methods.

The evolution of industrial intelligent manufacturing necessitates wireless communication systems capable of replacing conventional wired infrastructures, offering superior flexibility, scalability, and reduced maintenance overhead. While 5 G New Radio (NR) Ultra-Reliable Low-Latency Communication (uRLLC) standards (Release 15-17) have shown promise for mission-critical applications, current implementations remain constrained by their unidirectional optimization paradigm, unable to simultaneously satisfy the dual imperatives of sub-millisecond latency ($\lt 1$ms) and 99.9999% reliability demanded by industrial control systems. To address these challenges, we present a transformative subband full-duplex (SBFD) network architecture that ensures persistent time-domain spectral availability for concurrent uplink/downlink operations, thereby eliminating direction-switching latency. Our solution introduces three key innovations: (1) an interference-aware SBFD resource allocation framework that strategically isolates UL/DL subbands to minimize cross-link interference (CLI), (2) a dual-optimization algorithm that jointly maximizes spectral efficiency while guaranteeing channel-adaptive reliability thresholds, and (3) a practical implementation scheme compatible with existing 5G NR physical layer specifications. Extensive simulations under realistic factory channel models demonstrate 58.3% reduction in aggregate CLI and 41.2% improvement in control command decoding accuracy compared to legacy half-duplex systems. This research establishes a new paradigm for wireless industrial networks, effectively closing the performance gap between 5G URLLC specifications and the exacting demands of Industry 4.0 applications.

With the rapid development of the low-altitude economy, the widespread deployment of unmanned aerial vehicles (UAVs) necessitates effective sensing technologies for airspace monitoring. Integrated sensing and communication (ISAC) enables mobile communication base stations (BSs) to function as sensing nodes, providing a promising solution for UAV detection. Accurate identification of UAVs often relies on the extraction of distinctive micro-Doppler signatures generated by their rotating blades. However, in urban environments, these signatures are frequently obscured due to low signal-to-noise ratio (SNR) and strong dynamic interference from vehicles, pedestrians, and, in particular, birds. To address this challenge, this paper proposes a robust micro-Doppler feature extraction method based on multicarrier integration and a rotor micro-Doppler null space pursuit (rmD-NSP) algorithm. In one real-world scenario, both a bird and a UAV appeared within the same range cell, with the UAV’s micro-Doppler signals heavily masked by the bird’s strong reflections. After applying the proposed algorithm, distinct micro-Doppler features are successfully extracted, revealing approximately eight rotor blade flashes of the UAV within a 0.1s interval and two wingbeat cycles of the bird within the 0.5s observation window.

The phase shift information (PSI) overhead poses a critical challenge to enabling real-time intelligent reflecting surface (IRS)-assisted wireless systems, particularly under dynamic and resource-constrained conditions. In this paper, we propose a lightweight PSI compression framework, termed meta-learning-driven compression and reconstruction network (MCRNet). By leveraging a few-shot adaptation strategy via model-agnostic meta-learning (MAML), MCRNet enables rapid generalization across diverse IRS configurations with minimal retraining overhead. Furthermore, a novel depthwise convolutional gating (DWCG) module is incorporated into the decoder to achieve adaptive local feature modulation with low computational cost, significantly improving decoding efficiency. Extensive simulations demonstrate that MCRNet achieves competitive normalized mean square error performance compared to state-of-the-art baselines across various compression ratios, while substantially reducing model size and inference latency. These results validate the effectiveness of the proposed asymmetric architecture and highlight the practical scalability and real-time applicability of MCRNet for dynamic IRS-assisted wireless deployments.

Physical layer security (PLS) technology based on the fixed-position antenna (FPA) has attracted widespread attention. Due to the fixed feature of the antennas, current FPA-based PLS schemes cannot fully utilize the spatial degree of freedom, and thus a weaken secure gain in the desired/undesired direction may exist. Different from the concept of FPA, movable antenna (MA) is a novel technology that reconfigures the wireless channels and enhances the corresponding capacity through the flexible movement of antennas on a minor scale. MA-empowered PLS enjoys huge potential and deserves further investigation. In this paper, for the first time, we investigate the secrecy performance of MA-enabled PLS system where a MA-based base station (BS) transmits the confidential information to multiple single-antenna Bobs, in the presence of the single-antenna eavesdropper (Eve) with no location information, by jointly optimizing the beamforming and antenna positions at the BS. Furthermore, the non-convex optimization problem can be solved via the methods of projected gradient ascent and alternating optimization. Also, and simulated annealing is adopted to find a high-quality feasible solution. Simulation results demonstrate the effectiveness and correctness of the proposed method. In particular, MA-enabled PLS scheme can significantly enhance the secrecy rate compared to the conventional FPA-based ones for different settings of key system parameters.

Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, different from current physical layer security methods, which depends on the additional communication interference costing extra power resources, in this paper, we investigate a novel physical layer security solution, under which the inherent radar sensing interference of the vehicles is utilized to secure wireless communications. To measure the performance of physical layer security methods in ISAC-based IoV systems, we first define an improved security performance metric called by transmission reliability and sensing accuracy based secrecy rate (TRSA_SR), and derive closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), success ranging probability (SRP) for evaluating transmission reliability, security and sensing accuracy, respectively. Furthermore, we formulate an optimization problem to maximize the TRSA_SR by utilizing radar sensing interference and joint design of the communication duration, transmission power and straight trajectory of the legitimate transmitter. Finally, the non-convex feature of formulated problem is solved through the problem decomposition and alternating optimization. Simulations indicate that the sensing interference utilization, combined with joint design of transmission power and straight trajectory of the transmitter, achieves a secrecy rate of 3.92bps/Hz for different noise powers for the case of perfect channel state information (CSI). The proposed method maintains robustness, achieving a 60.17% improvement of TRSA_SR under unavailable CSI and location information of the Eve.

This paper investigates the joint active transceiver and passive beamforming design to maximize the weighted sum-rate (WSR) of an IRS-aided multi-streams multiuser multiple-input multiple-output broadcast channel (MIMO-BC) downlink transmission system. Due to the coupling of the transceiver parameters, the considered WSR optimization problem is highly non-convex and thus challenging to solve. Different from the normally used methods, such as the weighted minimum mean-square error (WMMSE), we rely on the matrix fractional programming (MFP) theory to derive an effective algorithm to the WSR problem. Specifically, we reformulate the original problem into a tractable one by exploiting the special structure of the objective function, i.e., a MFP which involves a matrix ratio inside a logarithm in the objective function. An alternating optimization (AO) framework is then devised to decompose the reformulated problem into four subproblems, which optimize the introduced auxiliary variable, the transmit beamforming matrix, the receive matrix, and the reflecting beamforming matrix by fixing other variables respectively. Through the matrix quadratic transform, we reformulate the MFP problem as a convex one, and thus obtain the optimal transmit beamforming matrix. By leveraging the optimality conditions for unconstrained optimization problems, the optimal receive beamforming matrix and the introduced auxiliary variable are derived in closed form. For solving the passive beamforming subproblem, we propose an iterative algorithm based on successive convex approximation (SCA). Since the computational complexity of SCA is relatively high, we propose a computationally efficient method based on manifold optimization (MO) to optimize the passive beamforming matrix. Finally, we also consider the robust beamforming design when the system suffers from imperfect CSI. Simulation results demonstrate the effectiveness of the proposed methods.

Task offloading technology plays a crucial role in the Internet of Vehicles (IoV) by minimizing processing delays through the joint optimization of heterogeneous computing resources supported by vehicles, roadside units (RSUs), and macro base stations (MBSs). Previous works have often ignored the wireless interference during the exchange and sharing of task data. Additionally, the potential for vehicles with similar driving behaviors to form vehicle platooning (VEH-PLA) and effectively integrate individual vehicle resources has not been adequately addressed. Furthermore, as a novel resource management paradigm, VEH-PLA should consider task categorization since vehicles within a VEH-PLA may have identical task offloading requestsan aspect that has also received insufficient attention. In this paper, considering wireless interference, vehicle mobility, VEH-PLA, and task categorization, we propose four task offloading models aimed at minimizing processing delays. By utilizing centralized training and decentralized execution (CTDE) based on multi-agent deep reinforcement learning (MADRL), we present a task offloading decision-making method to find the global optimal offloading decision. This results in significant enhancements in resource load balancing and reductions in processing delays. Finally, simulations validate that the proposed method significantly outperforms traditional task offloading approaches in terms of minimizing processing delays while maintaining balanced resource utilization.

Mobile Crowd-Sensing (MCS) has emerged as a significant approach in various domains for collecting and disseminating sensing data. However, it is a challenging issue to select appropriate participants for a sensing task. This paper introduces a Quality Awareness Incentive Mechanism based on Taxation and Data Aggregation (QIM-TDA), which can choose the reliable participants without losing the platform utility and the data quality. Firstly, we define a reputation model to measure the reliability of participant and select more reliable participants based on their reputation in order to maximize platform utility. Secondly, a truth discovery algorithm is proposed to aggregate the sensing data and ensure the data quality of MCS. Finally, a normalized taxation mechanism is discussed in order to prevent the excessive accumulation of reputation for participant and further enhance the data quality. The simulation results prove that QIM-TDA can significantly improve the data quality and task completion rate compared to some typical mechanisms.

The emergence of connected autonomous vehicles (CAVs) has transformed the realms of transportation and communications. Meeting the demands of future CAVs networks requires the seamless integration of two essential functions: communications and sensing. However, in dense CAVs scenarios, meeting the demands for one-to-one beam-CAV services become challenging due to limited spatial freedom and severe beams interference. In this paper, a novel sensing-assisted CAV s clustering and beams alignment method is proposed. Based on the echo signal, the kinematic parameters are measured, and the extended Kalman filter (EKF) is designed for angle tracking. On this basis, a CAV s clustering method is also proposed. Simulation results verify superior tracking performance compared to other methods. The root mean square error (RMSE) is less than 0.03 and the sum rates can be improved by up to 4 times.

Ultra-reliable and low latency (uRLLC) plays an important role in the context of automatic driving, in terms of the road safety, which mainly depends on fast data transmission and precise positioning. However, handling this data and positioning efficiently faces a significant challenge, because of high data volume and vehicles' mobility. To support the high rate of sensing data and positioning information sharing between vehicles and base stations, mmWave communication technology, featured by high frequency and bandwidth, is identified as an potential solution. In addition, constrained by the strong directionality of mmWave, to improve the performance of high reliability, low latency and precise sensing, realtime mmWave beam direction regulation is urgently needed. In this paper, based on the multi-source sensing information, we propose a joint design of fast and robust communication and sensing 3D mmWave beam tracking (MSI-ISACBT) algorithm for two kinds of dynamic motions. In detail, for the case of low dynamic motion, the beam direction can be updated by using historical state information and reflected echo signal strength, while it can be adjusted according to the image information acquired by the camera for the case of high dynamic motion. Simulations validate the effectiveness and correctness of the proposed MSI-ISACBT. Compared with other popular methods, MSI-ISACBT achieve a stable throughput of 2.8Gbps and more smooth beam angle control with 16ms delay.

Data aggregation (DA) plays an important role in the context of Internet of Things (IoT). Although some favorable solutions have been proposed to improve the performances of DA, the complex collusion attacks are often ignored and may produce more serious negative impact on aggregation accuracy. In this article, we design a novel dynamic robust iterative filtering (DRIF) mechanism to enhance the quality of service of IoT applications and improve the vulnerability of DA to the collusion attack. First, the initial reputations based on the maximum likelihood estimation are assigned to sensor nodes in order to resist the collusion attack. Second, the sensor nodes obtain the aggregation result through iterative filtering so as to ensure the accuracy of DA. Especially, a weight updating scheme is proposed to eliminate the negative effect of the accidental anomaly or collusion nodes. Finally, the simulation study indicates that the proposed DRIF mechanism is effective and it can achieve a higher accuracy in the presence of complex dynamic collusion attacks.

Link scheduling plays a pivotal role in accommodating stringent reliability and latency requirements. In this paper, we focus on the availability and effectiveness of applying protocol interference model (PIM) under Rayleigh fading model to solve the problem. The motivation is that PIM caters to distributed link scheduling algorithm design, but usually lead to irrationality due to its localization behavior. While Rayleigh fading model can accurately describe the inherent characteristic of wireless signal propagation, but the features of global interference and channel fading make algorithm design more challenging. To be specific, we first remove the effect of channel fading on algorithmic design by establishing the relationship between Rayleigh fading model and non-fading model. We then propose a centralized once link elimination (OLE) algorithm by utilizing local nature of PIM, and achieve its distributed implementation based on the message delivery with time complexity of$O(\Delta _{\max }\ln \Delta _{\max })$, where$\Delta _{\max }$is the maximum number of nodes around a given node inside some range. Furthermore, based on random contention resolution, we design another distributed algorithm to schedule all the links within$O(\Delta ^{3}_{\max }\ln \Delta _{\max })$rounds. Simulations show that the PIM is of great confidence as same as Rayleigh fading model, and the proposed algorithms outperform three popular link scheduling algorithms.

Radio frequency fingerprint identification (RFFI) is a physical layer security methodology to recognize individual devices by leveraging hardware imperfections inevitably induced in the manufacturing process. However, the performance degradation caused by the time-varying channel impacts and interferences has severely restricted the development of RFFI. To this end, we present a channel-agnostic RFFI system, which consists of three modules, i.e., signal preprocessing module, feature extraction module, and classification module. In the signal preprocessing module, we first propose a novel approach, referred to as limiter-based spectral circular shift bidirectional division (LB-SCSBD), to generate two parallel spectral quotient (SQ) sequences. Then, we define the spectral quotient constellation (SQC) symbols according to different modulation formats, and thereby transform the SQ sequences into four magnitude-based sequences in terms of two channel-robust signal representations, i.e., the SQ magnitude (SQM) and SQC error vector magnitude (SQC-EVM). In the feature extraction module, we present a moment-based statistical feature extractor (MB-SFE) to extract the device-specific information from the above four sequences. In the classification module, the extracted statistics are fed into the multi-class support vector machine (SVM) for training and testing. We take WiFi as a case study and evaluate the performance of the proposed RFFI system by classifying eight simulated device models and six universal software radio peripheral (USRP) transmitter radios. Experimental results show that (i) the proposed method achieves the accuracies of 99.84% and 98.26% with eight devices in QPSK and 16QAM cases, as well as the accuracy of 92.42% with six USRP devices (ii) the proposed method exhibits superior classification performance in comparison to some existing RFFI methods, leading to a significant accuracy improvement of at least 38.33%.

Airborne tactical networks (ATN) play a pivotal role in enabling information sharing between manned and unmanned military aircrafts. The design of effective ATNs faces two significant challenges: the network ossification problem and the complexity associated with managing heterogeneous resources. Wireless network virtualization provides a practical solution for the first challenge by abstracting, isolating, and sharing wireless resources among different entities. Flexible and scalable virtual request embedding (VRE) algorithms have the potential ability to address the other challenge. However, existing VRE algorithms are not suitable for the virtualization of an ATN because they do not adequately consider key factors such as global interference, reliability and delay-sensitive information sharing in the air-battlefield context. In this paper, we propose an analytical framework of joint wireless network virtualization and resource allocation in the context of an ATN. This framework ensures coordination between physical node and link resources for the VRE. Based on the proposed framework, we design a centralized embedding mechanism that maps available physical resources to served users by constructing a directed resource topology and designing wireless link scheduling algorithms. Furthermore, we design two VRE algorithms that account for two types of delay sensitivity: transmission time and waiting time, depending on whether virtual requests are split or not. Through simulations, we validate the effectiveness of our algorithms and analyze the impact of key system parameters on the delay performance.

Ultra-reliable and low-latency communication (uRLLC) plays an important role in the development of 5G-advanced and 6G wireless networks. Combining unmanned aerial vehicles (UAVs) with non-orthogonal multiple access (NOMA) offers a promising solution to achieve improved reliability and lower latency. This is made possible by enabling line-of-sight (LoS) links and concurrent transmissions through the use of UAVs and NOMA, respectively. However, because of the inherent openness of wireless channel, uRLLC faces the security challenges against being eavesdropped. Physical Layer Security (PLS) has been proposed as an efficient method to secure uRLLC, since it uses only the properties of wireless channels (such as fading, interference, and noise). Although the potential benefits of NOMA-UAV provide a better coverage for ground users, it remains a significant challenge since it may provide a LoS link to eavesdroppers. Therefore, in this paper, we investigate the security and reliability performance of UAV and NOMA based uRLLC scenario, under which UAV serves two different types of users with different needs, i.e., secret users and public users. By using stochastic geometry tools, we derive the closed-form expression of the secrecy rate, an important metric in the study of PLS. Additionally, the secure performance is enhanced by maximizing the secrecy rate through optimizing the hovering height and power assignment of UAV. It should be noted that the hovering position is optimized via power allocation when there is only one secret user. Evaluations demonstrate the effectiveness and correctness of our theoretical analysis.

Internet of Things (IoT) is rapidly spreading and reaching a multitude of different domains, since the fifth generation (5G) wireless technologies are the key enablers of many IoT applications. It is hence apparent that the broadcast nature of IoT devices makes data security unprecedentedly critical. Compared with traditional cryptography algorithms, which cannot cater for the features of IoT devices characterized by the severe limits in terms of energy, computation and storage capabilities, physical layer security (PLS) has been regarded as a promising solution to facilitate secure communications by exploiting the intrinsic randomness of the wireless medium. However, most of previous works assumed that all devices are static, and the impact of mobility on PLS deserves further investigation. In this paper, applying two types of random mobile models, i.e., the models of Random WayPoint (RWP) and Random Direction (RD), we study the impact of mobility on PLS in a scenario with three types of wireless devices (i.e., a destination, multiple interferers and an eavesdropper). Specifically, we establish an analytical framework for secrecy transmission capacity (STC), a fundamental metric in the study of PLS, under RWP and RD models. To the best of our knowledge, this is the first paper to derive STC and present the condition to achieve a positive STC with the consideration of mobility. We conclude that the RWP mobile destination can achieve a higher STC than that achievable in RD mobile and static scenarios, while RWP mobile eavesdropper is a challenging scenario to obtain a positive STC. Therefore, we propose an effective secrecy improvement strategy for the latter. Simulation validates the theoretical analyses.

The application of digital tracking mechanism introduces a series of leakage problems of users' personal sensitive information related to the trajectory. Therefore, we propose a secure storage scheme for trajectory data. Firstly, four-dimensional spatiotemporal clustering of the trajectory data is performed to reduce the spatiotemporal complexity of data storage. Secondly, the privacy level of the trajectory data in the clusters is measured individually, which ensures the needs for personalized privacy protection are met. Finally, a noise trajectory (NTR) tree based on differential privacy is constructed, and the allocation of privacy budget and noise addition are optimized. Extensive simulations show that our scheme improves in terms of time efficiency, and achieves a flexible and effective balance between data accuracy and privacy.

Due to the openness nature of wireless medium, security issues have increasingly become a bottleneck that restricts the development of ultra-reliable and low-latency communications (uRLLCs). Physical layer security (PLS) technique has been proposed to fulfill the security and confidentiality of information transmission by exploiting the characteristics of the wireless channel, which caters to the features of uRLLC. Furthermore, PLS also shows great practicality in artificial intelligence field, especially in wireless intelligent networks. However, the previous works on the study of PLS ignored the significance of mobility and limited packet length constraint required by uRLLC for satisfying low latency, in this paper, we investigate the impact of mobility on the secrecy performance of uRLLC by using the Random WayPoint (RWP) model and the Random Direction (RD) model. Specifically, with the tools of stochastic geometry, to observe the impact of key system parameters on the secrecy performance, we establish the closed-form expression of connection outage probability, secrecy outage probability, and decoding error probability-based secrecy transmission capacity (DEP-STC). Furthermore, we derive the condition that achieves a positive DEP-STC under two moving models, which can offer the network designer some greatly significant insights into achieving perfect secrecy. Simulations validate our derived theoretical results, and indicate that RWP moving receiver can obtain a higher security level than RD moving one, while RWP eavesdropper can lead to a lower security.

This paper studies joint beamforming problems for an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system, and the goal is to maximize the sum rate by jointly optimizing the transmit beamforming vectors at BSs and the reflective beamforming vector at the IRS, subject to the individual maximum transmit power constraints at BSs, and the reflection constraints at the IRS. Due to the formulated optimization problem is highly non-convex, we propose an alternating optimization (AO) algorithm based on successive convex approximation (SCA) such that the transmit and reflective beamforming vectors can be optimized alternately. We further consider the SINR balancing beamforming design scheme by maximizing the minimum SINR among all users to enhance the fairness among users, in which the transmit and reflective beamforming vectors are optimized in an alternating manner. The transmit beamforming vectors are optimized by the second-order-cone programming (SOCP) based on bisection method and the reflective beamforming vector is updated based on the technique of semidefinite relaxation (SDR). Simulation results show that the two proposed algorithms considerably outperform the benchmark zero-forcing (ZF) scheme. Moreover, the AO algorithm based on SCA has good communication performance than the other two schemes. And the AO algorithm based on bisection search guarantees the fairness for all users.

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of$O(\ln n)$, where$n$is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of$\Theta (\ln n)$. Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule.

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity$O(n\ln n)$, where$n$is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of$O(\ln n)$is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

This paper analyzes the secrecy transmission capacity (STC) over wireless ad hoc networks, where the legitimate nodes and eavesdroppers are distributed as Poisson point process. We calculate the connection outage probability (COP) and the bounds of the secrecy outage probability (SOP) based on the tools from stochastic geometry and establish a theoretical model for STC. Different from previous work considering the lower bound of STC, we characterize the relationship between target outage constraints and the densities of legitimate transmitters and eavesdroppers, and propose a necessary condition to achieve an upper bound of the positive STC. That is, the length of legitimate transmitter-receiver pair has an upper bound.

Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users' QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price.