Residual-Based Analysis of the Mismatch Between LoRa Channel Models and Field Measurements in Urban and Rural Environments

Muhamad Bagus Fikril Alan; Pradini Puspitaningayu; Nurhayati Nurhayati; Muhammad 'Aamir Nashrullah; Nobuo Funabiki; Erwin Sutanto; Fahmi Fahmi

doi:10.24036/eksakta/vol27-iss03/683

EKSAKTA: Berkala Ilmiah Bidang MIPA

Open Access

Residual-Based Analysis of the Mismatch Between LoRa Channel Models and Field Measurements in Urban and Rural Environments

Muhamad Bagus Fikril Alan ¹

, Pradini Puspitaningayu ¹

, Nurhayati Nurhayati ¹

, Muhammad 'Aamir Nashrullah ¹

, Nobuo Funabiki ²

, Erwin Sutanto ³

, Fahmi Fahmi ⁴

¹ Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya, Surabaya, Indonesia

² Department of Electrical and Communication Engineering, Faculty of Engineering, Okayama University, Okayama, Japan

³ Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia

⁴ Department of Electrical Engineering, Faculty of Engineering, Universitas Sumatera Utara, Medan, Indonesia

Published 2026-06-30

Vol. 27 No. 03 (2026) PAGES 298-315

DOI 10.24036/eksakta/vol27-iss03/683

Views / Downloads 38 43

Download PDF

Abstract

This study investigates discrepancies between classical propagation models (Okumura-Hata, CI, FI) and LoRa field measurements in urban and rural environments. While rural areas exhibited linear path loss trends, urban scenarios showed distinct signal saturation, resulting in significant residuals even after model optimization. To address this, a residual-based analysis using Machine Learning (Linear Regression, Decision Tree, Random Forest) was proposed to map these systematic errors. The evaluation reveals that Random Forest (RF) significantly outperforms other algorithms, achieving an of 0.961 and an RMSE of 3.291 dB. These findings demonstrate that model mismatches follow deterministic patterns driven by environmental features rather than random noise. The study concludes that integrating ML-based residual compensation is essential for accurate radio planning in heterogeneous network deployments.

References

1

[1] Sadeghi-Niaraki, A. (2023). Internet of Thing (IoT) review of review: Bibliometric overview since its foundation. Future Generation Computer Systems, 143, 361-377.

2

[2] J. Linder, (2026). Iot Industry Statistics, Gitnux. [Online]. Available: https://gitnux.org/iot-industry-statistics/

3

[3] Duguma, A. L., & Bai, X. (2024). How the internet of things technology improves agricultural efficiency. Artificial Intelligence Review, 58(2), 63.

4

[4] Sutanto, E., Fahmi, F., Nurdin, Y., Puspitaningayu, P., & Aziz, M. (2024). Microgrid monitoring for patient safety in hospital using distributed residual current. IEEE Internet of Things Journal, 11(18), 29979-29992.

5

[5] Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., & Sikdar, B. (2019). A survey on IoT security: application areas, security threats, and solution architectures. IEEE access, 7, 82721-82743.

6

[6] Manzano, L. G., Boukabache, H., Danzeca, S., Heracleous, N., Murtas, F., Perrin, D., ... & Silari, M. (2021). An IoT LoRaWAN network for environmental radiation monitoring. IEEE Transactions on Instrumentation and Measurement, 70, 1-12.

7

[7] Alobaidy, H. A., Abdullah, N. F., Nordin, R., Behjati, M., Abu-Samah, A., Maizan, H., & Mandeep, J. S. (2024). Empowering extreme communication: propagation characterization of a LoRa-based internet of things network using hybrid machine learning. IEEE Open Journal of the Communications Society, 5, 3997-4023.

8

[8] Lloret, J., Garcia, L., Jimenez, J. M., Sendra, S., & Lorenz, P. (2021). Cluster-based communication protocol and architecture for a wastewater purification system intended for irrigation. IEEE Access, 9, 142374-142389.

9

[9] Suji Prasad, S. J., Thangatamilan, M., Suresh, M., Panchal, H., Rajan, C. A., Sagana, C., ... & Sadasivuni, K. K. (2022). An efficient LoRa-based smart agriculture management and monitoring system using wireless sensor networks. International Journal of Ambient Energy, 43(1), 5447-5450.

10

[10] Dallalbashi, Z. E., Alhayalir, S., Mnati, M. J., & Alhayali, A. A. A. (2023). Low-cost battery monitoring circuit for a photovoltaic system based on LoRa/LoRaWAN network. Indonesian Journal of Electrical Engineering and Computer Science, 29(2), 669-677.

11

[11] Ahmad, K. A., Segaran, J. D., Hashim, F. R., & Jusoh, M. T. (2017). Lora propagation at 433 MHz in tropical climate environment. Journal of Fundamental and Applied Sciences, 9(3S), 384-394.

12

[12] Urabe, I., Li, A., Fujisawa, M., Kim, S. J., & Hasegawa, M. (2023). Combinatorial mab-based joint channel and spreading factor selection for lora devices. Sensors, 23(15), 6687.

13

[13] Hudiono, H., Taufik, M., Perdana, R. H. Y., & Rakhmania, A. E. (2021). Digital centralized water meter using 433 MHz LoRa. Bulletin of Electrical Engineering and Informatics, 10(4), 2062-2071.

14

[14] Anzum, R. (2021). Factors that affect LoRa propagation in foliage medium. Procedia Computer Science, 194, 149-155.

15

[15] Paredes, M., Bertoldo, S., Carosso, L., Lucianaz, C., Marchetta, E., Allegretti, M., & Savi, P. (2019). Propagation measurements for a LoRa network in an urban environment. Journal of Electromagnetic Waves and Applications, 33(15), 2022-2036.

16

[16] Săcăleanu, D. I., Manciu, I. P., & Perişoară, L. A. (2019, June). Performance analysis of LoRa technology in wireless sensor networks. In 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1-5). IEEE.

17

[17] Anisah, I., & Yuliana, M. (2023, December). Experimental results of LoRa network radio propagation modeling in campus area. In 2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 427-432). IEEE.

18

[18] El Chall, R., Lahoud, S., & El Helou, M. (2019). LoRaWAN network: Radio propagation models and performance evaluation in various environments in Lebanon. IEEE Internet of Things Journal, 6(2), 2366-2378.

19

[19] Azevedo, J. A., & Mendonça, F. (2024). A critical review of the propagation models employed in LoRa systems. Sensors, 24(12), 3877.

20

[20] Lima, W. G., Lopes, A. V., Cardoso, C. M., Araújo, J. P., Neto, M. C., Tostes, M. E., ... & Barros, F. J. (2024). LoRa technology propagation models for IoT network planning in the Amazon regions. Sensors, 24(5), 1621.

21

[21] Mahjoub, T., Mnaouer, A. B., Said, M. B., & Boujemaa, H. (2024). LoRa signal propagation and path loss prediction in Tunisian date palm oases. Computers and Electronics in Agriculture, 222, 109027.

22

[22] Vasudevan, M., & Yuksel, M. (2024). Machine learning for radio propagation modeling: A comprehensive survey. IEEE Open Journal of the Communications Society, 5, 5123-5153.

23

[23] Masadan, N. A. B., Habaebi, M. H., & Yusoff, S. H. (2018, September). LoRa LPWAN propagation channel modelling in IIUM campus. In 2018 7th International Conference on Computer and communication Engineering (ICCCE) (pp. 14-19). IEEE.

24

[24] Alan, M. B. F., Puspitaningayu, P., Sutanto, E., Fahmi, F., & Widayaka, P. D. (2024, January). Design and implementation of photovoltaic monitoring system using Arduino UNO R4. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 899-903). IEEE.

25

[25] Puspitaningayu, P., Alan, M. B. F., Sutanto, E., Fahmi, F., & Kartini, U. T. (2024, January). An Evaluation of LoRaWAN and WLAN for IoT-based Photovoltaic Microgrid Monitoring. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 1255-1259). IEEE.

26

[26] Díaz Zayas, A., Rivas Tocado, F. J., & Rodríguez, P. (2020). Evolution and testing of NB-IoT solutions. Applied Sciences, 10(21), 7903.

27

[27] Marini, R., Mikhaylov, K., Pasolini, G., & Buratti, C. (2022). Low-power wide-area networks: Comparison of LoRaWAN and NB-IoT performance. IEEE Internet of Things Journal, 9(21), 21051-21063.

28

[28] Al-Shareeda, M. A., Alsadhan, A. A., Qasim, H. H., & Manickam, S. (2023). Long range technology for internet of things: review, challenges, and future directions. Bulletin of Electrical Engineering and Informatics, 12(6), 3758-3767.

29

[29] Budi, B., & Machdi, A. R. (2024). Distance Testing on Point to Point Communication with Lora Based on RSSI and Log Normal Shadowing Model. Journal Of Energy And Electrical Engineering, 5(2).

30

[30] de Camargo, E. T., Spanhol, F. A., & Castro e Souza, Á. R. (2021). Deployment of a LoRaWAN network and evaluation of tracking devices in the context of smart cities. Journal of Internet Services and Applications, 12(1), 8.

31

[31] Zhang, H., Meng, F., Xu, J., Liu, Z., & Meng, J. (2022). Evaluation of machine learning models for daily reference evapotranspiration modeling using limited meteorological data in Eastern Inner Mongolia, North China. Water, 14(18), 2890.

32

[32] Mahjoub, T., Mnaouer, A. B., Said, M. B., & Boujemaa, H. (2024). LoRa signal propagation and path loss prediction in Tunisian date palm oases. Computers and Electronics in Agriculture, 222, 109027.

33

[33] Iliev, I., Velchev, Y., Petkov, P. Z., Bonev, B., Iliev, G., & Nachev, I. (2024). A machine learning approach for path loss prediction using combination of regression and classification models. Sensors, 24(17), 5855.

34

[34] Xu, T., Ma, D., Fang, W., & Huang, Y. (2025). Experimental study on the propagation characteristics of LoRa signals in maize fields. Electronics, 14(11), 2156.

Residual-Based Analysis of the Mismatch Between LoRa Channel Models and Field Measurements in Urban and Rural Environments

Abstract

References

How to Cite

Most read articles by the same author(s)

Residual-Based Analysis of the Mismatch Between LoRa Channel Models and Field Measurements in Urban and Rural Environments

Abstract

References

How to Cite

Cite Article

Article Update

Most read articles by the same author(s)