A Machine Learning Approach to Reduce Latency in Edge Computing for IoT Devices
Received: 11 July 2024 | Revised: 28 July 2024 | Accepted: 3 August 2024 | Online: 13 August 2024
Corresponding author: Hamayun Khan
Abstract
Nowadays, high latency in Edge Computing (EC) for Internet of Things (IoT) devices due to network congestion and online traffic reduces the acquired precision, performance, and processing power of the network. Data overload in IoT significantly impacts the real-time capabilities of user experience, decision-making efficiency, operational costs, and security in EC. By combining EC innovation and three Machine Learning (ML) models, namely Decision Trees (DT), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), this research aims to tackle the inactivity of IoT devices and information cleaning from errors. Its purpose is to preserve information astuteness and highlight the efficacy of each model's execution by using the essential components of previous approaches. The proposed model evaluates the precision, performance, and quality enhancement by measuring the Mean Square Error (MSE), coefficient of determination (R2), and accuracy.
Keywords:
edge computing, machine learning, data analysis, model evaluation, IoT, convolutional neural networks, latency reduction, mean square errorDownloads
References
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Copyright (c) 2024 Muddassar Ali, Hamayun Khan, Muhammad Tausif Afzal Rana, Arshad Ali, Muhammad Zeeshan Baig, Saif Ur Rehman, Yazed Alsaawy
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