Malware Suppressing Strategies for Smart Agricultural UAV–WSN in Unknown Environment: A Reinforcement Learning Framework

To address the dynamic control challenges of malware propagation in smart agricultural UAV-WSN (Unmanned Aerial Vehicle-Wireless Sensor Network) systems, this paper proposes a novel hybrid control modeling framework based on fractional-order calculus. A 14-dimensional dual-layer SEIQR-SEIQRL fractional-order propagation model with time-delay effects is established to characterize cross-layer infection mechanisms between UAVs and sensor nodes, as well as system memory properties. By innovatively integrating direct treatment and dynamic quarantine strategies, and further incorporating node energy constraints, this study formulates an optimal control problem. To overcome the dependency of traditional numerical solutions on system models, a reinforcement learning (RL)-based decision framework is designed, which integrates the Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), Twin Delayed Deep Deterministic policy gradient (TD3) and Multi-Agent Soft Actor-Critic (MASAC) algorithms. Experimental results validate the memory advantages of the proposed fractional-order theory, the effectiveness of hybrid control strategies, and the adaptability of RL-based solvers. This study provides a dynamically defensive architecture with memory-aware capabilities, offering both theoretical innovation and engineering applicability for IoT security in smart agriculture, thereby supporting reliable data acquisition in sustainable precision agriculture.