Optimal Route Selection in 5G-based Smart Health-care Network: A Reinforcement Learning Approach

Abdul Ahad; Mohammad Tahir; Muhammad Aman Sheikh Sheikh; Amna Mughees; Kazi Istiaque Ahmed

doi:10.1109/APCC49754.2021.9609815

Optimal Route Selection in 5G-based Smart Health-care Network: A Reinforcement Learning Approach

Abdul Ahad^*, Mohammad Tahir, Muhammad Aman Sheikh Sheikh, Amna Mughees, Kazi Istiaque Ahmed

^*Awdur cyfatebol y gwaith hwn

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8 Dyfyniadau (Scopus)

Crynodeb

Smart health-care is the most promising application of the next-generation 5G wireless network. Because of low latency and high data rate, many applications with high resources are supporting 5G, including smart health-care application. In smart health-care, medical sensors exchange data to establish a network. However, the mobility of nodes and density changes the network topology usually. Medical sensor nodes have limited energy, which is used for transmission and receiving of data. In this paper, an idea of selection of route is distinguished by taking into account of stability and higher residual energy in 5G-based smart health-care network to decrease energy consumption along with links disconnection and improve network lifetime. For this purpose, we present reinforcement learning-based algorithm and investigate the effect of various learning rates on energy consumption, links disconnection and network lifetime in smart health-care network.

Iaith wreiddiol	Saesneg
Teitl	Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021
Golygyddion	Mohd Fais Mansor, Nordin Ramli, Mahamod Ismail
Cyhoeddwr	Institute of Electrical and Electronics Engineers Inc.
Tudalennau	248-253
Nifer y tudalennau	6
ISBN (Electronig)	9781728172545
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/APCC49754.2021.9609815
Statws	Cyhoeddwyd - 19 Tach 2021
Cyhoeddwyd yn allanol	Ie
Digwyddiad	26th IEEE Asia-Pacific Conference on Communications, APCC 2021 - Virtual, Kuala Lumpur, Malaisia Hyd: 11 Hyd 2021 → 13 Hyd 2021

Cyfres gyhoeddiadau

Enw	Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021

Cynhadledd

Cynhadledd	26th IEEE Asia-Pacific Conference on Communications, APCC 2021
Gwlad/Tiriogaeth	Malaisia
Dinas	Virtual, Kuala Lumpur
Cyfnod	11/10/21 → 13/10/21

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

Mynediad at Ddogfen

10.1109/APCC49754.2021.9609815

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

Ahad, A., Tahir, M., Sheikh, M. A. S., Mughees, A., & Ahmed, K. I. (2021). Optimal Route Selection in 5G-based Smart Health-care Network: A Reinforcement Learning Approach. Yn M. F. Mansor, N. Ramli, & M. Ismail (Gol.), Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021 (tt. 248-253). (Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCC49754.2021.9609815

Ahad, Abdul ; Tahir, Mohammad ; Sheikh, Muhammad Aman Sheikh et al. / Optimal Route Selection in 5G-based Smart Health-care Network : A Reinforcement Learning Approach. Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021. Gol. / Mohd Fais Mansor ; Nordin Ramli ; Mahamod Ismail. Institute of Electrical and Electronics Engineers Inc., 2021. tt. 248-253 (Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021).

@inproceedings{0233ccb5cefc442899db94d0124aab5f,

title = "Optimal Route Selection in 5G-based Smart Health-care Network: A Reinforcement Learning Approach",

abstract = "Smart health-care is the most promising application of the next-generation 5G wireless network. Because of low latency and high data rate, many applications with high resources are supporting 5G, including smart health-care application. In smart health-care, medical sensors exchange data to establish a network. However, the mobility of nodes and density changes the network topology usually. Medical sensor nodes have limited energy, which is used for transmission and receiving of data. In this paper, an idea of selection of route is distinguished by taking into account of stability and higher residual energy in 5G-based smart health-care network to decrease energy consumption along with links disconnection and improve network lifetime. For this purpose, we present reinforcement learning-based algorithm and investigate the effect of various learning rates on energy consumption, links disconnection and network lifetime in smart health-care network.",

keywords = "5G, Reinforcement learning, Routing and Network lifetime, Smart health-care",

author = "Abdul Ahad and Mohammad Tahir and Sheikh, {Muhammad Aman Sheikh} and Amna Mughees and Ahmed, {Kazi Istiaque}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 26th IEEE Asia-Pacific Conference on Communications, APCC 2021 ; Conference date: 11-10-2021 Through 13-10-2021",

year = "2021",

month = nov,

day = "19",

doi = "10.1109/APCC49754.2021.9609815",

language = "English",

series = "Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "248--253",

editor = "Mansor, {Mohd Fais} and Nordin Ramli and Mahamod Ismail",

booktitle = "Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021",

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Ahad, A, Tahir, M, Sheikh, MAS, Mughees, A & Ahmed, KI 2021, Optimal Route Selection in 5G-based Smart Health-care Network: A Reinforcement Learning Approach. yn MF Mansor, N Ramli & M Ismail (gol.), Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021. Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021, Institute of Electrical and Electronics Engineers Inc., tt. 248-253, 26th IEEE Asia-Pacific Conference on Communications, APCC 2021, Virtual, Kuala Lumpur, Malaisia, 11/10/21. https://doi.org/10.1109/APCC49754.2021.9609815

Optimal Route Selection in 5G-based Smart Health-care Network: A Reinforcement Learning Approach. / Ahad, Abdul; Tahir, Mohammad; Sheikh, Muhammad Aman Sheikh et al.
Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021. gol. / Mohd Fais Mansor; Nordin Ramli; Mahamod Ismail. Institute of Electrical and Electronics Engineers Inc., 2021. t. 248-253 (Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

TY - GEN

T1 - Optimal Route Selection in 5G-based Smart Health-care Network

T2 - 26th IEEE Asia-Pacific Conference on Communications, APCC 2021

AU - Ahad, Abdul

AU - Tahir, Mohammad

AU - Sheikh, Muhammad Aman Sheikh

AU - Mughees, Amna

AU - Ahmed, Kazi Istiaque

PY - 2021/11/19

Y1 - 2021/11/19

N2 - Smart health-care is the most promising application of the next-generation 5G wireless network. Because of low latency and high data rate, many applications with high resources are supporting 5G, including smart health-care application. In smart health-care, medical sensors exchange data to establish a network. However, the mobility of nodes and density changes the network topology usually. Medical sensor nodes have limited energy, which is used for transmission and receiving of data. In this paper, an idea of selection of route is distinguished by taking into account of stability and higher residual energy in 5G-based smart health-care network to decrease energy consumption along with links disconnection and improve network lifetime. For this purpose, we present reinforcement learning-based algorithm and investigate the effect of various learning rates on energy consumption, links disconnection and network lifetime in smart health-care network.

AB - Smart health-care is the most promising application of the next-generation 5G wireless network. Because of low latency and high data rate, many applications with high resources are supporting 5G, including smart health-care application. In smart health-care, medical sensors exchange data to establish a network. However, the mobility of nodes and density changes the network topology usually. Medical sensor nodes have limited energy, which is used for transmission and receiving of data. In this paper, an idea of selection of route is distinguished by taking into account of stability and higher residual energy in 5G-based smart health-care network to decrease energy consumption along with links disconnection and improve network lifetime. For this purpose, we present reinforcement learning-based algorithm and investigate the effect of various learning rates on energy consumption, links disconnection and network lifetime in smart health-care network.

KW - 5G

KW - Reinforcement learning

KW - Routing and Network lifetime

KW - Smart health-care

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U2 - 10.1109/APCC49754.2021.9609815

DO - 10.1109/APCC49754.2021.9609815

M3 - Conference contribution

AN - SCOPUS:85123479214

T3 - Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021

SP - 248

EP - 253

BT - Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021

A2 - Mansor, Mohd Fais

A2 - Ramli, Nordin

A2 - Ismail, Mahamod

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 11 October 2021 through 13 October 2021

ER -

Ahad A, Tahir M, Sheikh MAS, Mughees A, Ahmed KI. Optimal Route Selection in 5G-based Smart Health-care Network: A Reinforcement Learning Approach. Yn Mansor MF, Ramli N, Ismail M, golygyddion, Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. t. 248-253. (Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021). doi: 10.1109/APCC49754.2021.9609815