Learning to allocate: a delay and temperature-aware slot allocation framework for WBAN with TDMA-MAC.
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| Title: | Learning to allocate: a delay and temperature-aware slot allocation framework for WBAN with TDMA-MAC. |
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| Authors: | Mystica, K. Jasmine1 (AUTHOR) jasminemysticak@gmail.com, Manickam, J. Martin Leo1 (AUTHOR) josephmartin74@gmail.com |
| Source: | Wireless Networks (10220038). Jan2025, Vol. 31 Issue 1, p165-183. 19p. |
| Subjects: | Institute of Electrical & Electronics Engineers, Time division multiple access, Body area networks, Artificial intelligence, Telecommunication, Reinforcement learning, Bipartite graphs |
| Abstract: | Data aggregation in the Wireless Body Area Networks (WBAN) is a multidimensional problem. It can be addressed at different levels of the network. The proposed work identifies the scheduling problem for the slots in a Time Division Multiple Access Medium Access Control (TDMA-MAC) superframe. A resource-constrained single channel WBAN is considered, and the proposed work models the set of nodes and slots of one subframe as a bipartite graph and aims to obtain a globally optimal matching solution for one subframe with end-to-end latency and temperature rise minimization as prime goals. Later, it extends the solution to the entire superframe, which consists of several subframes. The proposed Learning to Allocate (LTA) framework uses a Multi-Agent Reinforcement Learning (MARL)-based dynamic bipartite weight update. The proposed Reinforcement Learning-Optimized Delay and Temperature Aware Scheduling (RL-ODTAS) algorithm deployed on a WBAN co-ordinator was tested on a custom-made simulation testbed with heterogeneous nodes that handle two categories of data. The simulation results indicate an average of 14.15% end-to-end delay improvement for emergency data. Also, at the end of 1500 superframes, a temperature rise reduction of up to 0.43 °C is seen compared to Hungarian Matching without a learning component. [ABSTRACT FROM AUTHOR] |
| Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 182844107 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning to allocate: a delay and temperature-aware slot allocation framework for WBAN with TDMA-MAC. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mystica%2C+K%2E+Jasmine%22">Mystica, K. Jasmine</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jasminemysticak@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Manickam%2C+J%2E+Martin+Leo%22">Manickam, J. Martin Leo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> josephmartin74@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wireless+Networks+%2810220038%29%22">Wireless Networks (10220038)</searchLink>. Jan2025, Vol. 31 Issue 1, p165-183. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Institute+of+Electrical+%26+Electronics+Engineers%22">Institute of Electrical & Electronics Engineers</searchLink><br /><searchLink fieldCode="DE" term="%22Time+division+multiple+access%22">Time division multiple access</searchLink><br /><searchLink fieldCode="DE" term="%22Body+area+networks%22">Body area networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication%22">Telecommunication</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bipartite+graphs%22">Bipartite graphs</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Data aggregation in the Wireless Body Area Networks (WBAN) is a multidimensional problem. It can be addressed at different levels of the network. The proposed work identifies the scheduling problem for the slots in a Time Division Multiple Access Medium Access Control (TDMA-MAC) superframe. A resource-constrained single channel WBAN is considered, and the proposed work models the set of nodes and slots of one subframe as a bipartite graph and aims to obtain a globally optimal matching solution for one subframe with end-to-end latency and temperature rise minimization as prime goals. Later, it extends the solution to the entire superframe, which consists of several subframes. The proposed Learning to Allocate (LTA) framework uses a Multi-Agent Reinforcement Learning (MARL)-based dynamic bipartite weight update. The proposed Reinforcement Learning-Optimized Delay and Temperature Aware Scheduling (RL-ODTAS) algorithm deployed on a WBAN co-ordinator was tested on a custom-made simulation testbed with heterogeneous nodes that handle two categories of data. The simulation results indicate an average of 14.15% end-to-end delay improvement for emergency data. Also, at the end of 1500 superframes, a temperature rise reduction of up to 0.43 °C is seen compared to Hungarian Matching without a learning component. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11276-024-03753-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 165 Subjects: – SubjectFull: Institute of Electrical & Electronics Engineers Type: general – SubjectFull: Time division multiple access Type: general – SubjectFull: Body area networks Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Telecommunication Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Bipartite graphs Type: general Titles: – TitleFull: Learning to allocate: a delay and temperature-aware slot allocation framework for WBAN with TDMA-MAC. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mystica, K. Jasmine – PersonEntity: Name: NameFull: Manickam, J. Martin Leo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10220038 Numbering: – Type: volume Value: 31 – Type: issue Value: 1 Titles: – TitleFull: Wireless Networks (10220038) Type: main |
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