IoT Device Data for Cloud Storage Optimization

This dataset contains AI-generated multi-device IoT data designed to evaluate hybrid cloud storage strategies combining Traditional Replication and Erasure Coding (TR-EC). Each record includes timestamp, device ID, device category, device type, measured data value, anomaly score, and priority level. Although synthetic, the dataset simulates realistic IoT environments covering healthcare, environmental monitoring, energy, security, and industrial devices. It is intended to support research on cloud storage optimization, fault tolerance, and data replication strategies in distributed systems. Researchers can use this dataset to simulate distributed storage scenarios, evaluate storage overhead and latency, validate machine learning models for anomaly detection, and benchmark hybrid storage approaches. This AI-generated dataset provides a reproducible and scalable foundation for studying efficient IoT data management in cloud computing.