Machine-Learning-Based Optimal Cooperating Node Selection for Internet of Underwater Things

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00378214" target="_blank" >RIV/68407700:21230/24:00378214 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/JIOT.2024.3381834" target="_blank" >https://doi.org/10.1109/JIOT.2024.3381834</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JIOT.2024.3381834" target="_blank" >10.1109/JIOT.2024.3381834</a>

Result language

angličtina

Original language name

Machine-Learning-Based Optimal Cooperating Node Selection for Internet of Underwater Things

Original language description

Multihop communication has gained prominence within the realm of the Internet of Underwater Things (IoUT) owing to its exceptional reliability amidst the challenges posed by the underwater acoustic environment. Despite this, the persistence of limitations caused by propagation delay, high collision rate, and limited energy in underwater communication remains, representing the most formidable hurdles in ensuring the successful transmission of data gathered by sensor nodes. To address these challenges, we employ a machine learning (ML)-based optimal cooperating node selection for each hop, considering the Shortest propagation delay, minimal residual Energy, and a low Collision rate (referred to as SEC). For this purpose, we initially assemble the sensor nodes to create a list of cooperative nodes, considering the aspect of SEC. Then, using an assembled list of cooperating sensor nodes, we employ ML-based algorithms, such as reinforcement learning (RL-SEC), deep Q-networks (DQN-SEC), and deep deterministic policy gradient (DDPG-SEC), to predict the optimal cooperating node for each hop. The simulation results of the DDPG-SEC demonstrate a significant improvement of approximately 56% when compared with RL-SEC, DQN-SEC, and other state-of-the-art techniques.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20203 - Telecommunications

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

2327-4662

Volume of the periodical

11

Issue of the periodical within the volume

12

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

22471-22482

UT code for WoS article

001242362600125

EID of the result in the Scopus database

2-s2.0-85189177113