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Machine Learning-Based Performance-Efficient MAC Protocol for Single Hop Underwater Acoustic Sensor Networks

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Abstract

Advances in the development of underwater full duplex sensors have meant they can now provide solutions to underwater acoustic sensor networks (UASN) that suffer from narrow bandwidth and long end-to-end transmission delay. In this paper, a full machine learning-based duplex medium access control (MAC) protocol for single hop underwater acoustic sensor networks is proposed to enhance the performance on throughput, delay and fairness access. The proposed protocol is designed in different access schemes for uplink and downlink transmission according to their main uses, respectively. The access scheme of the downlink is contention free to ensure that command information can be delivered to sensor nodes with a low collision rate. A hybrid scheme is utilized in the uplink to maximize the network throughput to meet transmission requirements of monitoring data collected by sensor nodes. In this way, transmission resources can be used more efficiently and fairer. In addition, as a unique characteristic of underwater acoustic communication, propagation time in the transmission is taken into consideration in the design of the MAC protocol. Simulation results and analysis exhibited that the proposed protocol performs significantly better than state-of-the-art MAC protocols for UASN on network throughput, end-to-end delay and transmission fairness. Underwater acoustic sensor networks (UASN), machine learning medium access control (MAC) protocol, full duplex transmission, network throughput, and end-to-end delay.

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Data Availability

The data used to support the finding of this study are included in the article.

Abbreviations

UASN:

Underwater acoustic sensor networks

MAC:

Medium access control

UAV:

Unmanned Aerial vehicle

WSN:

Wireless Sensor Network

SIC:

Self-interference cancellation

AP :

Access Point

TDMA:

Time division multiple access

ALOHA:

Additive Links On-line Hawaii Area

CSMA:

Carrier sense multiple access

CSMA/CA:

Carrier sense multiple access with collision avoid

DTMAC:

Delay Tolerant MAC protocol

VI-ALOHA:

Variable Interval ALOHA

CDMA:

Code division multiple access

FDMA:

Frequency division multiple access

RISM:

Receiver initiated spectrum management

HCFMA:

Hybrid Collision-Free Medium Access

UPMAC:

Underwater Practical MAC

WT:

Wake up to transmit

DIFS:

Distributed inter-frame spacing

TD:

Transmission delay

DTP:

Data transmission period

SIFS:

Short inter-frame spacing

U-DTP:

The uplink data transmission period

D-DTP:

Downlink data transmission period

CP:

Contending period

CFP:

Contend free period

CW:

Contending window

DT:

Data transmission

NS:

Next Subset

FI :

Fairness index

SNR:

Signal noise ratio

PL:

Propagation loss

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Funding

This work is supported by the National Natural Science Foundation of China under Grant No.62001384, the Xi’an Key Laboratory of Intelligent Perception and Cultural Inheritance under Grant No. 2019219614SYS011CG033, and also supported by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant No. 2020JQ-605, It is also founded by the China Postdoctoral Science Foundation under Grant No.2020M683694XB. Fundamental Research Funds for the Provincial Universities of Zhejiang, Grant No. GK219909299001-024.

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710127, Shaanxi, China

    Wei Zhang, Jiayu Li, Yuhang Wan, Xu Yao & Maojun Li

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  1. Wei Zhang
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  2. Jiayu Li
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  3. Yuhang Wan
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  4. Xu Yao
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  5. Maojun Li
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Correspondence to Wei Zhang.

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Zhang, W., Li, J., Wan, Y. et al. Machine Learning-Based Performance-Efficient MAC Protocol for Single Hop Underwater Acoustic Sensor Networks. J Grid Computing 20, 41 (2022). https://doi.org/10.1007/s10723-022-09636-9

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