scholarly journals The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G

2021 ◽  
Author(s):  
Mithun Mukherjee ◽  
Vikas Kumar ◽  
Mian Guo ◽  
Daniel B. da Costa ◽  
Ertugrul Basar ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Physical Layer Security ◽  
Research Trends ◽  
Edge Computing ◽  
System Level ◽  
Mobile Edge Computing ◽  
Enabling Technology ◽  
Future Evolution ◽  
Sixth Generation ◽  
Cloud Server

Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.

scholarly journals The Interplay of Reconfigurable Intelligent Surfaces and Mobile Edge Computing in Future Wireless Networks: A Win-Win Strategy to 6G

2021 ◽  
Author(s):  
Mithun Mukherjee ◽  
Vikas Kumar ◽  
Mian Guo ◽  
Daniel B. da Costa ◽  
Ertugrul Basar ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Physical Layer Security ◽  
Research Trends ◽  
Edge Computing ◽  
System Level ◽  
Mobile Edge Computing ◽  
Enabling Technology ◽  
Future Evolution ◽  
Sixth Generation ◽  
Cloud Server

Reconfigurable intelligent surface (RIS)-empowered communication is being considered as an enabling technology for sixth generation (6G) wireless networks. The key idea of RIS-assisted communication is to enhance the capacity, coverage, energy efficiency, physical layer security, and many other aspects of modern wireless networks. At the same time, mobile edge computing (MEC) has already shown its huge potential by extending the computation, communication, and caching capabilities of a standalone cloud server to the network edge. In this article, we first provide an overview of how MEC and RIS can benefit each other. We envision that the integration of MEC and RIS will bring an unprecedented transformation to the future evolution of wireless networks. We provide a system-level perspective on the MEC-aided RIS (and RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline some of the fundamental challenges that pertain to the implementation of MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends in the RIS-assisted MEC are discussed.

scholarly journals IEEE Access Special Section Editorial: Mobile Edge Computing for Wireless Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 11439-11442 ◽  
Author(s):  
Guanding Yu ◽  
Jun Zhang ◽  
Victor C. M. Leung ◽  
Marios Kountouris ◽  
Chonggang Wang
Keyword(s):  
Wireless Networks ◽  
Special Section ◽  
Edge Computing ◽  
Mobile Edge Computing

scholarly journals Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1446 ◽  
Author(s):  
Liang Huang ◽  
Xu Feng ◽  
Luxin Zhang ◽  
Liping Qian ◽  
Yuan Wu
Keyword(s):  
Deep Learning ◽  
Low Complexity ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Wireless Devices ◽  
Cloud Server ◽  
Convergence Performance ◽  
Multi Server

This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs’ energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.

scholarly journals Privacy-Aware Online Task Offloading for Mobile-Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Dali Zhu ◽  
Ting Li ◽  
Haitao Liu ◽  
Jiyan Sun ◽  
Liru Geng ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Location Privacy ◽  
Optimization Problems ◽  
Energy Performance ◽  
Edge Computing ◽  
System Level ◽  
Mobile Edge Computing ◽  
Task Offloading

Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation-demanding and latency-critical tasks to the resource-rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and user-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near-optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi-armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy-aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.

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