Enhancing IoT Network Efficiency Using Machine Learning Approaches

Technology has seen an exponential growth in the recent times. This in turn, has advanced the extent of device connectivity at an unrivaled pace by enabling large number of devices to communicate with each other at the same time and smooth data transfer. However, the management of such interconnected systems under the IoT framework stands to be a formidable task. Its deployment gets impeded by several challenges, including resource wastage, scalability, heterogeneity, and security attacks. This paper aims to study the unusual activities that can threaten the security of IoT connections, and to offer a feasible solution using deep learning approaches. In particular, it explores Graph Neural Networks (GNNs) and specifically examines Graph Convolutional Networks (GCNs). The 2-layer GCN model outperforms a similar MLP with an F1-score of 0.9577 and a test accuracy of 95.89%. The outcomes show the model’s robustness and strong capacity for generalization, with steady convergence throughout training. These findings outline the scope for this type of neural network model to be vastly employed in anomaly detection mechanisms, leading to enhanced IoT network management, deployment and performance capabilities.