Internet of Thing Data

Internet of Things (IoT) data presents significant challenges due to its volume, heterogeneity, and dynamic nature, demanding efficient management and analysis for diverse applications. Current research focuses on developing robust indexing structures, employing deep learning models (like CNNs and GRUs) for classification and anomaly detection, and utilizing federated learning to address privacy concerns while enabling collaborative model training. These advancements are crucial for optimizing resource utilization, improving the accuracy and reliability of IoT-based predictions (e.g., in predictive maintenance and energy management), and unlocking the full potential of IoT data in various sectors.