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Updated: Sep 22, 2025

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Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network

Galang P N Hakim1,2, Mohamed Hadi Habaebi2, Siti Fauziah Toha3

  • 1Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana, Jakarta 11650, Indonesia.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary

This study introduces a new Fuzzy Adaptive Neuro-Fuzzy Inference System (ANFIS) pathloss model for wireless sensor networks in tropical environments. The Fuzzy ANFIS model significantly outperforms existing models for near-ground propagation, improving communication reliability.

Keywords:
LoRaRSSIforestfuzzy ANFISjunglenear groundopen dirt roadpathloss propagation modelwireless sensor network

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Area of Science:

  • Wireless communication systems
  • Electromagnetic wave propagation
  • Environmental effects on radio signals

Background:

  • Wireless Sensor Networks (WSNs) in remote tropical areas face significant communication challenges due to environmental factors.
  • Existing propagation models often fail to accurately predict signal behavior in near-ground conditions with low antenna heights.

Purpose of the Study:

  • To analyze the performance of the LoRa (Long Range) pathloss propagation model for near-ground signals in tropical environments.
  • To develop and validate an improved pathloss model for low-height antenna scenarios in WSNs.

Main Methods:

  • Development and analysis of a Fuzzy Adaptive Neuro-Fuzzy Inference System (ANFIS) pathloss model.
  • Comparison of the developed model against benchmark models like Optimized FITU-R Near Ground, Okumura-Hata, and ITU-R Free Space.
  • Validation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) statistical metrics at 433 MHz and 868 MHz.

Main Results:

  • The Fuzzy ANFIS model achieved the lowest RMSE (0.88) and MAE (1.61) at 433 MHz in open dirt road environments.
  • Benchmark models showed higher RMSE and MAE values across different environments (forest, jungle, open dirt road) and frequencies.
  • The proposed Fuzzy ANFIS model demonstrated superior performance in near-ground propagation compared to all other evaluated models.

Conclusions:

  • The Fuzzy ANFIS pathloss model provides a more accurate prediction for wireless communication links in near-ground tropical environments.
  • This improved model is crucial for enhancing the reliability and performance of Wireless Sensor Networks in challenging terrains.
  • The findings support the use of advanced intelligent models for optimizing WSN communication link design.