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Open AccessJournal ArticleDOI

Mobile Edge Computing: A Survey

TLDR
The definition of MEC, its advantages, architectures, and application areas are provided; where the security and privacy issues and related existing solutions are also discussed.
Abstract
Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

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Mobile Edge Computing: A
Survey
architecture, applications, approaches and challenges
Nasir Abbas
Master’s Thesis Autumn 2016


Mobile Edge Computing: A Survey
Nasir Abbas
December 12, 2016

ii

Abstract
Mobile edge computing (MEC) is an emergent architecture where cloud
computing services are extended to the edge of networks into the mobile
base stations. As a promising edge technology, it can be applied to mobile,
wireless and wireline scenarios, using software and hardware platforms,
located at the network edge in the vicinity of end users. MEC provides
seamless integration of multiple application service providers and vendors
towards mobile subscribers, enterprises and other vertical segments. It is
an important component in the proposed 5G architecture that supports
variety of innovative applications and services where ultra low latency
is required. However, there are some challenges exists in the MEC eco
system. To address these challenges, first off need to understand the
network infrastructure of MEC, cloud and cellular network.
Some questions and problems are addressed in this thesis that outlines
the importance and challenges of MEC deployment. Impact of MEC
integration with the traditional mobile and cloud network appears in
this paper. A survey has been presented that contributes in general
understanding of mobile edge computing (MEC). Readers will have
an overview of MEC, such as definition, advantages, architectures and
applications. Moreover, related research and future directions are pointed
out in this thesis. Finally, security and privacy issues and their possible
solutions are also discussed.
iii

Citations
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Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

TL;DR: This survey discusses the current state of research on blockchain and FL, and compares the leading technologies and limitations, and discusses how to integrate blockchain andFL into UAV edge computing networks and the associated challenges and solutions.
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Acceptance of IoT Edge-Computing-Based Sensors in Smart Cities for Universal Design Purposes

TL;DR: In this paper , an online survey was conducted to identify and evaluate factors influencing the acceptance of data collection by IoT devices for universal design, which indicated that empathy for the disabled moderates and increases the positive relationship between the perceived usefulness of IoT devices and their acceptance.
Journal ArticleDOI

Towards Diversified IoT Image Recognition Services in Mobile Edge Computing

TL;DR: Huang et al. as discussed by the authors proposed an IoT image recognition services framework for different needs in the MEC environment, which consists of Hierarchical Discriminative Feature Extraction (HDFE) and Sub-extractor Deployment (Sub-ED) algorithms.
Proceedings ArticleDOI

A Customized Reinforcement Learning based Binary Offloading in Edge Cloud

TL;DR: In this paper, a customized Q-learning based approach is proposed to reduce the task completion time and improve the quality-of-service (QoS) of the microservices.
References
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ReportDOI

The NIST Definition of Cloud Computing

Peter Mell, +1 more
TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
Journal ArticleDOI

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

TL;DR: An overview of the Internet of Things with emphasis on enabling technologies, protocols, and application issues, and some of the key IoT challenges presented in the recent literature are provided and a summary of related research work is provided.
Journal ArticleDOI

Recent advances in augmented reality

TL;DR: This work refers one to the original survey for descriptions of potential applications, summaries of AR system characteristics, and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.
Journal ArticleDOI

The Case for VM-Based Cloudlets in Mobile Computing

TL;DR: The results from a proof-of-concept prototype suggest that VM technology can indeed help meet the need for rapid customization of infrastructure for diverse applications, and this article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them.
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