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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
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Issues per year: 4
Current issue: Aug 2023
Next issue: Nov 2023
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PUBLISHER

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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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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  4/2018 - 2

 HIGHLY CITED PAPER 

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
 
View the paper record and citations in View the paper record and citations in Google Scholar
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: 1,003 | Views: 2,404

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

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 5 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 5
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Cited-By CrossRef

[1] Offloading Edge Vehicular Services in Realistic Urban Environments, Gilly, Katja, Mishev, Anastas, Filiposka, Sonja, Alcaraz, Salvador, IEEE Access, ISSN 2169-3536, Issue , 2020.
Digital Object Identifier: 10.1109/ACCESS.2020.2965258
[CrossRef]

[2] Mobile‚Äźaware dynamic resource management for edge computing, Filiposka, Sonja, Mishev, Anastas, Gilly, Katja, Transactions on Emerging Telecommunications Technologies, ISSN 2161-3915, Issue 6, Volume 30, 2019.
Digital Object Identifier: 10.1002/ett.3626
[CrossRef]

[3] Fault Tolerant Distributed Python Software Transactional Memory, POPOVIC, M., BASICEVIC, I., DJUKIC, M., POPOVIC, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.04003
[CrossRef] [Full text]

Updated 2 days, 19 hours ago

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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