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Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems.

Authors :
Alrowais, Fadwa
Almasoud, Ahmed S.
Marzouk, Radwa
Al-Wesabi, Fahd N.
Hilal, Anwer Mustafa
Rizwanullah, Mohammed
Motwakel, Abdelwahed
Yaseen, Ishfaq
Source :
Computers, Materials & Continua; 2022, Vol. 72 Issue 2, p2783-2795, 13p
Publication Year :
2022

Abstract

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves amethod in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks (CTT) of 0.680. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15462218
Volume :
72
Issue :
2
Database :
Complementary Index
Journal :
Computers, Materials & Continua
Publication Type :
Academic Journal
Accession number :
156150312
Full Text :
https://doi.org/10.32604/cmc.2022.025204