Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

Al Moteri, Moteeb; Khan, Surbhi Bhatia; Alojail, Mohammed

Abstract

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

Citation

Al Moteri, M., Khan, S. B., & Alojail, M. (in press). Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT. Systems, 11(6), 308. https://doi.org/10.3390/systems11060308

• Journal Article Type: Article
• Acceptance Date: Jun 12, 2023
• Online Publication Date: Jun 16, 2023
• Deposit Date: Jul 6, 2023
• Publicly Available Date: Jul 6, 2023
• Journal: Systems
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 6
• Pages: 308
• DOI: https://doi.org/10.3390/systems11060308
• Keywords: Information Systems and Management, Computer Networks and Communications, Modeling and Simulation, Control and Systems Engineering, Software