This article investigates the issue of cooperative real-time trajectory design for multiple unmanned aerial vehicles (UAVs) to support emergency communication in disaster areas. To restore communication links rapidly between mobile users (MUs) and the ground base stations, UAVs equipped with both radio frequency (RF) modules and free space optics (FSO) modules are utilized as relay nodes. Given the challenges of setting up a central controller for the UAVs and the urgency of emergency communication, the trajectory design problem for these UAVs is formulated as a distributed cooperative optimization problem. Based on the enhanced ${K}$ -mean algorithm and multiagent PPO (MAPPO) algorithm, a cooperative trajectory design method, abbreviated as KMAPPO, is proposed for the UAVs to minimize interaction overhead and optimize deployment efficiency. Compared to the state-of-the-art deep reinforcement learning (DRL) methods, simulations reveal KMAPPO’s superior performance. It converges 32% faster, boosts RF allocation efficiency, and augments FSO communication backhaul capacity.

This article focuses on zero-trust foundation models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as least privilege access, continuous verification, data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving artificial intelligence (AI) across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

A cloud network schedules diverse tasks to multi-access edge computing (MEC) or cloud platforms within dynamic industrial Internet of Things (IIoT). The scheduling is influenced by the diverse intents of different parties, including the time-sensitive nature of device-generated tasks and the energy efficiency of servers. The complexity of this problem under dynamic network conditions is underscored by its nature as a Markov state transition process, typically classified as NP-hard. We introduce an intent-driven intelligent task scheduling approach (IITSA), which models a partially observable Markov decision process (POMDP) and introduces a multi-agent proximal policy optimization (MAPPO) method. We introduce a dynamic adaptive mechanism to effectively address conflicts arising from the temporal requirements and energy limitations associated with various tasks on MEC servers. This mechanism enhances the reward function of MAPPO, for which we offer comprehensive mathematical analysis to validate its convergence performance. Simulation results showcase that our proposed IITSA effectively achieves a harmonious trade-off between time-sensitive demands and infrastructure energy efficiency while exhibiting high adaptability. Compared to state-of-the-art algorithms like MADDPG and QMIX, IITSA reduces energy consumption by 11.68% and 7.07%, and enhances on-time completion numbers for time-sensitive tasks by 18.33% and 12.17%, respectively.

Establishing network slices in line with the intent of particular applications through closed-loop automation represents a key goal on designing future advanced networks. However, the lack of association between application intents and network services imposes challenges to accurately capture user preference and generate customized network slices. This article introduces Intent-Oriented Network Slicing (IONS) based on hypergraph theory to realize the customization of application intents, while maximizing the utilization of network resources. Specifically, IONS introduces a cutting-edge hypergraph framework, utilizing scenarios and patterns as the foundational elements of its hyperedges, which can be further enhanced by creating a dynamic adaptive attention mechanism tailored to the hypergraph’s structure. Through this innovative approach, a sophisticated correlation model is established, linking application intents with network slicing strategies. This model significantly enhances the precision of user acquisition, optimizing network resources and user experience, while thus improving the customization of slice generation. Simulations demonstrate that the generated network slices can accurately reflect application intents.