Power Control for MACA-based Underwater MAC Protocol: A Q-Learning Approach

Junho Cho; Faisal Ahmed; Ethungshan Shitiri; Ho Shin Cho

doi:10.1109/TENSYMP52854.2021.9550973

Power Control for MACA-based Underwater MAC Protocol: A Q-Learning Approach

Junho Cho
, Faisal Ahmed
, Ethungshan Shitiri
, Ho Shin Cho

School of Electronics Engineering

Hanwha

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

9 Scopus citations

Abstract

Underwater acoustic sensors are battery-powered and spend a major portion of their limited energy during packet transmissions. To conserve energy, multiple access collision avoidance (MACA)-based MAC protocols are designed to lower the data packet transmission power, while using the maximum transmission power for control packets. However, lowering the data transmission power make the data packets susceptible to collisions. In this regard, a reinforcement learning-based power control scheme is proposed for MACA-based underwater MAC protocol that can reduce collisions while maintaining high energy efficiency. A key feature of the proposed scheme is that it enables the sensor nodes to prevent collisions without any prior knowledge of the interferences, eliminating the need for additional signaling. Simulation results show that the proposed scheme significantly improves the energy efficiency and the throughput of MACA-based power control schemes.

Original language	English
Title of host publication	TENSYMP 2021 - 2021 IEEE Region 10 Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665400268
DOIs	https://doi.org/10.1109/TENSYMP52854.2021.9550973
State	Published - 23 Aug 2021
Event	2021 IEEE Region 10 Symposium, TENSYMP 2021 - Jeju, Korea, Republic of Duration: 23 Aug 2021 → 25 Aug 2021

Publication series

Name	TENSYMP 2021 - 2021 IEEE Region 10 Symposium

Conference

Conference	2021 IEEE Region 10 Symposium, TENSYMP 2021
Country/Territory	Korea, Republic of
City	Jeju
Period	23/08/21 → 25/08/21

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

collisions
interference
medium access control
power control
Q-learning
underwater acoustic sensor networks

Access to Document

10.1109/TENSYMP52854.2021.9550973

Cite this

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title = "Power Control for MACA-based Underwater MAC Protocol: A Q-Learning Approach",

abstract = "Underwater acoustic sensors are battery-powered and spend a major portion of their limited energy during packet transmissions. To conserve energy, multiple access collision avoidance (MACA)-based MAC protocols are designed to lower the data packet transmission power, while using the maximum transmission power for control packets. However, lowering the data transmission power make the data packets susceptible to collisions. In this regard, a reinforcement learning-based power control scheme is proposed for MACA-based underwater MAC protocol that can reduce collisions while maintaining high energy efficiency. A key feature of the proposed scheme is that it enables the sensor nodes to prevent collisions without any prior knowledge of the interferences, eliminating the need for additional signaling. Simulation results show that the proposed scheme significantly improves the energy efficiency and the throughput of MACA-based power control schemes.",

keywords = "collisions, interference, medium access control, power control, Q-learning, underwater acoustic sensor networks",

author = "Junho Cho and Faisal Ahmed and Ethungshan Shitiri and Cho, \{Ho Shin\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Region 10 Symposium, TENSYMP 2021 ; Conference date: 23-08-2021 Through 25-08-2021",

year = "2021",

month = aug,

day = "23",

doi = "10.1109/TENSYMP52854.2021.9550973",

language = "English",

series = "TENSYMP 2021 - 2021 IEEE Region 10 Symposium",

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

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address = "United States",

}

Cho, J, Ahmed, F, Shitiri, E & Cho, HS 2021, Power Control for MACA-based Underwater MAC Protocol: A Q-Learning Approach. in TENSYMP 2021 - 2021 IEEE Region 10 Symposium. TENSYMP 2021 - 2021 IEEE Region 10 Symposium, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Region 10 Symposium, TENSYMP 2021, Jeju, Korea, Republic of, 23/08/21. https://doi.org/10.1109/TENSYMP52854.2021.9550973

Power Control for MACA-based Underwater MAC Protocol: A Q-Learning Approach. / Cho, Junho; Ahmed, Faisal; Shitiri, Ethungshan et al.
TENSYMP 2021 - 2021 IEEE Region 10 Symposium. Institute of Electrical and Electronics Engineers Inc., 2021. (TENSYMP 2021 - 2021 IEEE Region 10 Symposium).

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

TY - GEN

T1 - Power Control for MACA-based Underwater MAC Protocol

T2 - 2021 IEEE Region 10 Symposium, TENSYMP 2021

AU - Cho, Junho

AU - Ahmed, Faisal

AU - Shitiri, Ethungshan

AU - Cho, Ho Shin

PY - 2021/8/23

Y1 - 2021/8/23

N2 - Underwater acoustic sensors are battery-powered and spend a major portion of their limited energy during packet transmissions. To conserve energy, multiple access collision avoidance (MACA)-based MAC protocols are designed to lower the data packet transmission power, while using the maximum transmission power for control packets. However, lowering the data transmission power make the data packets susceptible to collisions. In this regard, a reinforcement learning-based power control scheme is proposed for MACA-based underwater MAC protocol that can reduce collisions while maintaining high energy efficiency. A key feature of the proposed scheme is that it enables the sensor nodes to prevent collisions without any prior knowledge of the interferences, eliminating the need for additional signaling. Simulation results show that the proposed scheme significantly improves the energy efficiency and the throughput of MACA-based power control schemes.

AB - Underwater acoustic sensors are battery-powered and spend a major portion of their limited energy during packet transmissions. To conserve energy, multiple access collision avoidance (MACA)-based MAC protocols are designed to lower the data packet transmission power, while using the maximum transmission power for control packets. However, lowering the data transmission power make the data packets susceptible to collisions. In this regard, a reinforcement learning-based power control scheme is proposed for MACA-based underwater MAC protocol that can reduce collisions while maintaining high energy efficiency. A key feature of the proposed scheme is that it enables the sensor nodes to prevent collisions without any prior knowledge of the interferences, eliminating the need for additional signaling. Simulation results show that the proposed scheme significantly improves the energy efficiency and the throughput of MACA-based power control schemes.

KW - collisions

KW - interference

KW - medium access control

KW - power control

KW - Q-learning

KW - underwater acoustic sensor networks

UR - https://www.scopus.com/pages/publications/85117521511

U2 - 10.1109/TENSYMP52854.2021.9550973

DO - 10.1109/TENSYMP52854.2021.9550973

M3 - Conference contribution

AN - SCOPUS:85117521511

T3 - TENSYMP 2021 - 2021 IEEE Region 10 Symposium

BT - TENSYMP 2021 - 2021 IEEE Region 10 Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 23 August 2021 through 25 August 2021

ER -

Power Control for MACA-based Underwater MAC Protocol: A Q-Learning Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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