Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression
Issued Date
2023-04-01
Resource Type
ISSN
14248220
Scopus ID
2-s2.0-85152314854
Pubmed ID
37050586
Journal Title
Sensors
Volume
23
Issue
7
Rights Holder(s)
SCOPUS
Bibliographic Citation
Sensors Vol.23 No.7 (2023)
Suggested Citation
Phaiboon S., Phokharatkul P. Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression. Sensors Vol.23 No.7 (2023). doi:10.3390/s23073525 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81574
Title
Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Path loss models are essential tools for estimating expected large-scale signal fading in a specific propagation environment during wireless sensor network (WSN) design and optimization. However, variations in the environment may result in prediction errors due to uncertainty caused by vegetation growth, random obstruction or climate change. This study explores the capability of multi-boundary fuzzy linear regression (MBFLR) to establish uncertainty relationships between related variables for path loss predictions of WSN in agricultural farming. Measurement campaigns along various routes in an agricultural area are conducted to obtain terrain profile data and path losses of radio signals transmitted at 433 MHz. Proposed models are fitted using measured data with “initial membership level” ((Formula presented.)). The boundaries are extended to cover the uncertainty of the received signal strength indicator (RSSI) and distance relationship. The uncertainty not captured in normal measurement datasets between transmitter and receiving nodes (e.g., tall grass, weed, and moving humans and/or animals) may cause low-quality signal or disconnectivity. The results show the possibility of RSSI data in MBFLR supported at an (Formula presented.) of 0.4 with root mean square error (RMSE) of 0.8, 1.2, and 2.6 for short grass, tall grass, and people motion, respectively. Breakpoint optimization helps provide prediction accuracy when uncertainty occurs. The proposed model determines the suitable coverage for acceptable signal quality in all environmental situations.