Existing monostatic integrated sensing and communication (ISAC) systems face challenges in realizing high-precision sensing due to a singular observation angle. This drives the exploration of cooperative sensing ISAC modes. However, communication and sensing heavily rely on line-of-sight (LoS) links, and the issue of obstacle blocking remains unavoidable even with collaborative efforts among multiple base stations (BSs). Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), can create virtual links in full space by dynamically adjusting its element coefficients. This effectively mitigates the pathloss caused by blockages. Thus, integrating STAR-RIS into cooperative ISAC networks holds substantial potential, which has not been widely investigated. In this study, a STAR-RIS-aided cooperative sensing framework is first proposed. To maximize the sensing signal-to-clutter-plus-noise ratio (SCNR), we jointly optimize the transmit beamforming at BS, transmission and reflection coefficients at STAR-RIS, and receive beamforming at multi-receivers. Also, the receivers selection is considered. To solve this nonconvex problem, we propose a joint optimization algorithm using alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR) based on the receivers selection mechanism, called the RS-AFS algorithm. Numerical results validate that the RS-AFS algorithm can significantly enhance the sensing SCNR.

Low earth orbit satellite communication has lower latency and is more suitable for real time communication than high orbit and medium orbit satellites, secure networking is crucial for providing continuous services to low earth orbit satellites. This paper proposes a method for dynamic secure networking of low earth orbit satellites, the method is based on signcryption elliptic curve algorithm in ISO/IEC 29150:2011. The biggest contribution is the combination of certificateless public key mechanism and signcryption algorithm to support secure networking of satellites. Finally, Scyther was used to conduct security analysis on the algorithm and process, it has been proven that the certificateless public key signcryption system can effectively solve the problems of authentication and secure transmission between satellites.

Federated learning (FL) is a privacy-enhanced distributed machine learning framework, in which multiple clients collaboratively train a global model by exchanging their model updates without sharing local private data. However, the adversary can use gradient inversion attacks to reveal the clients’ privacy from the shared model updates. Previous attacks assume the adversary can infer the local learning rate of each client, while we observe that: (1) using the uniformly distributed random local learning rates does not incur much accuracy loss of the global model, and (2) personalizing local learning rates can mitigate the drift issue which is caused by non-IID (identically and in-dependently distributed) data. Moreover, we theoretically derive a convergence guarantee to FedAvg with uniformly perturbed local learning rates. Therefore, by perturbing the learning rate of each client with random noise, we propose a learning rate perturbation (LRP) defense against gradient inversion attacks. Specifically, for classification tasks, we adapt LPR to ada-LPR by personalizing the expectation of each local learning rate. The experiments show that our defenses can well enhance privacy preservation against existing gradient inversion attacks, and LRP outperforms 5 baseline defenses against a state-of-the-art gradient inversion attack. In addition, our defenses only incur minor ac-curacy reductions (less than 0.5%) of the global model. So they are effective in real applications.

It is well known that the future network will achieve seamless global coverage, and the convergence of satellite and terrestrial networks is the basic feature of the next generation network evolution(6G) in a complementary way that satellite network has global coverage and disaster resistance while terrestrial network has large-scale base stations and perfect coverage capability. Meanwhile, the openness of satellite wireless channels, the time variability of satellite networking, and the heterogeneity of the converged network makes the converged network face unprecedented security challenges. This paper analyzes the new security risks introduced due to the inherent converged network features, designs a promising method which is named NAKA for satellite terminal access satellite network to facilitate authentication unification, and wireless security negotiation protection process to solve the authentication and security initialization in the access process from a security perspective.

With the rapidly increasing data traffic in vehicle-to-infrastructure (V2I) networks, the demand for communication and computing rises proportionally. As such, joint communication, computing and sensing becomes crucial for addressing the demands in V2I networks. This paper proposes an energy-efficient joint communication, sensing and mobile edge computing (MEC) system for V2I networks. In the proposed system, the roadside unit (RSU) is equipped with the functions of communication, sensing, and cache-aided computing. By combining beam prediction and tracking, the optimization problem of system energy consumption is formulated. The problem is divided into three parts, which are solved using the joint communication, sensing and MEC online (JCSMO) strategy. Simulation results show that the proposed JCSMO strategy outperforms the baseline schemes in system energy consumption.

This document proposes a method of addressing and routing for massive Low Earth Orbit (LEO) satellite constellations. It is based on the concepts and technologies of New IP. LEO satellite networks can be used for Internet access and for the Non-Terrestrial Network (NTN) integration with 5G and beyond. Our new addressing method leverages the characteristics of satellites with known orbit elements; and simplifies the satellite identification by using limited indexes. The new routing method combines semantic addressing with source routing and generates a new semantic routing scheme. Compared with traditional methods, the new proposal dramatically reduces the workload in satellite, such as table size, TCAM (Ternary Content-addressable Memory) lookups, and packet header size. Thus, it is more suitable to network in space where the harsh environment will restrict the hardware performance, power consumption, link bandwidth and system complexity.

Knowledge distillation has proven to be an effective model compression method that exploits the knowledge from a teacher model for supervising a student model by minimizing the distribution difference between the knowledge and the prediction produced by the student model. In this work, we focus on improving its performance from the perspective of divergence measures. A general form representing a family of divergence measures is introduced and a novel learning paradigm that jointly optimizes multiple measures is proposed by formalizing it as a multi-objective learning problem. Conditioned on Pareto optimality, the weights of different divergences are tuned in an automated way during training. Extensive experiments on multiple datasets show the proposed method can significantly improve the performance of student networks compared to the state-of-the-art methods for knowledge distillation. Codes are available at https://github.com/CML00/MoDiv.

With the process of China's entering a serious aging society, it generally becomes one of the problems for people caring as elderly falls frequently. Thus, it is necessary for us find a way that the old people get help timely when it happened. On studies related to falls in outdoors currently, popular methods are on Computer Vision and Inertial Sensor. The problems of these researches are focus on the fall phenomenon of elderly mostly, lacking of deep excavation and analysis of experimental data. This paper is bringing forward Ë(() Set theory that means `Changing Set', which forms a dynamic set of the interrelated elements with FR (Frame of reference), D(t) (Experimental numerical set), T (Computing method set) and P (Results reasoning analysis set). We study the reasoning algorithmic using Ë(t) Set with normalized component data (both the angular velocity and acceleration from Inertial Sensor), to define feature points of the Ë(t) Set and determine the automatic alarm status of the fall time of the elderly.

This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market.