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JCR Impact Factor: 0.700
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Issues per year: 4
Current issue: Aug 2024
Next issue: Nov 2024
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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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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.

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  2/2020 - 2

IoT Framework for Interoperability in the oneM2M Architecture

KANG, S. See more information about KANG, S. on SCOPUS See more information about KANG, S. on IEEExplore See more information about KANG, S. on Web of Science, CHUNG, K. See more information about CHUNG, K. on SCOPUS See more information about CHUNG, K. on SCOPUS See more information about CHUNG, K. on Web of Science
 
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Download PDF pdficon (1,808 KB) | Citation | Downloads: 985 | Views: 2,257

Author keywords
inference mechanisms, information science, internet of things, semantic web, standardization

References keywords
semantic(15), internet(12), interoperability(8), ontology(5), computing(5), access(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02002
Web of Science Accession Number: 000537943500002
SCOPUS ID: 85087450714

Abstract
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The IoT is expected that many devices and sensors can be interconnected and interact over the Internet. Conventional IoT solutions rely on vertically developed machine-to-machine solutions that yield limited interoperability. To ensure interoperability between IoT solutions, the oneM2M global initiative defines a horizontal M2M service layer. To provide more intelligent services, such as autonomous interaction services, semantic-level interoperability should be ensured. Previous studies have proposed solutions based on ontologies to realize semantic level interoperability. However, in dynamic environments such as IoT, where data generated by many devices must be processed, an ontology leads to a system performance degradation owing the overhead of the resource mapping mechanism. In this study, we propose a semantic IoT framework based on the Resource Description Framework graph extension scheme. We utilize an aggregator based on the oneM2M standard platform. All data are represented as an RDF graph, and reconfigured dynamically through semantic queries. The proposed semantic IoT gateway provides a user-based rule management mechanism via the Web, thereby enabling rule configuration to be dynamically tailored to user requirements. Finally, the performance is evaluated compared with a solution that utilizes an ontology in a real IoT system.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 1 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 3
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Cited-By CrossRef

[1] Behind the definition of Industry 5.0: a systematic review of technologies, principles, components, and values, Ghobakhloo, Morteza, Iranmanesh, Mohammad, Tseng, Ming-Lang, Grybauskas, Andrius, Stefanini, Alessandro, Amran, Azlan, Journal of Industrial and Production Engineering, ISSN 2168-1015, Issue 6, Volume 40, 2023.
Digital Object Identifier: 10.1080/21681015.2023.2216701
[CrossRef]

[2] IoT based Smart Healthcare Monitoring Systems: A Review, Tiwari, Divyanshu, Prasad, Devendra, Guleria, Kalpna, Ghosh, Pinaki, 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), ISBN 978-1-6654-2554-4, 2021.
Digital Object Identifier: 10.1109/ISPCC53510.2021.9609393
[CrossRef]

[3] Semantic Interoperability and Reusability in IoT: a Systematic Mapping Study, Alsaeh, Amal, Sezen, Arda, 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), ISBN 979-8-3315-3149-2, 2024.
Digital Object Identifier: 10.1109/IDAP64064.2024.10710911
[CrossRef]

Updated 2 days, 4 hours ago

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Faculty of Electrical Engineering and Computer Science
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