Efficient collaboration among Unmanned Aerial Vehicles (UAVs) has significant performance improvement for UAV-based applications. Task offloading is the typical collaboration form for UAV system. However, it still be a challenging problem for UAV system due to task dependencies and the UAV mobility which makes the traditional offloading approaches inefficiency. In this paper, we model the offloading problem as the Sequential Task Offloading Problem (sTOP), which takes the task spatiotemporal dependencies into account. We propose a Graph-based Spatiotemporal Reinforcement Learning (GSTRL) framework, where the environment is modeled as a heterogeneous graph to capture the diverse relationships among system entities. A spatiotemporal state extraction module is designed, which integrates a Heterogeneous Graph Neural Network (HGNN) for spatial dependency modeling and a Long Short-Term Memory (LSTM) network for temporal dynamics. Based on the extracted representations, a masked Proximal Policy Optimization (mPPO) algorithm is proposed to make valid and efficient offloading decisions under multiple system constraints. Extensive experiments using real UAV trajectory and building distribution datasets validate that the proposed method improves the average reward by approximately 25% over state-of-the-art DRL-based and heuristic baselines, by increasing task success rate and operational effectiveness ratio (OER) to 30–50%, while reducing execution time by up to 40% in complex multi-UAV systems.

In edge computing, end devices (EDs) containerize tasks with the necessary resources and offload subsets to a nearby high-capacity edge server (ES) to improve efficiency. Most existing research focuses on inseparable task offloading to minimize response times or resource allocation to reduce energy consumption. However, task execution can be speeded up with excessive computing and network resources, it will increase energy consumption and incur unnecessarily high costs. Besides, complex applications like autonomous driving often partition sequential tasks to improve performance, necessitating a joint optimization of sequential task offloading and multi-resource allocation. In this paper, we introduce a Stackelberg game-based framework to model the interplay between these elements.EDs, acting as leaders, determine the offloading breakpoints of sequential tasks and the locality for processing. TheES, as the follower, uses the Karush-Kuhn-Tucker (KKT) conditions and a Boundary-constrained quasi-Particle Swarm Optimization (Bc-qPSO) algorithm to refine computing and network resource allocation, aiming to reduce system costs effectively. Our simulations show that the proposed algorithms reduce cost by approximately 10%-20% compared to traditional methods, highlighting their potential for improving the efficiency of edge computing systems.

Intelligent terminal devices (TDs) usually request delay-sensitive and resource-demanding jobs, which are consisted of many sequential tasks. Mobile edge computing (MEC) offloads tasks to edge networks closer to TDs, making up for the lack of long delay response in the cloud, but it has a limited energy supply. Thanks to low-energy TDs also having processing capacity, it is a critical and challenging issue to offload sequential tasks for sustainable computing and reducing carbon emission in aterminal-edge-cloud(TEC) architecture. Existing research on offloading is limited to MEC orcloud-edgecoordination environment, and ignores the impact of sequential task (S-Task) constraint and service constraint. To bridge the gap, our paper first formulates the jointly optimalS-Taskoffloading and service deployment (JOTOSD) problems objected to maximize the energy utility related to response delay, which is NP-hard and is divided into deployment and offloading sub-problems. Then, we propose a comprehensive offloading and deployment (COD) method, including the Break-Point (BP) algorithm and the convex programming-based edge offloading (CVEO) algorithm under a service deployment strategy provided by an iterative service deployment (ISD) algorithm. Simulate results prove that the proposed method can improve by about 20% of energy utility by compared with other heuristic algorithms.