Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform
The Internet of Medical Things (IoMT) paradigm provides pervasive healthcare services in-home monitoring networks. Nowadays, these services play an imperative part in the life of human beings. However, excessive requirements of health services result in insufficient spectrum resources and service de...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10098804/ |
| _version_ | 1797822206318739456 |
|---|---|
| author | Sungwook Kim |
| author_facet | Sungwook Kim |
| author_sort | Sungwook Kim |
| collection | DOAJ |
| description | The Internet of Medical Things (IoMT) paradigm provides pervasive healthcare services in-home monitoring networks. Nowadays, these services play an imperative part in the life of human beings. However, excessive requirements of health services result in insufficient spectrum resources and service delays. In this study, a novel spectrum allocation scheme is proposed for the IoMT system platform. The main challenge of our scheme is to effectively share the limited spectrum resource while dynamically handling different service requests. To achieve a mutually desirable solution for multiple IoMT devices, our proposed scheme is designed as a bi-level control algorithm using the ideas of multi-agent reinforcement learning (MARL) and the Balakrishnan-G <inline-formula> <tex-math notation="LaTeX">$\acute {o}$ </tex-math></inline-formula> mez-Vohra (BGV) solution. At the first level, each IoMT device selects its salient point according to the MARL model. At the second level, the spectrum resource is distributed through the BGV solution, which is implemented by considering the selected salient point of each device. Through the sequential interactions of intelligent devices, our bi-level control approach can effectively guide individual IoMT devices to choose cooperation strategies while optimizing the spectrum allocation process. Finally, numerical results show the effectiveness of our proposed scheme through the comparisons with benchmark protocols. We demonstrate the performance improvement of our method in terms of the normalized device payoff, IoMT system throughput and device fairness. |
| first_indexed | 2024-03-13T10:04:31Z |
| format | Article |
| id | doaj.art-98900231caae409db44a3e27cba71928 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T10:04:31Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-98900231caae409db44a3e27cba719282023-05-22T23:00:28ZengIEEEIEEE Access2169-35362023-01-0111480594806810.1109/ACCESS.2023.326633110098804Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) PlatformSungwook Kim0https://orcid.org/0000-0003-1967-151XDepartment of Computer Science, Sogang University, Seoul, Mapo-gu, South KoreaThe Internet of Medical Things (IoMT) paradigm provides pervasive healthcare services in-home monitoring networks. Nowadays, these services play an imperative part in the life of human beings. However, excessive requirements of health services result in insufficient spectrum resources and service delays. In this study, a novel spectrum allocation scheme is proposed for the IoMT system platform. The main challenge of our scheme is to effectively share the limited spectrum resource while dynamically handling different service requests. To achieve a mutually desirable solution for multiple IoMT devices, our proposed scheme is designed as a bi-level control algorithm using the ideas of multi-agent reinforcement learning (MARL) and the Balakrishnan-G <inline-formula> <tex-math notation="LaTeX">$\acute {o}$ </tex-math></inline-formula> mez-Vohra (BGV) solution. At the first level, each IoMT device selects its salient point according to the MARL model. At the second level, the spectrum resource is distributed through the BGV solution, which is implemented by considering the selected salient point of each device. Through the sequential interactions of intelligent devices, our bi-level control approach can effectively guide individual IoMT devices to choose cooperation strategies while optimizing the spectrum allocation process. Finally, numerical results show the effectiveness of our proposed scheme through the comparisons with benchmark protocols. We demonstrate the performance improvement of our method in terms of the normalized device payoff, IoMT system throughput and device fairness.https://ieeexplore.ieee.org/document/10098804/Internet of Medical Thingsmulti-agent reinforcement learningBalakrishnan-Gómez-Vohra solutionbi-level spectrum allocationcooperative game theory |
| spellingShingle | Sungwook Kim Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform IEEE Access Internet of Medical Things multi-agent reinforcement learning Balakrishnan-Gómez-Vohra solution bi-level spectrum allocation cooperative game theory |
| title | Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform |
| title_full | Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform |
| title_fullStr | Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform |
| title_full_unstemmed | Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform |
| title_short | Learning and Game Based Spectrum Allocation Model for Internet of Medical Things (IoMT) Platform |
| title_sort | learning and game based spectrum allocation model for internet of medical things iomt platform |
| topic | Internet of Medical Things multi-agent reinforcement learning Balakrishnan-Gómez-Vohra solution bi-level spectrum allocation cooperative game theory |
| url | https://ieeexplore.ieee.org/document/10098804/ |
| work_keys_str_mv | AT sungwookkim learningandgamebasedspectrumallocationmodelforinternetofmedicalthingsiomtplatform |