Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing

The Internet of Things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading model that considers the constrained computation and radio resources of industrial IoT devices to guarantee efficient sharing of resources between multiple users. This model is formulated as an optimization problem with the goal of decreasing energy consumption and computation delay. This type of problem is non-deterministic polynomial time-hard due to the curse-of-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. In addition, a 128-bit Advanced Encryption Standard-based cryptographic approach is proposed to satisfy the data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead in terms of energy and time by up to 64.7% in comparison with the local execution approach. It also outperforms the full offloading scenario by up to 13.2%, where it can select some computation tasks to be offloaded while optimally rejecting others. Finally, it is adaptable and scalable for a large number of mobile devices. © Copyright 2021, Mary Ann Liebert, Inc., publishers.

Elgendy I.A.1, 2 , Muthanna A. 3, 4 , Hammoudeh M.5 , Shaiba H.6 , Unal D.7 , Khayyat M.8
Mary Ann Liebert Inc.
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  • 1 Department of Computer Science and Technology, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
  • 2 Department of Computer Science, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt
  • 3 Department of Communication Networks and Data Transmission, St. Petersburg State University of Telecommunication, St. Petersburg, Russian Federation
  • 4 Applied Mathematics and Communications Technology Institute, Peoples' Friendship University of Russia (RUDN University), Moscow, Russian Federation
  • 5 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
  • 6 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
  • 7 Department of Electrical Engineering, KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar
  • 8 Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
5G; computation offloading; deep reinforcement learning; mobile edge computing; security
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