Cooperative Covert Communication and Task Offloading for AI-Empowered Multi-UAV Networks

Multi-Objective Optimization for Secure UAV-Assisted Data Collection via Intermittent Jamming

Resource Allocation for Secure Dual-UAV-Assisted ISAC System

Integrated sensing and communication (ISAC) is a rising technology in the next wireless communication networks, enabling the simultaneous execution of communication and sensing tasks by fully utilizing limited spectrum resources. In this work, we investigate the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system. Specifically, a base station UAV communicates with users and transmits radar signals to locate potential eavesdroppers, while simultaneously providing information to a jammer UAV to perform jamming tasks. Considering constraints such as maximum UAV velocity, transmit power, propulsion energy, and sensing thresholds, we maximize the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories. The presence of a non-convex problem, originating from tightly coupled variables, is tackled by an efficient iterative algorithm. In particular, the original optimization problem is decomposed into six subproblems, and non-convex subproblems are transformed into approximately convex forms via successive convex approximation. Then, block coordinate descent techniques are employed to solve all subproblems sequentially. Numerical results demonstrate the convergence and effectiveness of the proposed algorithm.

Hierarchical Optimization of UAV Deployment and Resource Allocation for ISAC-Enabled Low-Altitude Wireless Networks

Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.

ISAC Enabled Anti-UAV: Joint Beamforming and Trajectory Design for Multi-UAVs

The rapid proliferation of Uncrewed Aerial Vehicles (UAVs) introduces significant challenges to low-altitude airspace security, particularly from unauthorized intrusions. To address these vulnerabilities, Integrated Sensing and Communication (ISAC) has emerged as a key enabler for anti-UAV systems. However, existing studies focusing on cellular networks with fixed base stations are ill-suited for the continuous movement of target UAVs, thus failing to meet the dual demands of flexible sensing and reliable positioning. To address this, we propose an ISAC-enabled anti-UAV scheme solely based on cooperative UAVs. Specifically, we first derive the optimal transmit power under the constraint of space-air transmission outage probability tolerance. Subsequently, we deduce the sensing Fisher information matrix and Cramér-Rao Bound (CRB) by incorporating the position uncertainty of the target UAV. Then, we formulate a long-term CRB minimization problem to enhance cooperative sensing performance. To tackle this NP-hard problem, we design a robust optimization algorithm that jointly optimizes transmit-receive beamforming, association scheduling, and UAV trajectory, by transforming the structurally complex CRB matrix into a set of semi-definite constraints, and resolving the inherent position uncertainty. Numerical results demonstrate that our proposed algorithm outperforms representative algorithms in terms of sensing accuracy and robustness.

IRS-Assisted UAV Secure Communication via Agentic AI

Low-altitude uncrewed aerial vehicle (UAV) communication offers notable advantages over terrestrial base stations in terms of flexibility and deployment efficiency. However, the high likelihood of line-of-sight (LoS) propagation renders the communication links between UAVs and ground users (GUs) particularly susceptible to eavesdropping. To address this issue, we consider an intelligent reflecting surface (IRS)-assisted low-altitude UAV secure communication system, in which communication security is strengthened through adaptive control of the wireless propagation environment, even when eavesdroppers are present. We aim to maximize the secrecy rate of GUs while minimizing the UAV energy consumption by jointly optimizing the continuous UAV trajectory, power allocation, and discrete IRS phase shifts. Considering the dynamic, non-convex, and NP-hard nature of the optimization problem, we propose an agentic artificial intelligence (AI) approach, namely alternating optimization (AO) and generative diffusion model-based deep deterministic policy gradient (AO-GDMDDPG) approach. The proposed agentic AI approach is composed of two cooperative agents that operate over a hybrid and high-dimensional decision space, in which the UAV agent adopts a generative AI (GenAI)-enhanced deep reinforcement learning (DRL) method to optimize continuous decision variables, whereas the IRS agent relies on the AO method to determine discrete IRS phase shifts. Simulation results demonstrate the superiority of the AO-GDMDDPG approach over benchmark algorithms with respect to secrecy rate improvement and UAV energy consumption reduction.

A Secure Hierarchical Federated Learning Architecture for UAV-Enabled Edge Computing Systems

Unmanned aerial vehicles (UAVs) are emerging as mobile edge nodes for temporary coverage, aerial sensing, disaster response, public-safety monitoring, intelligent transportation, and smart-agriculture services. Although federated learning (FL) enables distributed model training without transferring raw data, conventional flat FL is not well aligned with UAV-enabled edge systems because ground clients may experience mobility-dependent availability, unstable wireless links, and costly longrange synchronization with a remote coordinator. Hierarchical federated learning (HFL) mitigates these limitations by introducing UAV edge aggregators between ground clients and the global coordinator. This article provides a simulation-based assessment of a secure HFL-UAV architecture implemented in Python. The simulator captures non-independent and identically distributed (non-IID) client data, mobility-aware client-UAV association, two-level model aggregation, malicious client behavior, trust-weighted robust aggregation, optional differential privacy, secure-aggregation overhead, communication cost, latency, and UAV energy consumption. Under a model-replacement attack, the secure HFL-UAV scheme maintains the learning performance of the considered synthetic classification task while substantially reducing the aggregation trust assigned to malicious clients. The results also expose important design tradeoffs: security mechanisms increase local communication overhead, UAV-assisted aggregation introduces energy cost, and trust filtering must be calibrated carefully under non-IID data. The proposed simulation framework therefore provides a reproducible basis for evaluating secure aerial edge learning systems.