Integrated Sensing, Communication, and Computing for Low-Altitude Wireless Networks with Security-Aware Information Freshness

Abstract

Low-altitude wireless networks (LAWNs) are highly vulnerable to eavesdropping due to their dominant line-of-sight links, which give adversaries similar propagation conditions to legitimate users. While integrated sensing, communication, and computing (ISCC) has been widely studied, existing works typically treat security as an independent constraint, overlooking its impact on cross-layer system behavior. In this paper, we reveal that secrecy mechanisms, particularly artificial noise, introduce a fundamental coupling among sensing, communication, and computing. Specifically, artificial noise competes for the shared power budget with sensing and reduces the effective transmission rate governing age-of-information (AoI), leading to a non-trivial security-sensing-freshness trade-off. To capture this effect, we develop a unified security-aware ISCC framework and formulate a multi-objective optimization problem over beampattern error, secrecy rate, and AoI. We further propose a constraint-aware deep reinforcement learning-assisted evolutionary algorithm for efficient optimization. Simulation results demonstrate up to 14.7% performance gain over baselines and reveal a critical regime in artificial noise allocation, where excessive security enhancement degrades overall system performance.