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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021
Editors	Mohd Fais Mansor, Nordin Ramli, Mahamod Ismail
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	248-253
Number of pages	6
ISBN (Electronic)	9781728172545
DOIs	https://doi.org/10.1109/APCC49754.2021.9609815
Publication status	Published - 19 Nov 2021
Externally published	Yes
Event	26th IEEE Asia-Pacific Conference on Communications, APCC 2021 - Virtual, Kuala Lumpur, Malaysia Duration: 11 Oct 2021 → 13 Oct 2021

Publication series

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

Conference

Conference	26th IEEE Asia-Pacific Conference on Communications, APCC 2021
Country/Territory	Malaysia
City	Virtual, Kuala Lumpur
Period	11/10/21 → 13/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

5G
Reinforcement learning
Routing and Network lifetime
Smart health-care

Access to Document

10.1109/APCC49754.2021.9609815

Cite this

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. In M. F. Mansor, N. Ramli, & M. Ismail (Eds.), Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021 (pp. 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. editor / Mohd Fais Mansor ; Nordin Ramli ; Mahamod Ismail. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 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",

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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. in MF Mansor, N Ramli & M Ismail (eds), 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., pp. 248-253, 26th IEEE Asia-Pacific Conference on Communications, APCC 2021, Virtual, Kuala Lumpur, Malaysia, 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. ed. / Mohd Fais Mansor; Nordin Ramli; Mahamod Ismail. Institute of Electrical and Electronics Engineers Inc., 2021. p. 248-253 (Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

UR - https://www.scopus.com/pages/publications/85123479214

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. In Mansor MF, Ramli N, Ismail M, editors, Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 248-253. (Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021). doi: 10.1109/APCC49754.2021.9609815

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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