Security-Aware computation offloading for Mobile edge computing-Enabled smart city

Abstract

Smart city has obtained increasing attention from both academic and industry which has the potential to improve human living standards. A smart city consists of a great number of smart devices which are generating large amounts of data and emerging applications all the time. However, the computing capacity of smart devices are limited. Fortunately, the emergence of MEC can solve the above problem. However, the resources of edge servers in MEC are limited and the capabilities of edge servers are heterogeneous. It is important to improve the average resource utilization of all edge servers and keep load balancing of edge server cluster simultaneously. On the other hand, quite a few numbers of applications are delay-sensitive, it is necessary to ensure the security of these applications. In this paper, we consider joint optimization of mobile device and edge server in MEC-enabled smart city, improving the overall performance of the system. Technically, a new multi-objective computation offloading method is implemented to reduce time consumption, energy consumption, and keep load balancing of edge servers, as well as increase average resource utilization of edge servers while meeting the deadline constraint of delay-sensitive applications. Sufficient experiments have been conducted to prove the effectiveness and superiority of our proposed method in different scenarios.

A Recommendation of a System Integrated With Edge Nodes, Multi-Cloud Instance and Decision-Making Mechanism for Voting System

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Intelligent computation offloading for educational virtual reality applications in smart campus using MoCell

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“…The deadline constrain and battery capacity constraint need to be considered in the offloading decision to ensure the security of AR applications. 52 In this article, the double constraints contain task latency and VD energy consumption is leveraged to represent security constraint model, which are represented as…”

Hybrid Fuzzy Archimedes‐based Light GBM‐XGBoost model for distributed task scheduling in mobile edge computing

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