Next Article in Journal
A New Approach to Estimating the Path Loss in Underground Wireless Sensor Networks
Previous Article in Journal
Can a Network Attack Be Simulated in an Emulated Environment for Network Security Training?
Article Menu

Export Article

Open AccessArticle
J. Sens. Actuator Netw. 2017, 6(3), 17; doi:10.3390/jsan6030017

RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments

1
Department of Computer Science, COMSATS Institute of Information Technology, Wah Cantt 47040, Pakistan
2
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
3
School of Software and Electrical Engineering, Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne EN511c, Australia
4
Department of Computer Science, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
5
Department of Computer Science, Air University, Islamabad 44000, Pakistan
6
School of Computing and Digital Technology, Birmingham City University, Birmingham B47XJ, UK
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 1 June 2017 / Revised: 7 August 2017 / Accepted: 9 August 2017 / Published: 15 August 2017
View Full-Text   |   Download PDF [2140 KB, uploaded 15 August 2017]   |  

Abstract

We are witnessing the emergence of new big data processing architectures due to the convergence of the Internet of Things (IoTs), edge computing and cloud computing. Existing big data processing architectures are underpinned by the transfer of raw data streams to the cloud computing environment for processing and analysis. This operation is expensive and fails to meet the real-time processing needs of IoT applications. In this article, we present and evaluate a novel big data processing architecture named RedEdge (i.e., data reduction on the edge) that incorporates mechanism to facilitate the processing of big data streams near the source of the data. The RedEdge model leverages mobile IoT-termed mobile edge devices as primary data processing platforms. However, in the case of the unavailability of computational and battery power resources, it offloads data streams in nearer mobile edge devices or to the cloud. We evaluate the RedEdge architecture and the related mechanism within a real-world experiment setting involving 12 mobile users. The experimental evaluation reveals that the RedEdge model has the capability to reduce big data stream by up to 92.86% without compromising energy and memory consumption on mobile edge devices. View Full-Text
Keywords: fog computing; mobile edge computing; cloud computing; mobile computing; big data reduction fog computing; mobile edge computing; cloud computing; mobile computing; big data reduction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Habib ur Rehman, M.; Jayaraman, P.P.; Malik, S.R.; Khan, A.R.; Medhat Gaber, M. RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments. J. Sens. Actuator Netw. 2017, 6, 17.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Sens. Actuator Netw. EISSN 2224-2708 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top