Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment Article Swipe
Mohammed Aljebreen
,
Marwa Obayya
,
Hany Mahgoub
,
Saud S. Alotaibi
,
Abdullah Mohamed
,
Manar Ahmed Hamza
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3345803
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3345803
Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network’s efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.
Related Topics
Concepts
Computer science
Load balancing (electrical power)
Network packet
Distributed computing
Cluster analysis
Chaotic
Efficient energy use
Internet of Things
Throughput
Process (computing)
Artificial intelligence
Machine learning
Computer network
Wireless
Computer security
Grid
Operating system
Mathematics
Engineering
Electrical engineering
Telecommunications
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3345803
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10368015.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390075110
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390075110Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2023.3345803Digital Object Identifier
- Title
-
Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things EnvironmentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-21Full publication date if available
- Authors
-
Mohammed Aljebreen, Marwa Obayya, Hany Mahgoub, Saud S. Alotaibi, Abdullah Mohamed, Manar Ahmed HamzaList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2023.3345803Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10368015.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10368015.pdfDirect OA link when available
- Concepts
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Computer science, Load balancing (electrical power), Network packet, Distributed computing, Cluster analysis, Chaotic, Efficient energy use, Internet of Things, Throughput, Process (computing), Artificial intelligence, Machine learning, Computer network, Wireless, Computer security, Grid, Operating system, Mathematics, Engineering, Electrical engineering, Telecommunications, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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