Task Offloading with Temporal Awareness in Heterogeneous Cloud–Edge–Ground Systems

Abstract

In post-disaster or infrastructure-limited remote-area environments where local terrestrial networking infrastructure is unavailable or severely degraded, heterogeneous cloud–edge–ground (CEG) systems provide a viable platform for computation offloading. Ground devices such as IoT sensors and unmanned ground vehicles (UGVs) generate computation-intensive and latency-sensitive tasks, while fixed-wing unmanned aerial vehicles (UAVs) with edge servers offer intermediate processing and relay capabilities to the cloud. We aim to minimize system energy consumption and task latency by jointly considering offloading ratios, CPU frequencies and transmission power of the ground devices. However, efficiently partitioning tasks across these tiers is challenging due to dynamic wireless network conditions, limited onboard resources, and diverse task demands. We propose a temporal dynamics-aware multi-agent offloading and resource allocation framework for such CEG systems in this work. We introduce KAT-MAPPO, a Kolmogorov–Arnold Transformer-enhanced Multi-Agent Proximal Policy Optimization scheme. By embedding Kolmogorov–Arnold Network layers into the transformer encoder, the framework captures long-range temporal dependencies and improves feature representation, while MAPPO enables decentralized offloading under dynamic conditions. Resource allocation is further decoupled and solved analytically via convex optimization. Using a real-world setup, our simulation results show that KAT-MAPPO significantly outperforms existing baselines, achieving faster convergence, a 43% reduction in average task latency in the training evaluation setting, and up to 53.57% latency reduction for the case study scenarios, while also reducing energy consumption. These results highlight the promise of reinforcement learning considering temporal dynamics for efficient offloading in heterogeneous CEG systems.