IoT-Driven Environmental Monitoring and Pollution Source Attribution Using TimesNet and Spatio-Temporal Graph Neural Networks (ST-GCN)

Abstract

Environmental pollution poses a significant threat to public health and ecosystems, demanding advanced methods for real-time monitoring and source identification. Traditional IoT monitoring systems often fail to capture complex spatiotemporal patterns and raise privacy concerns. This paper introduces a robust, privacy-preserving IoT-based environmental monitoring framework integrating Times Net for temporal feature extraction and Spatio-temporal Graph Neural Networks (STAGE) for spatial relationship modeling. The system incorporates Federated Learning with Differential Privacy, Zero-Knowledge Proofs (ZKP) for authentication, and Post-Quantum Cryptography (CRYSTALS-Cyber) for blockchain-secured model updates. Experimental evaluation using real-world IoT data demonstrates a 93.4% prediction accuracy, a 12% privacy gain, and a 35% reduction in communication cost compared to traditional methods. The architecture is scalable, modular, and designed to support real-time, privacy-sensitive environmental monitoring in smart city applications.