Scene-Adaptive Task Offloading in Heterogeneous Edge Networks via Graph Neural Network-Enhanced Deep Reinforcement Learning

Efficient task offloading in UAV-assisted heterogeneous mobile edge computing (MEC) networks is increasingly challenged by the co-existence of operationally distinct workload scenarios—including high-demand bursts, resource-constrained periods, and balanced operational states—each demanding fundamentally different assignment strategies. In such networks, mobile executor nodes (e.g., UAVs or vehicle-mounted edge servers) must be dispatched to the vicinity of geographically distributed tasks, making assignment decisions jointly dependent on node mobility, the quality of sensing data, and dynamic resource availability. Conventional approaches based on combinatorial optimization with fixed parameters or greedy heuristics fail to adapt to these varying conditions, leading to resource depletion under sequential workloads or underutilization under high-demand bursts. To address these limitations, this paper proposes SAGE (Scene-Adaptive Graph-Enhanced offloading), a task-offloading framework that combines a heterogeneous graph neural network (HeteroGNN) with a dueling double DQN meta-controller and a mixed-integer linear programming (MILP) solver. At the state-representation level, a heterogeneous bipartite graph is constructed over mobile executor nodes and tasks, with type-specific projection layers encoding the semantic features of each node type and three-dimensional edge features—comprising task success probability, normalized service distance, and link quality—integrated via edge-gated message passing. At the decision level, the meta-controller perceives the current workload scenario through a seven-dimensional situational state vector fused with the graph embedding, selects an appropriate offloading strategy from a learned discrete action space, and drives the MILP solver to perform task-chain assignment under the selected configuration. Experiments on 60 fixed evaluation episodes spanning three representative workload scenarios demonstrate that SAGE achieves an overall reward improvement of 15.9% over the best fixed-strategy baseline, reduces the resource depletion rate to 16.7%, and maintains a high-priority task completion rate of 84.7%. Particularly under resource-constrained conditions, SAGE reduces the reward deficit by 72.3% relative to the best fixed strategy (from −0.531 to −0.147), demonstrating strong scene-adaptive decision-making capability.