This paper proposes a joint secure sensing and communication framework for full-link covert transmissions, which integrates an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system and compliant distributed cooperative jammers to enhance the communication quality of legitimate users while promoting the efficient utilization of limited resources. In the proposed scheme, upon sensing potential eavesdropper embodied by unmanned aerial vehicle (UAV), the dual-functional base station (BS) covertly transmits the acquired UAV state information to friendly jammers within relevant coverage area and issues activation commands promptly. Simultaneously, with IRS assistance, reconfigurable parameters such as signal phase in NOMA transmissions are adjusted to satisfy public user’s service requirements while facilitating covert communications for legitimate user. To ensure dynamic adaptability and link sustainability, the BS leverages historical sensing data to predict the UAV’s flight trajectory in real time and infer its movement intent. If the UAV exhibits a tendency to deviate from the currently effective jamming zone, the BS proactively activates friendly jammers in adjacent regions to maintain covert transmission rates and ensure robust system operation. To address the non-convex optimization challenge arising from jointly optimizing sensing beamforming, communication beamforming, and the IRS reflection matrix with highly coupled variables, we disassemble the problem into three subproblems. Correspondingly, an alternating optimization framework is designed by employing the semidefinite relaxation (SDR), Gaussian randomization, penalty-based methods, and Dinkelbach transformation to jointly maximize covert transmission rates while guaranteeing both sensing accuracy and communication quality of service (QoS). Simulation results demonstrate that the proposed scheme achieves superior covert transmission rates compared with benchmark schemes. Moreover, the dual-covertness mechanisms for sensing and communication further enhance the system security, validating the framework’s robustness in dynamic resource-constrained environments.

Security and reliability have always evolved alongside advancements in communication technologies. Facing imminent the sixth generation of mobile communication (6G) era, this work explores a jamming exploitation-enabled covert transmission framework for satellite-aerial-terrestrial integrated networks (SATINs), aiming to meet user privacy requirements in future complex adversarial scenarios characterized by stereoscopic coverage and multi-domain collaboration. The research scenario involves a three-dimensional space comprising four core elements: a satellite, an unmanned aerial vehicle (UAV) equipped with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (active STAR-RIS), an eavesdropper possessing dual functionalities of jamming and detection, and a ground terminal. Upon sensing malicious jamming from the eavesdropper, the UAV aerial platform serving as a relay node utilizes its on-board active STAR-RIS to achieve the targeted reflection and manipulation of the malicious jamming signals while concurrently facilitating the effective forwarding of the legitimate signals. Namely, breaking through the conventional mindset of “jamming suppression”, it equivalently constructs a “self-interference loop” centered on the eavesdropper, thereby degrading the adversary’s detection sensitivity. For this process, we construct a covert analysis framework featuring the joint design of the static/dynamic scenarios and the active STAR-RIS reflection-transmission matrices dominated by the UAV’s limited power, derive the analytical expression of the Kullback-Leibler (KL) divergence, and establish rigorous covertness constraints for the system. To address the highly coupled non-convex problem in the joint optimization,we propose a solution combining semidefinite relaxation (SDR), Dinkelbach transformation, Gaussian randomization, and the proximal policy optimization (PPO) framework, maximizing the system covert transmission rate while satisfying various constraints. Numerical results demonstrate that, compared with benchmark schemes, the proposed scheme exhibits superior flexibility and covert transmission advantages in adversarial environments.

Privacy security constitutes a critical challenge in low-altitude wireless communications. Motivated by the application requirements for stereoscopic coverage and multi-domain collaboration, this paper investigates a friendly jamming-assisted air-ground cooperative covert transmission scheme. In the considered system, an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) serves as a network hub. It relays confidential signals from an aerial hovering platform to ground users while cooperating with terrestrial jammer to realize environment-independent directional jamming. Benefiting from the UAV's relaying functionality, this architecture can significantly enhance the flexibility of the jamming mechanism and the security of the jamming node. With the objective of maximizing the UAV's energy efficiency associated with effective throughput, we formulate a joint optimization problem under strict covertness constraints. To solve this problem, we propose an algorithm that integrates semidefinite relaxation (SDR), the Dinkelbach method, and Gaussian randomization within a double deep Q-network (DDQN) framework. The UAV trajectory, onboard resource, user scheduling and RIS parameters are jointly optimized to simultaneously ensure the communication covertness and transmission performance. Numerical simulation results validate the superiority of the proposed scheme compared to benchmark solutions.

Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.

Autonomous aerial vehicle (AAV) covert communication could further enhance the quality and coverage of covert channels. However, in complex low-altitude environments, the air-to-ground (A2G) links are susceptible to the fading effects, which may degrade the performance of covert communication. In this paper, we investigate the fluid antenna (FA) enabled AAV covert communication, where a AAV equipped with FA serves multiple ground users in the presence of an active Warden. We aim to maximize the minimum average covert rate by jointly optimizing the beamforming vectors, AAV trajectory and fluid antenna positions. First, we derive closed-form expressions for the minimum detection error probability (MDEP) and the optimal detection threshold, accounting for uncertainties in both the noise variance and the self-interference channel coefficient. Secondly, we propose an alternating optimization algorithm subject to the covertness constraint, power constraint, and the Warden’s position uncertainty. Specifically, the original nonconvex problem is decomposed into tractable subproblems via the block coordinate descent, which could be solved successively by successive convex approximation, semidefinite relaxation, and Dinkelbach transformation. What’s more, a low-complexity algorithm is developed for the single-user scenario to improve the practical applicability of the proposed framework. Finally, simulation results validate the effectiveness of the proposed FA-AAV covert communication scheme. Moreover, compared to the fixed position antenna scheme, the FA-AAV could improve the covert performance, especially in strong channel fading environments, which is beneficial for practical application.

This work investigates the problem of finite blocklength covert communications with relay assistance in Internet of Things (IoT) to extend the communications range. We reconstruct the framework for analyzing covert communications under decoded and forwarded protocols based on Willie’s optimal detection method. The analytic expression of Kullback-Leibler (KL) divergence is derived, the upper bound of KL divergence is solved by using the convexity of KL divergence, and the strict covertness constraint of the system is obtained. Meanwhile, to maximize the effective throughput, a covert communication parameter configuration scheme is proposed. Theoretical analysis and simulation results indicate that the compromised relationship of transmit power between Alice and relay nodes, and a reasonable power allocation scheme can enhance the effective throughput of the system.

Full-state estimation with a limited number of sen-sors is a valuable and challenging task in monitoring and con-trolling complex physical systems. Supervised learning methods based on deep neural networks have shown excellent performance by learning the nonlinear mapping from sparse observations to global field. However, The neural network is a black box with weak explanation for physical processes, and the reconstruction performance is limited to the architecture and optimization of neural network. This paper aims to leverage the structure and laws inherent in data to reconstruct the global field by solving optimization problems instead of single network learning. We propose a unified global field reconstruction framework consisting of neural network prediction, proper orthogonal de-composition (POD), and linear optimization problem solving. The deep neural network is first trained to provide referenced global fields, which are combined with exact observations and the reference modes extracted by POD to establish a linear optimization problem. The objective of optimization problem is to superpose POD modes to satisfy the values of observations and referenced fields. The experiments conducted on fluid and thermal field reconstruction problems show that the proposed unified framework can significantly improve the reconstruction accuracy of neural networks and boost the performance of directly solving optimization problems without referenced fields.

For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolutional layer's good image feature extraction ability. However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise. In actual engineering, the noiseless and labeled training data is hardly obtained for the TFR. To solve these two problems, this paper proposes a deep Monte Carlo quantile regression (Deep MC-QR) method for reconstructing the temperature field and quantifying aleatoric uncertainty caused by data noise. On the one hand, the Deep MC-QR method uses physical knowledge to guide the training of CNN. Thereby, the Deep MC-QR method can reconstruct an accurate TFR surrogate model without any labeled training data. On the other hand, the Deep MC-QR method constructs a quantile level image for each input in each training epoch. Then, the trained CNN model can quantify aleatoric uncertainty by quantile level image sampling during the prediction stage. Finally, the effectiveness of the proposed Deep MC-QR method is validated by many experiments, and the influence of data noise on TFR is analyzed.

Low-Earth orbit (LEO) satellites are recognized as one of the most promising infrastructures for realizing global Internet of Things (IoT) services. With the explosive growth of user terminals (UTs) and data traffic, the integration of massive multiple-input multiple-output (mMIMO) techniques and LEO satellite communication systems has been regarded as a novel idea to enhance system capacity and realize global seamless high-speed interconnection. However, obtaining effective downlink channel state information (CSI) and establishing a simple and efficient hybrid beamforming mechanism are challenging tasks due to the limitations of objective factors, such as high dynamic, long delay, and low payload in LEO satellite scenarios. It is embodied in three aspects: 1) the untenable channel reciprocity in time division duplex (TDD) systems; 2) the training feedback costs and feedback delay in frequency division duplex (FDD) systems; and 3) the complex nonconvex optimization process faced by hybrid beamforming design. Driven by the performance advantages of the deep learning (DL) technology to deal with various problems in the field of physical layer communications, this article proposes to use a deep neural network (DNN) to solve the above challenges, and constructs SatCP and SatHB schemes for realizing downlink CSI acquirement and hybrid beamforming design, respectively. By deeply mining the potential correlation of the uplink-downlink channels between LEO satellites and UTs and exploring the mapping relationship between CSI and beamformers, the SatCP can assist LEO satellites to directly predict the future downlink CSI based on the observed uplink CSI with no need for downlink channel estimation, while the SatHB can easily generate the corresponding beamformers based on the downlink CSI predicted by the SatCP without requiring complex optimization. Numerical results demonstrate that the proposed SatCP and SatHB can play an effective auxiliary role in LEO satellite mMIMO communication systems.

With the prevalence of cloud computing, more and more enterprises are migrating applications to cloud infrastructures. Logs are the key to helping customers understand the status of their applications running on the cloud. They are vital for various scenarios, such as service stability assessment, root cause analysis and user activity profiling. Therefore, it is essential to manage the massive amount of logs collected on the cloud and tap their value. Although various log storages have been widely used in the past few decades, it is still a non-trivial problem to design a cost-effective log storage for cloud applications. It faces challenges of heavy write throughput of tens of millions of log records per second, retrieval on PB-level logs and massive hundreds of thousands of tenants. Traditional log processing systems cannot satisfy all these requirements. To address these challenges, we propose the cloud-native log database LogStore. It combines shared-nothing and shared-data architecture, and utilizes highly scalable and low-cost cloud object storage, while overcoming the bandwidth limitations and high latency of using remote storage when writing a large number of logs. We also propose a multi-tenant management method that physically isolates tenant data to ensure compliance and flexible data expiration policies, and uses a novel traffic scheduling algorithm to mitigate the impact of traffic skew and hotspots among tenants. In addition, we design an efficient column index structure LogBlock to support queries with full-text search, and combined several query optimization techniques to reduce query latency on cloud object storage. LogStore has been deployed in Alibaba Cloud on a large scale (more than 500 machines), processing logs of more than 100 GB per second, and has been running stably for more than two years.

\beginabstract The trend in the DBMS market is to migrate to the cloud for elasticity, high availability, and lower costs. The traditional, monolithic database architecture is difficult to meet these requirements. With the development of high-speed network and new memory technologies, disaggregated data center has become a reality: it decouples various components from monolithic servers into separated resource pools (e.g., compute, memory, and storage) and connects them through a high-speed network. The next generation cloud native databases should be designed for disaggregated data centers. In this paper, we describe the novel architecture of \name, which follows thedisaggregation design paradigm: the CPU resource on compute nodes is decoupled from remote memory pool and storage pool. Each resource pool grows or shrinks independently, providing \revon-demand provisoning at multiple dimensions while improving reliability. We also design our system to mitigate the inherent penalty brought by resource disaggregation, and introduce optimizations such as optimistic locking and index awared prefetching. Compared to the architecture that uses local resources, \name achieves better dynamic resource provisioning capabilities and 5.3 times faster failure recovery speed, while achieving comparable performance. \endabstract

Server consolidation is critical for energy efficiency of cloud-scale computing. In production environments like Aliyun, which is one of the largest public cloud platforms in the world, server consolidation has several challenges. First, the widespread use of local storage remarkably increases the migration cost (time). Second, the resource utilization of service instances varies over time, which may result in migration oscillation. Third, server consolidation must follow practical constraints. E.g., instances can only be migrated within a maintenance window, and both the resource utilization and the number of instances on a server are bounded. This paper designs and implements C4, a Cost-Efficient Consolidating Service for Aliyun's Cloud-Scale Computing, which Aliyun uses to consolidate servers. We analyze user pattern, resource utilization, and migration cost in Aliyun, showing that traditional utilization-based consolidation approaches cannot meet the needs in production environments, especially for the local-storage-based computing. This motivates us to propose the migration cost model, by which to select servers with the minimum migration time to release. We use the Worst-Fit heuristic to migrate instances to balance the load. Evaluation shows that C4 achieves cost-efficient, load-balanced, and oscillation-free consolidating service. We describe experience with over one year of C4 production deployment, lessons learned, and areas for future work.