Intelligent Mobile Edge Computing Networks for Internet of Things

Liming Chen,Junjuan Xia,Fusheng Zhu,Xiaoyun Kuang

doi:10.1109/access.2021.3093886

Abstract

In this work, an intelligent mobile edge computing (MEC) network is studied for Internet of Things (IoT) in the presence of eavesdropping environments, where there are multiple users who can offload their confidential tasks to the computational access point (CAP) for the assistance of computation. One unmanned aerial vehicle (UAV) attacker exists in the system and it can listen to the confidential data transmission from the users to the CAP. We optimize the system design of the intelligent MEC network, by adaptively allocating the offloading ratio and wireless bandwidth, to reduce the linearly weighted cost of the latency as well as energy consumption (EnC). Specifically, starting from the deep reinforcement learning, we devise a deep Q-network (DQN) network to adjust the offloading ratio and transmission bandwidth, which can help calculate the computational tasks and suppress the eavesdropping from the UAV efficiently. We finally provide some simulation results to validate the proposed offloading strategy. In particular, the proposed offloading strategy can achieve a much lower cost compared to the conventional ones, in the terms of latency and EnC.

Highlights

In recent years, with the rapid development of transmission technology, the data rate of wireless networks has increased significantly [1]–[3], which has promoted the development of Internet of Things (IoT) [4], [5]
The computational capability of mobile devices is very limited, and processing large-scale data will cause a lot of system latency, which will seriously reduce the quality of user services [12]–[14]
To deal with this problem, researchers have proposed cloud computing solutions, where the computational tasks of mobile device can be offloaded to the cloud server for the assistance of computation, and the results will be fedback to the mobile devices

Summary

Introduction

With the rapid development of transmission technology, the data rate of wireless networks has increased significantly [1]–[3], which has promoted the development of Internet of Things (IoT) [4], [5]. The computational capability of mobile devices is very limited, and processing large-scale data will cause a lot of system latency, which will seriously reduce the quality of user services [12]–[14]. To deal with this problem, researchers have proposed cloud computing solutions, where the computational tasks of mobile device can be offloaded to the cloud server for the assistance of computation, and the results will be fedback to the mobile devices. Researchers proposed mobile edge computing (MEC) technique, which is an extension of cloud computing technology [15]–[17]. An accessible computational access point (CAP) is established at the edge of a mobile network, providing mobile devices with intelligent services, such as the computational

Objectives

Results

Conclusion