Research on Deep Learning-Based Optimization Algorithms for IoT Communication

The rapid proliferation of Internet of Things (IoT) devices has brought new challenges in designing efficient, adaptive, and communication-aware optimization strategies under strict resource constraints. Traditional protocols often fall short in dynamic environments due to their reliance on static heuristics or heavy computation. In this paper, we propose NebulaLink, a symbolic communication framework that integrates Graph Neural Networks and Transformer-based attention to enable context-aware and semantically structured message exchange among IoT agents. To enhance efficiency and adaptability, we further introduce QuantaNet, a dynamic control mechanism that regulates communication frequency and content granularity via utility-triggered policies and knowledge discrepancy scheduling. The entire system supports federated optimization across decentralized agents, preserving privacy and reducing bandwidth usage without sacrificing performance. Our framework offers three key innovations: (1) a symbolic abstraction layer that supports interpretable and semantically compressed communication; (2) a learning-driven mechanism to selectively trigger message exchange based on task relevance and inter-agent knowledge gaps; and (3) an efficient federated learning approach suitable for heterogeneous and bandwidth-constrained IoT deployments. Extensive experiments across four real-world datasets show that our method significantly outperforms state-of-the-art baselines—including semantic-aware and Transformer-GNN models—in terms of accuracy, communication cost, and adaptability. These results demonstrate the potential of combining symbolic reasoning with neural communication policies for scalable IoT optimization.