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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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Journal ArticleDOI

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

TL;DR: This paper proposes an adaptive task offloading and resource allocation algorithm in the MEC environment that has the best performance in reducing the task average response time and the total system energy consumption, improving the system utility, which meets the profits of users and service providers.
Journal ArticleDOI

Task Scheduling for Smart City Applications Based on Multi-Server Mobile Edge Computing

TL;DR: This paper introduces a cooperative strategy for IoV and formulates an optimization problem to minimize the completion time with a specified cost and develops four evolving variants based on the alternating direction method of multipliers (ADMM) algorithm to solve the proposed problem.
Journal ArticleDOI

IoT Data Compression and Optimization Techniques in Cloud Storage: Current Prospects and Future Directions

TL;DR: It is necessary to optimize the system’s performance by improving the data compression and storage optimization, according to KeywORDS.
Journal ArticleDOI

Distributed Optimization for Computation Offloading in Edge Computing

TL;DR: Numerical results based on extensive experiments have demonstrated that the distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic.
Proceedings Article

Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning

TL;DR: This paper proposes an efficient and flexible 2-step pruning framework for DNN partition between mobile devices and edge servers that can greatly reduce either the wireless transmission workload of the device or the total computation workload.
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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