Detailed Information

Cited 49 time in webofscience Cited 59 time in scopus
Metadata Downloads

SoftEdgeNet: SDN Based Energy-Efficient Distributed Network Architecture For Edge Computing

Authors
Sharma, Pradip KumarRathore, ShailendraJeong, Young-SikPark, Jong Hyuk
Issue Date
Dec-2018
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE COMMUNICATIONS MAGAZINE, v.56, no.12, pp 104 - 111
Pages
8
Indexed
SCI
SCIE
SCOPUS
Journal Title
IEEE COMMUNICATIONS MAGAZINE
Volume
56
Number
12
Start Page
104
End Page
111
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/8856
DOI
10.1109/MCOM.2018.1700822
ISSN
0163-6804
1558-1896
Abstract
The volume of data traffic has increased exponentially due to the explosive growth of loT devices and the arrival of many new loT applications. Due to the large volume of data generated from loT devices, limited bandwidth, high latency, and real-time analysis requirements, the conventional centralized network architecture cannot meet users' requirements. Intensive real-time data analysis is one of the major challenges in current state-of-the-art centralized architectures due to the ubiquitous deployment of different types of sensors. To address the current challenges and adhere to the principles of architectural design, we are proposing a SoftEdgeNet model, which is a novel SDN-based distributed layered network architecture with a blockchain technique for a sustainable edge computing network. At the fog layer, we introduce an SDN-based secure fog node architecture to mitigate security attacks and provide real-time analytics services. We are also proposing a flow rule partition, and allocation algorithm at the edge of the network. The evaluation result shows our proposed model allows for a significant improvement of the interactions in real time data transmission. In terms of the ability to mitigate flooding attacks, the bandwidth is maintained above 9 Mb/s until the attack rates exceed 2000 PPS in the hardware environment and the bandwidth remained almost unchanged in the software environment. In the case of scalability of the proposed model, our proposed algorithm has performed better and proceeded linearly with the increase in traffic volume.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Advanced Convergence Engineering > Department of Computer Science and Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Jeong, Young Sik photo

Jeong, Young Sik
College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)
Read more

Altmetrics

Total Views & Downloads

BROWSE