Open Access

ARTICLE

Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

Ilyоs Abdullaev¹, Natalia Prodanova², K. Aruna Bhaskar³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Jungeun Kim^8,*

1 Dean of the Faculty of Economics, Department of Management and Marketing, Faculty of Economics, Urgench State University, Urganch, 220100, Uzbekistan
2 Basic Department Financial Control, Analysis and Audit of Moscow Main Control Department, Plekhanov Russian University of Economics, Moscow, 117997, Russia
3 Department of Computer Science and Engineering, KL Deemed to University, Vaddeswaram, Guntur, Andhra Pradesh, India
4 Department of Computer Science and Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, India
5 Department of Applied Data Science, Noroff University College, Kristiansand, Norway
6 Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates
7 Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
8 Department of Software, Kongju National University, Cheonan, 31080, Korea

* Corresponding Author: Jungeun Kim. Email:

Computers, Materials & Continua 2023, 76(2), 1463-1477. https://doi.org/10.32604/cmc.2023.038417

Received 12 December 2022; Accepted 16 March 2023; Issue published 30 August 2023

Abstract

Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computation-intensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server, which enables an optimum offloading decision to minimize the system cost. In addition, an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources. The TORA-DLSGO technique uses the deep belief network (DBN) model for optimum offloading decision-making. Finally, the SGO algorithm is used for the parameter tuning of the DBN model. The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

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Keywords

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