Energy-Efficient Joint Computation Offloading and Resource Allocation Optimization in AAV/HAP-Assisted AIoT Networks

The AAV/HAP-assisted Artificial Intelligence of Things (AIoT) integrates Artificial Intelligence (AI) technology into the Internet of Things (IoT) system, which provides ubiquitous global communications and intelligent computation services for mobile terminal devices (MTDs) by combining terrestrial and non-terrestrial communication networks. Efficient and energy-saving communication and computing resource support are crucial to improving the performance of AIoT systems. Therefore, an energy-efficient joint computation offloading and resource allocation strategy is proposed for AIoT supported by AAVs/HAPs. The strategy combines the advantages of Lyapunov optimization theory and the deep reinforcement learning (DRL)-based model to obtain optimal energy-efficient computation offloading and resource allocation decisions while guaranteeing long-term queue stability under dynamic network conditions. Firstly, the joint optimization problem is modeled as a multi-stage stochastic mixed integer nonlinear programming (MINLP) problem and reformulated into deterministic sub-problems under long-term performance constraints by the Lyapunov optimization theory. Then, a hybrid DRL-based online optimization algorithm is proposed to solve these sub-problems. This algorithm minimizes the overall system energy consumption by jointly optimizing computation offloading and resource allocation decisions. The simulation results show that the proposed strategy improves energy efficiency, decreases algorithm computation complexity, and guarantees the stability of task queues.