Authors

Bennett, E. O.

Department of Computer Science, Rivers State University, Port Harcourt, Nigeria

Cookey, E. E.

Department of Computer Science, Rivers State University, Port Harcourt, Nigeria

Abstract

The rise of Internet of Things (IoTs) has resulted in the increased use of IoT devices which has introduced new challenges in processing real-time data streams efficiently. This article focuses on developing a robust solution for real-time data processing using IoTs devices. The system employs a hybrid model combining K-means clustering and Random Forest classification. K-means algorithm clusters the incoming data, which is then processed by the Random Forest classifier for enhanced accuracy. A simulation of real-time IoTs data streams was conducted to test the system’s capability in handling multifaceted datasets. The system demonstrated remarkable results, achieving an accuracy of 99.99% and a latency of 19.53ms, outperforming benchmark systems with higher latency of 73.6ms. This low-latency, high-scalability system effectively processes real-time IoTs data streams through a web-based interface, showcasing its practicality for applications requiring efficient data handling and quick decision-making in IoTs environments.

Keywords

Data stream processing Internet of Things Data protection scalability Latency

Citation of this Article

Bennett, E. O., & Cookey, E. E. (2026). Real-Time Data Stream Processing System Generated by Internet of Things (IoTs) Devices. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 3(2), 13-25. Article DOI: https://doi.org/10.47001/JAIET/2026.302002

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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