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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2019-Dec-16
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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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  4/2018 - 2

Supporting Location Transparent Services in a Mobile Edge Computing Environment

GILLY, K. See more information about GILLY, K. on SCOPUS See more information about GILLY, K. on IEEExplore See more information about GILLY, K. on Web of Science, FILIPOSKA, S. See more information about  FILIPOSKA, S. on SCOPUS See more information about  FILIPOSKA, S. on SCOPUS See more information about FILIPOSKA, S. on Web of Science, MISHEV, A. See more information about MISHEV, A. on SCOPUS See more information about MISHEV, A. on SCOPUS See more information about MISHEV, A. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,495 KB) | Citation | Downloads: 401 | Views: 1,018

Author keywords
context-aware services, handover, mobile nodes, resource management, wireless networks

References keywords
computing(15), cloud(10), communications(9), internet(7), edge(6), migration(5), virtual(4), survey(4), resource(4), mobile(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 11 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04002
Web of Science Accession Number: 000451843400002
SCOPUS ID: 85058806251

Abstract
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Emerging models such as mobile edge computing provide the necessary characteristics for the deployment of Internet of Things applications by supplying the connected devices with local computing facilities essential for latency sensitive applications. One of the major issues of the underlying edge computing architecture is to cope with the device mobility that imposes dynamically changing network requirements. In this paper, we propose a resource management approach that aims to improve the location transparency and provide high quality of experience for end users by optimising latencies perceived when nodes are accessing services hosted on the edge of the network. By managing the virtualised computing resources based on the node location area information, the main objective of the approach is to minimise the network latency perceived by mobile nodes for both the initial allocation and the dynamic resource migration during the service lifetime while the requester node is changing location areas. The system is trying to achieve the most accurate 'follow me' service where the assigned resources closely follow the current mobile node location. The presented results show the effectiveness of the proposed solution in comparison to traditional resource management techniques on the macro and micro scale.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 59]


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[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 46]


[13] H. Gupta, A. Vahid Dastjerdi, S. K. Ghosh, R. Buyya, "ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments," Software: Practice and Experience vol. 47 no. 9, 2017, pp. 1275-1296.
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[14] M. Aazam, E.-N. Huh, "Dynamic resource provisioning through fog micro datacenter", IEEE International Conference on Pervasive Computing and Communication Workshops, USA, March 2015.
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[15] C. T. Do, N. H. Tran, C. Pham, M. G. R. Alam, J. H. Son, C. S. Hong, "A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing," 2015 IEEE International Conference on Information Networking (ICOIN), Jan. 2015, pp. 324-329.
[CrossRef] [SCOPUS Times Cited 52]


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[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 39]


[17] K. Velasquez, D. P. Abreu, M. R. M. Assis, C. Senna, D. F. Aranha, L. F. Bittencourt, N. Laranjeiro, M. Curado, M. Vieira, E. Monteiro, E. Madeira, "Fog orchestration for the Internet of Everything: state-of-the-art and research challenges," Journal of Internet Services and Applications 9:14, 2018.
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[18] C. Pahl, B. Lee, "Containers and clusters for edge cloud architectures-a technology review," 3rd IEEE International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy, Aug. 2015.
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[19] M. Abo-Zahhad, N. Sabor, S. Sasaki, S. M. Ahmed, "A centralized immune-voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks," Information Fusion vol. 30 issue C, July 2016, pp. 36-51.
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[CrossRef] [Web of Science Times Cited 698] [SCOPUS Times Cited 922]




References Weight

Web of Science® Citations for all references: 3,272 TCR
SCOPUS® Citations for all references: 5,191 TCR

Web of Science® Average Citations per reference: 131 ACR
SCOPUS® Average Citations per reference: 208 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-03-27 03:05 in 161 seconds.




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