This paper develops a new approach of large language model (LLM) enhanced reconfigurable intelligent surface (RIS), in a bid to achieve energy-efficient and reliable communication in 6G Internet of Vehicles (IoV). It is well known that RIS offers an innovative solution to improve signal quality by intelligently adjusting the propagation path of radio waves. However, configuring RIS in the dynamically changing vehicular environment remains a challenge. This study leverages the analytical capabilities of LLM, combined with key IoV data such as channel status, vehicle movement patterns, and quality of service requirements, to model and optimize RIS-based IoV communication systems. The work focuses on constructing a realtime model of the RIS-based IoV wireless transmission system and proposes an optimal strategy for wireless resource allocation. Through comprehensive simulation results, this paper justifies the significant performance advantages of LLM-enhanced RIS, demonstrating a viable technical pathway for the development of 6G IoV.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.

The space–air–ground-integrated network (SAGIN) will greatly promote the development of the Internet of Things (IoT). Green IoT will be an important part of SAGIN. Ambient backscatter communication (AmBC) is a potential solution for green SAGIN IoT. To improve the achievable sum rate (ASR) of the AmBC system, we propose a reconfigurable intelligent surface (RIS)-assisted AmBC system. In the single-backscatter device (BD) AmBC scenario, we first give the phase shifts that maximize the gain of the reflection link of the AmBC system, and then give the optimal reflection coefficient. The proposed scheme does not need to solve the convex semidefinite program (SDP) problem and has the characteristics of low computational complexity. In the multi-BD AmBC scenario, we first propose a multi-BD phase shifts initialization strategy to ensure the stability of the proposed scheme. Then, we give the optimal reflection coefficient and phase shifts based on the iterative method. Simulations show that the RIS-assisted AmBC scheme is superior to the non-RIS-assisted AmBC scheme.

This paper looks into the realization of digital twin (DT) technology in unmanned aerial vehicle (UAV) networks. We propose, in particular, a framework for DT-based UAV applications in which distinct jobs in the digital twin interact with UAVs in the physical world via task manager scheduling. Furthermore, we investigate the use of 3D mmWave Radar imaging on UAVs and apply it to the process of radio frequency (RF) characterizing. After that, the method of 3D ray-tracing is employed to accomplish channel modelling of UAVs, which reflects RF domain digital twin match. Finally, we present numerical results to demonstrate that our developed digital twin platform can provide accurate RF presentation of UAV and therefore accomplish smart operating and administration of the actual UAV network.

This paper presents an overview of intelligent reflection technologies, including backscatter communication (BackCom) and reconfigurable intelligent surfaces (RIS), and their integration in 6G non-terrestrial networks (NTN) for Internet of Things (IoT) applications. The exponential growth in IoT devices necessitates efficient and energy-saving communication mechanisms to enable intelligent collaboration, which these technologies provide. We explore the benefits of RIS and BackCom in 6G NTN IoT environments, showing their potential to facilitate future applications. A case study of an RIS-enhanced bistatic BackCom scheme tailored for 6G NTN IoT is introduced, demonstrating considerable capacity improvements from integrating RIS with BackCom. Simulation results validate the effectiveness of RIS in significantly boosting BackCom performance. Finally, we discuss research opportunities to advance the implementation of intelligent reflection technologies from theoretical concepts to practical 6G NTN IoT solutions, involving optimization of RIS placement, channel estimation, and radio resource management. Our comprehensive analysis underscores intelligent reflection as an indispensable capability in 6G NTN IoT.

With the rapid development of intelligent connected vehicles (ICVs), their intelligent, remote, and high-speed mobility characteristics have led to an expansion of their attack surface, and thus an increase in security requirements. Consequently, security research for ICVs has become a current research focus. However, existing security risk monitoring measures lack linkage with attack behavior, and security defense strategies cannot fully and promptly respond to security risk levels and system vulnerabilities. Therefore, this paper proposes a security evaluation framework for ICVs based on attack chains. The framework focuses on the mechanism and steps of attack generation, evaluates the current risk level and degree of harm in a targeted manner, and maps appropriate security management measures according to the degree of harm. This security evaluation model integrates various defense mechanisms and resolves the mismatch between existing ICV security defense measures and security situations. The effectiveness of this model has been validated through experiments, and potential research directions in the future are discussed and analyzed.