A cache-aware Congestion Control Mechanism Using Deep Reinforcement Learning for Wireless Sensor Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00378795" target="_blank" >RIV/68407700:21230/25:00378795 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.adhoc.2024.103678" target="_blank" >https://doi.org/10.1016/j.adhoc.2024.103678</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.adhoc.2024.103678" target="_blank" >10.1016/j.adhoc.2024.103678</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A cache-aware Congestion Control Mechanism Using Deep Reinforcement Learning for Wireless Sensor Networks

Popis výsledku v původním jazyce

In Wireless Sensor Networks (WSN) communication protocols, rule-based approaches have been traditionally used for managing caching and congestion control. These approaches rely on explicitly defined, unchanging models. Recently, a trend has been toward incorporating adaptive methods that leverage machine learning (ML), including its subset deep learning (DL), during network congestion conditions. However, an adaptive cache-aware congestion control mechanism using Deep Reinforcement Learning (DRL) in WSN has not yet been explored. Therefore, this study developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. The DRL-CaCC uses intermediate caching parameters as its state space and adaptively moves the congestion window as its action space through the Rapid Start and DRL algorithms. The mechanism aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain between 20% and 40% compared to its baseline protocol, RT-CaCC. Finally, DRL-CaCC outperformed other caching-based and DRL-based congestion control protocols in terms of cache utilization, throughput, end-to-end delay, and packet loss metrics, with improvement gains between 10% and 30% in various congestion scenarios in WSN.

Název v anglickém jazyce

A cache-aware Congestion Control Mechanism Using Deep Reinforcement Learning for Wireless Sensor Networks

Popis výsledku anglicky

In Wireless Sensor Networks (WSN) communication protocols, rule-based approaches have been traditionally used for managing caching and congestion control. These approaches rely on explicitly defined, unchanging models. Recently, a trend has been toward incorporating adaptive methods that leverage machine learning (ML), including its subset deep learning (DL), during network congestion conditions. However, an adaptive cache-aware congestion control mechanism using Deep Reinforcement Learning (DRL) in WSN has not yet been explored. Therefore, this study developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. The DRL-CaCC uses intermediate caching parameters as its state space and adaptively moves the congestion window as its action space through the Rapid Start and DRL algorithms. The mechanism aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain between 20% and 40% compared to its baseline protocol, RT-CaCC. Finally, DRL-CaCC outperformed other caching-based and DRL-based congestion control protocols in terms of cache utilization, throughput, end-to-end delay, and packet loss metrics, with improvement gains between 10% and 30% in various congestion scenarios in WSN.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Ad Hoc Networks

ISSN

1570-8705

e-ISSN

1570-8713

Svazek periodika

166

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

001333567700001

EID výsledku v databázi Scopus

2-s2.0-85205498991