UAV-assisted MEC offloading strategy with peak AOI boundary optimization: A method based on DDQN

Abstract

In response to the requirements for large-scale device access and ultra-reliable and low-latency communication in the power internet of things, unmanned aerial vehicle-assisted multi-access edge computing can be used to realize flexible access to power services and update large amounts of information in a timely manner. By considering factors such as machine communication traffic, MAC competition access, and information freshness, this paper develops a cross-layer computing framework in which the peak Age of Information (AoI) provides a statistical delay boundary in the finite blocklength regime. We also propose a deep machine learning-based multi-access edge computing offloading algorithm. First, a traffic arrival model is established in which the time interval follows the Beta distribution, and then a business service model is proposed based on the carrier sense multiple access with collision avoidance algorithm. The peak AoI boundary performance of multiple access is evaluated according to stochastic network calculus theory. Finally, an unmanned aerial vehicle-assisted multi-level offloading model with cache is designed, in which the peak AoI violation probability and energy consumption provide the optimization goals. The optimal offloading strategy is obtained using deep reinforcement learning. Compared with baseline schemes based on non-cooperative game theory with stochastic learning automata and random edge unloading, the proposed algorithm improves the overall performance by approximately 3.52 % and 20.73 %, respectively, and provides superior deterministic offloading performance by using the peak AoI boundary.