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RADIO PROPAGATION MEASUREMENT AND CHARACTERIZATION IN OUTDOOR TALL FOOD GRASS AGRICULTURE FIELD FOR WIRELESS SENSOR NETWORK AT 2.4 GHZ BAND
Author(s) -
Tossaporn Srisooksai,
Kamol Kaemarungsi,
Junichi Takada,
Kentaro Saito
Publication year - 2018
Publication title -
progress in electromagnetics research c
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 34
ISSN - 1937-8718
DOI - 10.2528/pierc18062903
Subject(s) - wireless sensor network , field (mathematics) , wireless , characterization (materials science) , engineering , environmental science , telecommunications , electrical engineering , acoustics , physics , computer science , computer network , optics , mathematics , pure mathematics
This paper describes the radio propagation measurement campaign in a sugarcane field representing a tall food grass characteristic which is one of the common types of outdoor agriculture environments. The measurement was conducted by using a channel sounder having a bandwidth of 45.6 MHz at 2.45 GHz with the aim of investigating the propagation channel characteristics which are useful in deploying wireless sensor networks in precision agriculture. By analogy with the Ikegami model, the variation of the path loss over the relative angles between the plant rows and the line-ofsight direction from the transmitter to the receiver is identified. Utilizing this knowledge, this work justifies the procedure of predicting the path loss at any point in the field by a few measurement efforts. Furthermore, the Rician K-factor and root-mean-square delay spread are investigated for vegetation depths less than 40 m. The result shows that the relationship between the Rician K-factor and its corresponding path loss value in each measurement point can be fitted with the log-linear line. This leads to the possibility of predicting the K-factor at any point in the field. In addition, because the result of the root-mean-square delay spread is independent of the vegetation depth and the density of the plant, it is represented by the statistical model in which the Weibull distribution provides the best representation.

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