Generative Service Provisioning for IoT Devices Using Line Graph Structure
A service subgraph helps Internet-of-Things devices access resources in a dynamic Internet-of-Things device network. We propose a service subgraph generation method for Internet-of-Things device networks. Service subgraph generation aims to find more capable neighboring Internet-of-Things devices fo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10043850/ |
| _version_ | 1797902207642763264 |
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| author | Joohyun Kim Jae-Hoon Kim |
| author_facet | Joohyun Kim Jae-Hoon Kim |
| author_sort | Joohyun Kim |
| collection | DOAJ |
| description | A service subgraph helps Internet-of-Things devices access resources in a dynamic Internet-of-Things device network. We propose a service subgraph generation method for Internet-of-Things device networks. Service subgraph generation aims to find more capable neighboring Internet-of-Things devices for service provisioning. We apply a line graph structure for an adequate representation of device resources. The line graph structure effectively represents the resources in the generated service subgraph. A general node classification problem constituting the generated service subgraph identifies the appropriate resource binding for service provisioning. A node in the service subgraph corresponds to a unique relationship between devices. Service provisioning is guaranteed by reinforcement learning based on the resource binding identified by node classification. The proposed line graph structure and resource binding significantly enhance the traditional intelligent resource allocation method. In addition, the proposed scheme can effectively attain service subgraphs with very low computational complexity. The proposed generative service provisioning generally has a significantly lower occupation probability than the swarm intelligence-based algorithm. The average value of the occupation probability is 0.49 with the proposed method. It is 0.12 lower than that of swarm intelligence-based algorithm. |
| first_indexed | 2024-04-10T09:14:04Z |
| format | Article |
| id | doaj.art-370f10b25919495095ca27641cc307fa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:14:04Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-370f10b25919495095ca27641cc307fa2023-02-21T00:01:25ZengIEEEIEEE Access2169-35362023-01-0111154961550410.1109/ACCESS.2023.324489010043850Generative Service Provisioning for IoT Devices Using Line Graph StructureJoohyun Kim0Jae-Hoon Kim1https://orcid.org/0000-0002-4512-5275Department of Industrial Engineering, Ajou University, Suwon, South KoreaDepartment of Industrial Engineering, Ajou University, Suwon, South KoreaA service subgraph helps Internet-of-Things devices access resources in a dynamic Internet-of-Things device network. We propose a service subgraph generation method for Internet-of-Things device networks. Service subgraph generation aims to find more capable neighboring Internet-of-Things devices for service provisioning. We apply a line graph structure for an adequate representation of device resources. The line graph structure effectively represents the resources in the generated service subgraph. A general node classification problem constituting the generated service subgraph identifies the appropriate resource binding for service provisioning. A node in the service subgraph corresponds to a unique relationship between devices. Service provisioning is guaranteed by reinforcement learning based on the resource binding identified by node classification. The proposed line graph structure and resource binding significantly enhance the traditional intelligent resource allocation method. In addition, the proposed scheme can effectively attain service subgraphs with very low computational complexity. The proposed generative service provisioning generally has a significantly lower occupation probability than the swarm intelligence-based algorithm. The average value of the occupation probability is 0.49 with the proposed method. It is 0.12 lower than that of swarm intelligence-based algorithm.https://ieeexplore.ieee.org/document/10043850/Internet of Thingsline graphreinforcement learningservice provisioningsubgraph |
| spellingShingle | Joohyun Kim Jae-Hoon Kim Generative Service Provisioning for IoT Devices Using Line Graph Structure IEEE Access Internet of Things line graph reinforcement learning service provisioning subgraph |
| title | Generative Service Provisioning for IoT Devices Using Line Graph Structure |
| title_full | Generative Service Provisioning for IoT Devices Using Line Graph Structure |
| title_fullStr | Generative Service Provisioning for IoT Devices Using Line Graph Structure |
| title_full_unstemmed | Generative Service Provisioning for IoT Devices Using Line Graph Structure |
| title_short | Generative Service Provisioning for IoT Devices Using Line Graph Structure |
| title_sort | generative service provisioning for iot devices using line graph structure |
| topic | Internet of Things line graph reinforcement learning service provisioning subgraph |
| url | https://ieeexplore.ieee.org/document/10043850/ |
| work_keys_str_mv | AT joohyunkim generativeserviceprovisioningforiotdevicesusinglinegraphstructure AT jaehoonkim generativeserviceprovisioningforiotdevicesusinglinegraphstructure |