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Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Fadwa Alrowais1, Ahmed S. Almasoud2, Radwa Marzouk3, Fahd N. Al-Wesabi4,5, Anwer Mustafa Hilal6,*, Mohammed Rizwanullah6, Abdelwahed Motwakel6, Ishfaq Yaseen6

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Rafha Street, Riyadh, 11586, Saudi Arabia
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Computer Science, College of Science & Arts at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
5 Department of Information Systems, Faculty of Computer and Information Technology, Sana'a University, 61101, Yemen
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email: email

Computers, Materials & Continua 2022, 72(2), 2783-2795. https://doi.org/10.32604/cmc.2022.025204

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 a method 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 AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.

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APA Style
Alrowais, F., Almasoud, A.S., Marzouk, R., Al-Wesabi, F.N., Hilal, A.M. et al. (2022). Artificial intelligence based data offloading technique for secure MEC systems. Computers, Materials & Continua, 72(2), 2783-2795. https://doi.org/10.32604/cmc.2022.025204
Vancouver Style
Alrowais F, Almasoud AS, Marzouk R, Al-Wesabi FN, Hilal AM, Rizwanullah M, et al. Artificial intelligence based data offloading technique for secure MEC systems. Comput Mater Contin. 2022;72(2):2783-2795 https://doi.org/10.32604/cmc.2022.025204
IEEE Style
F. Alrowais et al., "Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems," Comput. Mater. Contin., vol. 72, no. 2, pp. 2783-2795. 2022. https://doi.org/10.32604/cmc.2022.025204



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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