AoI-Aware UAV-Assisted Secure Status Updating: An Agentic AI-Enabled DRL Approach

Abstract

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.