Data-driven IoT solutions: Leveraging RPMA, BLE, and LTE-M with gaussian mixture models for intelligent device management
1 John Tesla Inc, California, USA.
2 CarGurus Inc, Massachusetts, USA.
3 Saiana Technologies Inc, New Jersy, USA.
4 IBM, California, USA.
5 Centene management LLC, florida, United States.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2023, 09(01), 432-442.
Article DOI: 10.30574/wjaets.2023.9.1.0154
Publication history:
Received on 31 March 2023; revised on 11 May 2023; accepted on 14 June 2023
Abstract:
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.
Keywords:
IoT; Random Phase Multiple Access (RPMA); Bluetooth Low Energy (BLE); Long-Term Evolution for Machines (LTE-M); Gaussian Mixture Models (GMM)
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0