This paper forms a multi-objective optimization problem aimed at minimizing AoI and energy consumption while maximizing the eavesdropper’s Bit Error Rate by jointly optimizing UAV trajectories, time scheduling, and jamming parameters and develops an efficient iterative algorithm.
This work investigates the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system, and maximizes the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories.
Hongjiang Lei, Jianshuo Geng, Ki-Hong Park et al.· 0 citations
Multi-UAV edge networks, as an effective supplement to ground sensor systems, can significantly improve perception coverage and data processing efficiency. However, as UAV networks scale up, effective multi-UAV cooperation becomes increasingly critical and challenging, especially for coupled deployment, task allocation, and resource management. Meanwhile, due to the openness and broadcast nature of wireless channels, UAV transmissions are vulnerable to eavesdropping, making cooperative security protection essential for reliable UAV edge computing. To address these issues, this paper investigates a multi-UAV secure edge computing scenario in which UAVs cooperate both for self-jamming to thwart the aerial eavesdropper and for distributed edge computing to assist task processing. We establish the digital models of the UAV secure edge computing workflow and formulate the latency minimization problem under covert communication constraints. Then, a particle swarm optimization (PSO) + block coordinate descent (BCD) method for discrete state spaces and a multi-agent deep deterministic policy gradient (MADDPG)-based scheme suitable for continuous real-world environments are proposed, respectively. Extensive analysis and simulations demonstrate the effectiveness of our methods, achieving covert task offloading while significantly reducing task processing latency.
Xiaoyi Zhou, Hongzhi Guo, Bomin Mao et al.· IEEE Transactions on Cogniti...· 0 citations
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.
Xiaojie Wang, Lingfei Li, Zhaolong Ning et al.· IEEE Transactions on Wireles...· 1 citation
This paper proposes a novel physical-layer security framework for multi-UAV Integrated Sensing and Communication (ISAC) networks operating in adversarial environments. To maximize the sum secrecy rate of legitimate ground users (GUs) while satisfying minimum sensing beampattern-gain constraints for target illumination, we introduce a dynamic role allocation mechanism in which each UAV can switch, on a per-time-slot basis, between an ISAC mode—combining coherent communications with radar sensing—and a dedicated artificial noise (AN) jammer mode. The resulting optimization is cast as a highly coupled Mixed-Integer Non-Linear Program (MINLP) that jointly optimizes binary role indicators, transmit beamforming and sensing covariance matrices, and UAV trajectories. We solve this problem with a tailored Alternating Optimization (AO) algorithm that integrates a penalty-based Convex-Concave Procedure (CCP) for the binary role subproblem, Semidefinite Relaxation (SDR) for the beamforming subproblem, and a trust-region Successive Convex Approximation (SCA) for the trajectory subproblem. Numerical results demonstrate that the proposed dynamic-role architecture consistently outperforms both a static dedicated-jammer scheme and a fully optimized all-ISAC embedded-AN benchmark, confirming that its secrecy advantage arises from adaptive spatial-functional specialization rather than from artificial-noise transmission alone. Furthermore, we characterize the fundamental tradeoff between secrecy performance and stringent sensing beampattern-gain constraints, showing that moderate sensing requirements can be accommodated with no secrecy penalty.
M. M. Selim, Mohamed Rihan, Armin Dekorsy et al.· IEEE Open Journal of the Com...· 0 citations
The rapid expansion of real-time Internet of Things (IoT) applications has positioned uncrewed aerial vehicles (UAVs) as a promising solution for flexible and timely data collection in areas lacking robust infrastructure. This paper investigates a UAV-assisted secure status updating system, where a UAV serves as a mobile relay to forward status updating packets from ground devices (GDs) under the threat of a potential eavesdropper. To ensure information freshness and operational sustainability, we formulate a long-term stochastic optimization problem to minimize the cumulative average age-of-information (AoI) of all GDs and energy consumption of the UAV. The formulated optimization problem is an online mixed-integer non-linear programming problem, which involves the joint optimization of the flight speed, direction, and transmission power of the UAV as well as the binary scheduling indicator of GDs. To tackle the inherent non-convexity and complex spatial-temporal coupling, we propose an agentic artificial intelligence (AI)-enabled deep reinforcement learning (DRL) approach, named adaptive truncated quantile critics with large language models (LLM)-enabled state representation and reward function design (ATQC-L). Specifically, an adaptive truncated quantile mechanism is incorporated to mitigate distributional overestimation in dynamic environments. Furthermore, we leverage the reasoning capability of LLMs as an offline design-time agent to generate task-aware state representation and intrinsic reward functions. Simulation results demonstrate that the proposed ATQC-L algorithm outperforms representative DRL baselines in balancing information freshness and energy consumption of the UAV, while maintaining stable performance under different network scales, LLM backbones, truncation-parameter settings, imperfect eavesdropping channel state information, and mobile eavesdropping scenarios.
Chuang Zhang, Geng Sun, Jiahui Li et al.· IEEE Transactions on Cogniti...· 0 citations
Integrated sensing and communication (ISAC) technology, when deployed on unmanned aerial vehicles (UAVs), enables aerial base stations to simultaneously provide wireless connectivity to ground users and perform environmental sensing through echo signal analysis. However, the broadcast nature of wireless transmission, combined with the line-of-sight (LoS) propagation characteristics of UAVs, increases the risk of passive eavesdropping on transmitted signals during ISAC missions. This paper investigates the joint trajectory design and power allocation (JTDPA) problem for UAV-enabled ISAC systems in environments with multiple mobile ground users and potential eavesdroppers. The proposed approach formulates the optimization problem as a constrained Markov decision process (CMDP), aiming to balance communication rate, secrecy rate, and energy consumption. To address the limitations of existing secure trajectory designs, such as unnecessary energy expenditure and overly conservative avoidance actions, we propose a two-stage (TS) strategy that incorporates the safe twin delayed deep deterministic policy gradient (Safe-TD3) algorithm, referred to as TS-SafeTD3. In the first stage (sensing stage), the UAV navigates toward a user-centric location without communication to enhance initial coverage efficiency, while satisfying the minimum-distance safety constraints with respect to potential eavesdroppers.In the second stage (ISAC stage), Safe-TD3 is employed to jointly optimize both trajectory and power allocation under the same safety constraints to maximize the weighted secrecy rate. Simulation results indicate that the proposed algorithm improves the weighted secrecy rate and energy efficiency under various operational conditions, while maintaining a low violation probability of the safety constraints.
Yu-Jia Chen, Hai-Yan Huang, Ting-Wei Chen et al.· IEEE Transactions on Network...· 0 citations