An Efficient Multi-class Message Scheduling Scheme for Healthcare IoT Systems Article Swipe
Keehyun Park
,
Insung Kim
,
Joonsuu Park
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.14257/ijgdc.2018.11.5.06
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.14257/ijgdc.2018.11.5.06
The Internet of Things (IoT) concept, which involves communication within a network of objects, has become increasingly popular, and development in this area of technology is quite active.IoT environments involve the generation of large amounts of data, and require various levels of Quality of Service.For this reason, message scheduling schemes to deliver the data in IoT environments are considered to be essential.This paper proposes an efficient multi-class message scheduling algorithm for healthcare IoT environments.In the proposed message algorithm, messages are grouped into three message classes (UNC, RT and DT classes) based on the characteristics of the messages.The proposed message algorithm is resourceefficient because it uses a simpler priority calculation than the Multi-class Q-Learning message scheduling algorithm, which we proposed for IoT systems previously.Tests were conducted on a part of the oneM2M-based IoT system we have built.These showed that in most cases, the proposed scheduling algorithm performs better than the Multi-class Q-Learning scheduling algorithm.
Related Topics
Concepts
Computer science
Scheme (mathematics)
Internet of Things
Scheduling (production processes)
Class (philosophy)
Distributed computing
Computer network
Healthcare system
Health care
Computer security
Artificial intelligence
Mathematical optimization
Mathematics
Economic growth
Economics
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.14257/ijgdc.2018.11.5.06
- https://doi.org/10.14257/ijgdc.2018.11.5.06
- OA Status
- bronze
- Cited By
- 2
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2805296622
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2805296622Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14257/ijgdc.2018.11.5.06Digital Object Identifier
- Title
-
An Efficient Multi-class Message Scheduling Scheme for Healthcare IoT SystemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-05-31Full publication date if available
- Authors
-
Keehyun Park, Insung Kim, Joonsuu ParkList of authors in order
- Landing page
-
https://doi.org/10.14257/ijgdc.2018.11.5.06Publisher landing page
- PDF URL
-
https://doi.org/10.14257/ijgdc.2018.11.5.06Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://doi.org/10.14257/ijgdc.2018.11.5.06Direct OA link when available
- Concepts
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Computer science, Scheme (mathematics), Internet of Things, Scheduling (production processes), Class (philosophy), Distributed computing, Computer network, Healthcare system, Health care, Computer security, Artificial intelligence, Mathematical optimization, Mathematics, Economic growth, Economics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2019: 1Per-year citation counts (last 5 years)
- References (count)
-
18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.system | 132 |
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| abstract_inverted_index.grouped | 80 |
| abstract_inverted_index.involve | 29 |
| abstract_inverted_index.message | 47, 67, 76, 83, 98, 113 |
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| abstract_inverted_index.concept, | 5 |
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| abstract_inverted_index.algorithm | 69, 99, 144 |
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| abstract_inverted_index.efficient | 65 |
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| corresponding_author_ids | https://openalex.org/A5032703164 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I52010207 |
| citation_normalized_percentile.value | 0.53697961 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |