Enhancing Edge Computing Efficiency Using Autoencoding
Subject Areas : AI and RoboticsMahdi Tatar 1 , Fatemeh Nasiri 2
1 -
2 - Assistant Professor, Islamic Azad University, Yadegar-e-Imam Branch. Corresponding Author
Keywords: Internet of Things, Edge Computing, Data Storage, Autoencoders, Multilayer Perceptrons,
Abstract :
The rapid expansion of Internet of Things (IoT) devices has led to a significant increase in the volume of generated data, posing substantial challenges for edge computing environments. Traditional cloud-based computing methods struggle to meet the demands of this technology due to limitations related to latency and privacy. To overcome these challenges, researchers have been exploring innovative solutions to optimize data storage and processing in edge computing environments.
In this study, a hybrid approach is introduced that simultaneously leverages Autoencoders (AEs) and Multilayer Perceptrons (MLPs) to enhance data processing efficiency in IoT edge computing. By utilizing the advantages of both architectures, this approach provides a robust model for effectively managing large-scale IoT data. The evaluation results of the proposed method indicate that the model outperforms other approaches, achieving the highest accuracy (0.88), precision (0.75), recall (0.70), and F1-score (0.72). These findings suggest that the proposed solution can serve as an efficient approach for optimizing IoT data processing.
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