Energy-Aware Task Offloading in Massive IoT Edge Network Using Optimized Convolutional Neural Network

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 11
Year of Publication : 2024
Authors : Shoukath Cherukat, J. Benita
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How to Cite?

Shoukath Cherukat, J. Benita, "Energy-Aware Task Offloading in Massive IoT Edge Network Using Optimized Convolutional Neural Network," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 11, pp. 424-437, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P139

Abstract:

The importance of the Internet of Things (IoT) devices is rising in this digital age. However, the high energy consumption of these devices limits their long-term functioning and efficiency. Task offloading technique in IoT is often used to address energy optimization. Conventional task offloading strategies lead to shorter lifespans and inefficient use of devices. Energy-aware task offloading is critical in IoT edge networks within the Mobile Edge Computing (MEC) environments to enhance resource utilization and reduce latency. This paper proposes an optimized Convolutional Neural Network (CNN) for efficient task offloading. The method efficiently predicts optimal offloading decisions using a comprehensive dataset that includes network characteristics, user behavior and resource utilization features. The optimized CNN architecture achieved notable performance with 91.075% accuracy, 91.82% precision, 91.07% recall, and 90.74% F1 score. These results showed that the model ensures efficient resource allocation and extends the operational life of IoT devices. The key findings highlight the potential of Deep Learning (DL) models in contributing to the energy-aware task offloading field in real-time adaptive decision making within the MEC environments.

Keywords:

Internet of Things, Energy optimization, Mobile Edge Computing, Deep Learning, Convolutional Neural Network. 

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