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

机译：在工业移动边缘计算中的资源分配和安全意识数据卸载的高级深度学习

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摘要

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.
机译：事物互联网（物联网）通过连续环境监测和数据收集渗透我们的日常生活。低延迟通信，增强安全性和高效带宽利用率的承诺导致从移动云计算到移动边缘计算的转换。在这项研究中，我们提出了一种先进的深度加强资源分配和安全感感知数据卸载模型，其考虑了工业物联网设备的约束计算和无线电资源，以保证多个用户之间的资源共享。该模型作为优化问题，其目的是降低能量消耗和计算延迟。由于诅咒挑战，这种类型的问题是非确定性多项式艰难的挑战，因此，提出了深度学习优化方法以找到最佳解决方案。此外，提出了一种128位高级加密标准的加密方法，以满足数据安全要求。实验评价结果表明，与本地执行方法相比，所提出的模型可以减少能量和时间卸载高达64.7％。它还优于完全卸载方案，最高可达13.2％，在那里它可以选择一些要卸载的计算任务，同时最佳地拒绝他人。最后，它适用于大量移动设备。

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  • 来源
    《Big Data》 |2021年第4期|265-278|共14页
  • 作者

    Ibrahim A. Elgendy; Ammar Muthanna; Mohammad Hammoudeh; Hadil Shaiba; Devrim Unal; Mashael Khayyat;

  • 作者单位

    Department of Computer Science and Technology School of Computer Science and Technology Harbin Institute of Technology|Department of Computer Science Faculty of Computers and Information Menoufia University Menoufia;

    Department of Communication Networks and Data Transmission|Applied Mathematics and Communications Technology Institute Peoples' Friendship University of Russia (RUDN University);

    Department of Computing and Mathematics Manchester Metropolitan University;

    Department of Computer Science College of Computer and Information Sciences Princess Nourah bint Abdulrahman University;

    Department of Electrical Engineering KINDI Center for Computing Research College of Engineering Qatar University;

    Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    5G; computation offloading; deep reinforcement learning; mobile edge computing; security;

    机译：5G;计算卸载;深增强学习;移动边缘计算;安全性;

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