Energy efficient resource allocation and trajectory optimization method for secure digital twin-enabled UAV-assisted MEC in 6G networks

The future sixth generation (6G) mobile network will be a highly heterogeneous system that integrates diverse technologies and communication paradigms, encompassing diverse consumer electronics devices, including various internet of things (IoT) devices utilizing different protocols. The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) with 6G has created ground-breaking prospects for effective data processing and computation services in the IoT. Despite this improvement, the presence of an eavesdropper introduces significant security risks into the computing process of mobile devices (MDs), permitting the collection of sensitive data from MDs and perhaps affecting the correctness of offloaded computations. This study proposes an efficient technique for reducing energy usage in a secure digital twin (DT)-enabled UAV-assisted MEC metaverse network that faces the threat of a UAV eavesdropper. By ensuring the secure processing of all MDs data, the network meets its energy consumption objectives by optimizing trajectories and resources, taking into account parameters like as time, local computation, and offloading computation dispersion. Because of the complicated arrangement of the interplay of various variables and non-linear constraints, solving the problem directly is extremely difficult. To solve this complexity, an auxiliary variable is used to rearrange the problem into a more understandable format. We employ DT-enabled DRL to address this issue because traditional methods are inadequate. The empirical findings indicate that the proposed methodology achieved a reduction in energy consumption of approximately 83.32% and a decrease in time delay of roughly 11.97%, in comparison to the prevailing baseline methodologies.