Data-driven IoT solutions: Leveraging RPMA, BLE, and LTE-M with gaussian mixture models for intelligent device management

Background IoT networks have problems in terms of effective data management and communication. Device performance and data processing can be improved using technologies such as RPMA, BLE, and LTE-M, as well as machine learning models like GMM.
Methods This study combines RPMA, BLE, LTE-M, and Gaussian Mixture Models (GMM) to improve IoT device management, with an emphasis on energy efficiency, data throughput, and anomaly detection.
Objectives The primary goal is to optimize IoT networks by merging communication technologies and GMM for improved performance, anomaly detection, and resource management in real-time applications such as smart cities and agriculture.
Results The suggested method outperforms standard models in several critical measures, including 90% energy efficiency, 92% data throughput, 94% latency reduction, and 96% anomaly detection.
Conclusion This strategy improves IoT network performance by merging RPMA, BLE, LTE-M, and GMM, resulting in a scalable, energy-efficient solution for real-time data management and intelligent device monitoring across several industries.