Toward Secure and Energy-Efficient ISAC in Low-Altitude IoT: A Game-Theoretic DRL Framework With Adaptive Sensing
Fuhao Liu, Junsheng Mu, Wentao Xu, Jiansong Miao, Wael M. Bazzi, Shahid Mumtaz · IEEE Internet of Things Journal · 2025
Integrated sensing and communication (ISAC)-enabled low-altitude Internet of Things (IoT) networks hold significant potential for applications in smart cities and emergency communication systems. However, achieving secure and energy-efficient communication under complex environments, particularly in the presence of the mobile full-duplex eavesdropper (MFDE), presents significant challenges. This study investigates the optimization of secure rate energy efficiency (SREE) in ISAC-enabled low-altitude IoT networks, where the problem is further complicated by the strong coupling between unmanned aerial vehicles (UAV) trajectory design, power allocation, and artificial noise (AN) generation, leading to an optimization issue marked by significant dimensionality and a lack of convexity. To tackle this challenge, a power cost factor-based Twin Delayed Deep Deterministic Policy Gradient (CTD3) algorithm is developed, which incorporates a game-theoretic power allocation strategy into the TD3 framework to efficiently handle the high-dimensional coupled optimization problem. The algorithm reformulates part of the high-dimensional continuous optimization process into a strategy interaction problem and introduces a power cost factor into the utility function, effectively reducing the dimensionality of optimization variables and the overall computational burden. Furthermore, an adaptive dynamic sensing mechanism is introduced to enhance resource utilization while effectively countering the dynamic behavior of eavesdroppers. The effectiveness of the proposed strategy in enhancing SREE performance amidst environmental uncertainties is validated through extensive simulations, where it consistently outperforms baseline methods.