Elaris Computing Nexus
Efficient Data Transformation and Machine Learning Analytics in IoT Systems Using a Hybrid Fog Cloud Approach
Elaris Computing Nexus
Received On : 05 January 2025
Revised On : 23 February 2025
Accepted On : 30 March 2025
Published On : 10 April 2025
Volume 01, 2025
Pages : 022-031
Abstract
This study analyzes the efficacy of three data processing architectures: fog computing, cloud computing, and a hybrid fog-cloud computing framework within an Internet of Things (IoT) context. We evaluate the performance of machine learning methods regarding computation time, communication overhead, precision, and efficiency on IoT-based sensor data. The proposed hardware utilizes a Raspberry Pi as a fog node for local data processing and a cloud infrastructure for executing computationally intensive operations. Each architecture processes both raw and processed real-world IoT sensor data. The transformation procedure at the fog node compresses data, reducing its size and minimizing overhead during transmission to the cloud. The findings demonstrate that the hybrid architecture offers the best balance of execution speed, communication efficiency, and accuracy when compared to cloud-only and fog-only methods. The hybrid system leverages cloud computing capabilities and minimizes transmission costs by collecting data locally.
Keywords
IoT, Fog Computing, Cloud Computing, Hybrid Architecture, Machine Learning, Data Aggregation, Network Efficiency, Execution Time, Communication Costs, Energy Consumption, Data Transformation.
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Cite this Article
Ali-Кhusein, “Efficient Data Transformation and Machine Learning Analytics in IoT Systems Using a Hybrid Fog Cloud Approach”, Elaris Computing Nexus, pp. 022-031, 10 April 2025, doi: 10.XXXXX/ECN/2025003.
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© 2025 Ali-Кhusein. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.