Skip to main navigation Skip to search Skip to main content

Wireless Underground Sensor Network Path Loss Models for Durian Tree

  • Mahidol University
  • Kasem Bundit University

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

2 Citations (Scopus)

Abstract

In order to maintain plant growth and protect brackish water of durian tree, wireless underground sensor networks (WUSNs) are applied. In this study, path loss models were formulated based on the curve-fitting for LoRa WSN at the banana garden in Nakornchaisri district of Thailand. Additionally, there were deterministic large-scale models based on a measurement campaign at a frequency of 433 MHz. The path loss between the transmitter node and the receiver node was modeled and derived based on the received signal strength indicator (RSSI) measurements. We investigated how soil contents affected the path loss models especially sandy loam for durian planting. The results show that the proposed path loss model with topping attenuation factor (TAF) outperforms especially for underground-to-underground communication (UG2UG) comparing with conventional models.

Original languageEnglish
Title of host publication2021 Photonics and Electromagnetics Research Symposium, PIERS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages284-288
Number of pages5
ISBN (Electronic)9781728172477
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 Photonics and Electromagnetics Research Symposium, PIERS 2021 - Hangzhou, China
Duration: 21 Nov 202125 Nov 2021

Publication series

NameProgress in Electromagnetics Research Symposium
Volume2021-November
ISSN (Print)1559-9450
ISSN (Electronic)1931-7360

Conference

Conference2021 Photonics and Electromagnetics Research Symposium, PIERS 2021
Country/TerritoryChina
CityHangzhou
Period21/11/2125/11/21

Fingerprint

Dive into the research topics of 'Wireless Underground Sensor Network Path Loss Models for Durian Tree'. Together they form a unique fingerprint.

Cite this