IIUM Repository

Near ground pathloss propagation model using adaptive neuro fuzzy inference system for wireless sensor network communication in forest, jungle, and open dirt road environments

Hakim, Galang P. N. and Habaebi, Mohamed Hadi and Toha, Siti Fauziah and Islam, Md. Rafiqul and Yusoff, Siti Hajar and Adesta, Erry Yulian Triblas and Anzum, Rabeya (2022) Near ground pathloss propagation model using adaptive neuro fuzzy inference system for wireless sensor network communication in forest, jungle, and open dirt road environments. Sensors, 22 (9). pp. 1-18. E-ISSN 1424-8220

[img] PDF - Published Version
Restricted to Registered users only

Download (4MB) | Request a copy

Abstract

In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models.

Item Type: Article (Journal)
Uncontrolled Keywords: Fuzzy ANFIS; Wireless Sensor Network; near ground; LoRa; pathloss propagation model; RSSI; jungle; forest; open dirt road
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Dr. Mohamed Hadi Habaebi
Date Deposited: 25 Apr 2022 13:00
Last Modified: 28 Apr 2022 09:29
URI: http://irep.iium.edu.my/id/eprint/97712

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year