Real-Time Environmental Monitoring and Prediction in IoT Networks With Graph Neural Networks

This research investigates real-time natural checking and forecasting in IoT systems utilizing Graph Neural Networks (GNNs). Leveraging a comprehensive dataset from IoT sensors capturing discuss quality, temperature, mugginess, wind speed, and precipitation, we examine the execution of four calculations: Graph Convolutional Network (GCN), Graph Attention Network (GAT), Long Short-Term Memory (LSTM), and Graph Recurrent Neural Network (GRNN). Comes about illustrates the predominance of GNN-based models, especially GAT and GRNN, in capturing spatial and transient conditions for exact forecasts. The models accomplish amazing execution measurements, counting Mean Absolute Error (MAE) values of 4.95 for GAT and 4.73 for GRNN, Root Mean Squared Error (RMSE) values of 7.12 for GAT and 6.85 for GRNN, and R² scores of 0.78 for GAT and 0.80 for GRNN. These discoveries emphasize the potential of GNNs in revolutionizing natural observing practices, encouraging proactive decision-making, and cultivating maintainable and flexible communities. The study contributes to the broader talk on leveraging AI and IoT innovations for tending to natural challenges and lays the establishment for future research in this space.