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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

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

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Ad Hoc Networks

ISSN

1570-8705

e-ISSN

1570-8713

Volume of the periodical

166

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

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UT code for WoS article

001333567700001

EID of the result in the Scopus database

2-s2.0-85205498991