A hybrid graph attention network and proximal policy optimization framework for adaptive fog-cloud offloading and frameproof-based secure IoT data transmission

Introduction Internet of Things (IoT) applications require secure, low-latency data transmission in resource-constrained environments. Conventional cryptographic security is computationally intensive, and static scheduling adapts poorly to changing IoT-Fog-Cloud conditions. Methods A hybrid Graph Attention Network-Proximal Policy Optimization (GAT-PPO) framework is proposed. The IoT-Fog-Cloud infrastructure is modeled as a dynamic graph, from which GAT learns topology-aware node embeddings. A PPO agent uses these embeddings, together with task urgency, battery level, resource availability and task sensitivity, to select fog or cloud nodes for offloading. For high-sensitivity tasks, a lightweight frameproof combinatorics-based verification mechanism integrated with SHA-256 hashing is activated, and its outcome is incorporated into the PPO reward. The framework was implemented in Python and evaluated in a simulated fog-cloud environment using tasks derived from TON_IoT network traffic, over 10 independent runs. Results The framework achieved an average latency of 68.2 ± 0.7 ms, energy consumption of 31.5 ± 0.4 mJ/task, throughput of 912 ± 9.8 tasks/s, packet delivery ratio of 98.7 ± 0.3% and security success rate of 98.9 ± 0.2%, outperforming the Without GAT, GCN-PPO and GAT-DQN configurations (Welch's t-test, p < 0.001). Under representative attacks, the false acceptance rate ranged from 0.10% to 0.60% and the false rejection rate was 0.20%. Discussion The framework provides an effective approach for adaptive and secure IoT task offloading under the evaluated simulation conditions and shows potential for latency-sensitive IoT applications. Hardware-based validation and broader deployment studies remain future work.