A Data-Gathering Underwater Medium Access Control Scheme Using Carrier Sensing Associated Machine Learning

Jong Won Lee, Shin Young Park, Eun Ju Do, Ho Shin Cho

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a medium access control method in underwater sensor networks aimed at gathering data from multiple sensor nodes to a sink node. Considering the long propagation delay of underwater acoustic channels, exchanging control packets between nodes is inefficient. In our work, sensor nodes are trained through a machine learning and determine the correct timing for data transmission to avoid possible collisions without exchanging control packets. To learn varying channel conditions, sensor nodes employ a carrier sensing. Simulation results reveal that among machine learning models, the proposed scheme utilizing the MLP model exhibits the most outstanding performance.

Original languageEnglish
Title of host publicationICUFN 2024 - 15th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages570-572
Number of pages3
ISBN (Electronic)9798350385298
DOIs
StatePublished - 2024
Event15th International Conference on Ubiquitous and Future Networks, ICUFN 2024 - Hybrid, Hungary, Hungary
Duration: 2 Jul 20245 Jul 2024

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Conference

Conference15th International Conference on Ubiquitous and Future Networks, ICUFN 2024
Country/TerritoryHungary
CityHybrid, Hungary
Period2/07/245/07/24

Keywords

  • Carrier Sensing
  • Machine Learning
  • Medium Access Control
  • Underwater Sensor Networks (UWSNs)

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