EP-MUSTO: Entropy-Enhanced DRL-Based Task Offloading in Secure Multi-UAV-Assisted Collaborative Edge Computing

Unmanned aerial vehicles (UAVs)-assisted edge computing has emerged as an effective solution for providing contingency task offloading (TO) services when ground computing infrastructures are insufficient. However, UAVs face challenges in implementing efficient task offloading strategies due to their limited capabilities and the complexity of the privacy offloading problem. To address these challenges, this study constructs a digital twin (DT)-enabled UAV swarm-assisted secure computing model, which considers collaboration of devices, edges, and cloud resources. The model is designed to represent the three-tier computing environment as a DT virtual framework, allowing for the monitoring of network changes and the exploration of potential strategies. Furthermore, a joint optimization problem that considers time delay and energy consumption within encryption and decryption costs is formulated. To solve this problem, an entropy-enhanced proximal policy optimization-based multi-UAV assisted security-aware task offloading (EP-MUSTO) algorithm is proposed. In EP-MUSTO, the exploration capability is enhanced by utilizing an actor network with policy entropy, and the action cognition is improved through the parameterization of the hybrid action space (HAS). Experimental results demonstrate that compared with other advanced algorithms, EP-MUSTO achieves a reduction in security system costs (SSCs) and magnitude of convergence oscillations by at least 9.43% and 54.62%, respectively.