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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

2327-4662

Svazek periodika

11

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

22471-22482

Kód UT WoS článku

001242362600125

EID výsledku v databázi Scopus

2-s2.0-85189177113