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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Feb 2019
Next issue: May 2019
Avg review time: 80 days


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

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


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Remote Control of an Autonomous Robotic Platform Based on Eye Tracking, PASARICA, A., ANDRUSEAC, G. G., ADOCHIEI, I., ROTARIU, C., COSTIN, H., ADOCHIEI, F.
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An Enhanced Rule-Based Web Scanner Based on Similarity Score, LEE, M., LEE, Y., YOON, H.
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A High Optical Transmittance and Low Cost Touch Screen without Patterning, SAMADZAMINI, K., FROUNCHI, J., VELADI, H.
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Rule-Based Turkish Text Summarizer (RB-TTS), BIRANT, C. C., AKTAS, O.
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K-Linkage: A New Agglomerative Approach for Hierarchical Clustering, YILDIRIM, P., BIRANT, D.
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k-Degree Anonymity Model for Social Network Data Publishing, MACWAN, K. R., PATEL, S. J.
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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  1/2016 - 6

HIGH: A Hexagon-based Intelligent Grouping Approach in Wireless Sensor Networks

FAN, C.-S. See more information about FAN, C.-S. on SCOPUS See more information about FAN, C.-S. on IEEExplore See more information about FAN, C.-S. on Web of Science
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Download PDF pdficon (1,229 KB) | Citation | Downloads: 427 | Views: 1,478

Author keywords
wireless sensor networks, cluster head, energy efficiency, coverage ratio

References keywords
sensor(27), networks(20), deployment(11), systems(7), random(5), chen(5), information(4), computing(4), communications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-02-28
Volume 16, Issue 1, Year 2016, On page(s): 41 - 46
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.01006
Web of Science Accession Number: 000376995400006
SCOPUS ID: 84960075118

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In a random deployment or uniform deployment strategy, sensor nodes are scattered randomly or uniformly in the sensing field, respectively. Hence, the coverage ratio cannot be guaranteed. The coverage ratio of uniform deployment, in general, is larger than that of the random deployment strategy. However, a random deployment or uniform deployment strategy may cause unbalanced traffic pattern in wireless sensor networks (WSNs). Therefore, cluster heads (CHs) around the sink have larger loads than those farther away from the sink. That is, CHs close to the sink exhaust their energy earlier. In order to overcome the above problem, we propose a Hexagon-based Intelligent Grouping approacH in WSNs (called HIGH). The coverage, energy consumption and data routing issues are well investigated and taken into consideration in the proposed HIGH scheme. The simulation results validate our theoretical analysis and show that the proposed HIGH scheme achieves a satisfactory coverage ratio, balances the energy consumption among sensor nodes, and extends network lifetime significantly.

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

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[12] M. Won, H. Ra, T. Park and S. H. Son, "Modeling random deployment in wireless sensor networks for infrastructure-less cyber physical systems," The 2nd IEEE Int. Conf. on Cyber-Physical Systems, Networks, and Applications, pp. 81-86, Aug. 2014.
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References Weight

Web of Science® Citations for all references: 4,953 TCR
SCOPUS® Citations for all references: 7,932 TCR

Web of Science® Average Citations per reference: 198 ACR
SCOPUS® Average Citations per reference: 317 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 2019-04-22 00:56 in 166 seconds.

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

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