Runhui Zhao

As critical national infrastructure, the reliability and security of wireless communication and environmental sensing under the smart grid system face significant challenges in complex electromagnetic environments. This paper proposes an Integrated Sensing and Communication (ISAC) architecture based on Time-Modulated Metasurface (TMM) to address these challenges. Leveraging TMM’s capability to simultaneously generate multiple controllable harmonics, the system enables highly reliable communication, sensing, and active electronic countermeasure functions on a single hardware platform, applicable to conventional data transmission, Uncrewed Aerial Vehicle (UAV) inspection, and other scenarios. Using a time-modulated reflection with angle-adaptive linear phase, the system achieves deception in both range and Doppler, effectively shielding critical power infrastructure from radar detection. For communication security, TMM is used for directional modulation, focusing signals toward legitimate receivers while radiating jamming signals toward potential eavesdropping directions, thereby achieving physical-layer security. Simulation results demonstrate excellent performance in electronic countermeasure scenarios, providing an innovative solution for the secure operation of critical power facilities in complex electromagnetic environments.

Nowadays, unmanned aerial vehicles (UAVs) have become essential assets for defense operations, providing efficient and precise communication and surveillance capabilities in complex and hostile environments. Artificial intelligence (AI)-enabled jamming poses a critical threat to the security and reliability of UAV communication networks. To address this, we propose an offline-learning anti-jamming framework that leverages advanced algorithms, including deep reinforcement learning (DRL) and decision transformers (DT), to enhance UAV performance in intelligent jamming environments. This work adopts an offline, cloud-based training paradigm for anti-jamming, which learns from historical decision trajectories to handle uncertain and unseen jamming patterns and enable reliable decision making. The proposed framework constrains cumulative reward loss within a predefined bound while improving training stability and reducing the time complexity of DRL-based decision model training. The proposed framework exhibits greater performance of UAVs in controlling AI-enabled jams by showing remarkable results for the performance metrics such as Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), Packet Delivery Ratio (PDR), Jamming Detection Time (JDT), and latency (delay in response). The extensive experimental outcomes illustrate the efficiency of the proposed framework in complex defense operations and uncertain environments for mitigating AI-enabled attacks by counteracting the jamming attempts.

In Internet of Things (IoT) environments, malicious nodes can not only eavesdrop on legitimate communications but also emit jamming signals, which severely undermine secure and reliable communication among nodes. Reconfigurable Intelligent Surfaces (RIS) have emerged as a lightweight and promising technique to mitigate such threats. However, in typical scenarios that simultaneously involve RIS and multiple nodes, existing Deep Reinforcement Learning (DRL)-based methods often suffer from lengthy training procedures and are vulnerable to adversarial perturbations (AP) introduced by malicious nodes. This paper proposes an offline secure and anti-jamming communication decision framework based on a Temporal Convolutional Network (TCN)-based Twin-Efficiency Diffusion Model (TCN-TEDM). The framework effectively alleviates the tendency of traditional offline RL to yield degraded policies under strong jamming due to adversarial perturbations and state missingness. The model reconstructs perturbed states under a limited compute budget, thereby markedly improving decision stability under jamming. Extensive experimental results demonstrate that the proposed framework can efficiently generalize and transfer existing secure-communication and anti-jamming policies without online interaction, achieving superior stability and performance under state-perturbation and missingness scenarios.

In Agricultural Internet of Things (Ag-IoT), a large number of terminal sensor nodes with limited computational power and energy are widely deployed across various production sites to collect real-time data that support precision agriculture decision-making, which makes it challenging to perform complex computing tasks in real time. This article proposes an offline decision-making framework based on a compositional diffusion model that addresses the limitations of traditional offline deep reinforcement learning (DRL), which often struggles to produce high-quality policies in the presence of malicious-node interference and attacks and with insufficient decision trajectories in agricultural settings. Specifically, the proposed scheme leverages the diffusion model’s reverse generative process to synthesize numerous high-quality augmented samples from a small set of existing decision trajectories, which are then used to train robust policies, mitigating performance degradation in offline DRL due to limited trajectories and adversarial state perturbations introduced by malicious nodes. Experimental results demonstrate that the proposed method improves the quality and generalization of task offloading and resource allocation decisions, exhibiting superior stability and performance even under data sparsity and adversarial attacks.

Reconfigurable Intelligent Surface (RIS) technology, closely integrated with Artificial Intelligence (AI) as one of the Physical Layer (PL) technology approaches, is considered an ef-fective solution for enhancing security in industrial Internet of Things (IIoT) networks. Integrating RIS, AI, and heterogeneous IIoT networks presents a promising approach to improving the se-curity of wireless communications in IIoT. This paper discusses new AI-based Physical Layer Authentication (PLA) approaches and how they can be integrated with RIS to enable identity authen-tication and malicious node detection, aiming to provide light-weight isolation in IIoT terminals. An anti-jamming and secure communication framework for IIoT is proposed, which leverages Deep Reinforcement Learning (DRL) and RIS to defend against malicious jamming and eavesdropping targeting legitimate users' channels. Additionally, a foundational framework that employs Quantum Reinforcement Learning (QRL) is presented to improve RIS-assisted secure communication. Furthermore, the threats posed by Generative AI (GAI) to RIS-assisted IIoT communica-tion networks and possible defense schemes are provided. Finally, the paper concludes by discussing research opportunities and fu-ture challenges.