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

• Published in: Computers, materials & continua Vol. 76; no. 2; pp. 1463 - 1477
• Main Authors: Abdullaev, Ilyоs, Prodanova, Natalia, Aruna Bhaskar, K., Laxmi Lydia, E., Kadry, Seifedine, Kim, Jungeun
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 01.01.2023
• Subjects: Algorithms; Belief networks; Cloud computing; Computation offloading; Deep learning; Edge computing; Energy consumption; Mobile computing; Optimization; Real time; Resource allocation; Resource management
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2023.038417

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.