A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT Article Swipe
Dhananjay Bisen
,
Umesh Kumar Lilhore
,
Poongodi Manoharan
,
Fadl Dahan
,
Olfa Mzoughi
,
Fahima Hajjej
,
Praneet Saurabh
,
Kaamran Raahemifar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12061384
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12061384
In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. We utilised a CNN to determine the best-transferring strategy and the most efficient partitioning of a specific task. The MKM method has more than one cluster head in each cluster to lead. It also reduces the number of reclustering cycles, which helps to overcome the energy consumption and delay during the reclustering process. The proposed model determines a training dataset by covering all the aspects of cost function calculation. This training dataset helps to train the model, which allows for efficient decision-making in optimum energy usage. In MEC, clusters have a dynamic nature and frequently change their location. Sometimes, this creates hurdles for the clusters to form a cluster head and, finally, abandons the cluster. The selected cluster heads must be recognised correctly and applied to maintain and supervise the clusters. The proposed pairing of the modified k-means method with a CNN fulfils this objective. The proposed method, existing weighted clustering algorithm (WCA), and agent-based secure enhanced performance approach (AB-SEP) are tested over the network dataset. The findings of our experiment demonstrate that the proposed hybrid model is promising in aspects of CD energy consumption, overhead, packet loss rate, packet delivery ratio, and throughput compared to existing approaches.
Related Topics
Concepts
Cluster analysis
Computer science
Energy consumption
Overhead (engineering)
Network packet
Enhanced Data Rates for GSM Evolution
Path (computing)
Artificial intelligence
Energy (signal processing)
Process (computing)
Efficient energy use
Data mining
Convolutional neural network
Cluster (spacecraft)
Artificial neural network
Pattern recognition (psychology)
Engineering
Computer network
Mathematics
Operating system
Electrical engineering
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12061384
- https://www.mdpi.com/2079-9292/12/6/1384/pdf?version=1681115117
- OA Status
- gold
- Cited By
- 18
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4324140942
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4324140942Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12061384Digital Object Identifier
- Title
-
A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoTWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-14Full publication date if available
- Authors
-
Dhananjay Bisen, Umesh Kumar Lilhore, Poongodi Manoharan, Fadl Dahan, Olfa Mzoughi, Fahima Hajjej, Praneet Saurabh, Kaamran RaahemifarList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12061384Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/6/1384/pdf?version=1681115117Direct 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://www.mdpi.com/2079-9292/12/6/1384/pdf?version=1681115117Direct OA link when available
- Concepts
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Cluster analysis, Computer science, Energy consumption, Overhead (engineering), Network packet, Enhanced Data Rates for GSM Evolution, Path (computing), Artificial intelligence, Energy (signal processing), Process (computing), Efficient energy use, Data mining, Convolutional neural network, Cluster (spacecraft), Artificial neural network, Pattern recognition (psychology), Engineering, Computer network, Mathematics, Operating system, Electrical engineering, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 12, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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