Comparison of machine learning path loss model for wireless sensor networks in cassava crops
Wireless sensor networks play an essential role in modern agriculture, as they facilitate the monitoring of different variables that have an impact on crop yields. The successful operation of WSNs is highly dependent on their accurate deployment in the field, which requires proper modeling of radio...
- Autores:
-
Barrios-Ulloa, Alexis
De-La-Hoz-Franco, Emiro
Cama-Pinto, Alejandro
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13112
- Acceso en línea:
- https://hdl.handle.net/11323/13112
https://repositorio.cuc.edu.co/
- Palabra clave:
- Decision tree
K-Nearest-Neighbors
Machine learning
Path loss model
Radio wave propagation
Random Forest (RF)
- Rights
- embargoedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| spellingShingle |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops Decision tree K-Nearest-Neighbors Machine learning Path loss model Radio wave propagation Random Forest (RF) |
| title_short |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_full |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_fullStr |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_full_unstemmed |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| title_sort |
Comparison of machine learning path loss model for wireless sensor networks in cassava crops |
| dc.creator.fl_str_mv |
Barrios-Ulloa, Alexis De-La-Hoz-Franco, Emiro Cama-Pinto, Alejandro |
| dc.contributor.author.none.fl_str_mv |
Barrios-Ulloa, Alexis De-La-Hoz-Franco, Emiro Cama-Pinto, Alejandro |
| dc.subject.proposal.eng.fl_str_mv |
Decision tree K-Nearest-Neighbors Machine learning Path loss model Radio wave propagation Random Forest (RF) |
| topic |
Decision tree K-Nearest-Neighbors Machine learning Path loss model Radio wave propagation Random Forest (RF) |
| description |
Wireless sensor networks play an essential role in modern agriculture, as they facilitate the monitoring of different variables that have an impact on crop yields. The successful operation of WSNs is highly dependent on their accurate deployment in the field, which requires proper modeling of radio wave propagation. In this study, we evaluate three path loss models obtained from machine learning: K-Nearest-Neighbors, Random Forest, and Decision Tree. The measurements were carried out on a cassava crop, one of Colombia's most important agricultural products. Compared to vegetation models, the use of ML allows for predictions with reduced error. |
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2023 |
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2023-11-22 |
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2024-07-04T13:14:06Z |
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2024-07-04T13:14:06Z 2026-11-22 |
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A. Barrios-Ulloa, E. De-La Hoz-Franco and A. Cama-Pinto, "Comparison of machine learning path loss model for wireless sensor networks in cassava crops," 2023 IEEE Colombian Caribbean Conference (C3), Barranquilla, Colombia, 2023, pp. 1-6, doi: 10.1109/C358072.2023.10436224. |
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https://hdl.handle.net/11323/13112 |
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10.1109/C358072.2023.10436224 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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A. Barrios-Ulloa, E. De-La Hoz-Franco and A. Cama-Pinto, "Comparison of machine learning path loss model for wireless sensor networks in cassava crops," 2023 IEEE Colombian Caribbean Conference (C3), Barranquilla, Colombia, 2023, pp. 1-6, doi: 10.1109/C358072.2023.10436224. 979-8-3503-4180-5 10.1109/C358072.2023.10436224 Corporación Universidad de la Costa REDICUC - Repositorio CUC 979-8-3503-4179-9 |
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https://hdl.handle.net/11323/13112 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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2023 IEEE Colombian Caribbean Conference (C3) |
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[1] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091908. [2] A. Barrios-Ulloa, P. Ariza-Colpas, H. Sánchez-Moreno, A. P. QuinteroLinero, and E. De la Hoz-Franco, “Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145285. [3] R. O. Abolade, S. O. Famakinde, S. I. Popoola, O. F. Oseni, A. A. Atayero, and S. Misra, Support Vector Machine for Path Loss Predictions in Urban Environment, vol. 12255 LNCS. Springer International Publishing, 2020. [4] A. Navarro, D. Guevara, and G. A. Florez, “An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields,” in 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020, no. September, pp. 1–3, doi: 10.23919/URSIGASS49373.2020.9232365. [5] L. Sevgi, “Groundwave modeling and simulation strategies and path loss prediction virtual tools,” IEEE Trans. Antennas Propag., vol. 55, no. 6 I, pp. 1591–1598, 2007, doi: 10.1109/TAP.2007.897256. [6] O. O. Shoewu, L. A. Akinyemi, and L. Oborkhale, “Towards Developing Path loss Models for Dryland and Wetland Environments,” IEEE AFRICON Conf., vol. 2019-Septe, 2019, doi: 10.1109/AFRICON46755.2019.9134041. [7] E. Greenberg and E. Klodzh, “Comparison of deterministic, empirical and physical propagation models in urban environments,” in 2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), 2015, pp. 1–5, doi: 10.1109/COMCAS.2015.7360394. [8] M. Ayadi, A. Ben-Zineb, and S. Tabbane, “A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks,” IEEE Trans. Antennas Propag., vol. 65, no. 7, pp. 3675–3683, 2017, doi: 10.1109/TAP.2017.2705112. [9] S. Ojo, A. Sari, and T. P. Ojo, “Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models,” Open J. Appl. Sci., vol. 12, no. 06, pp. 990– 1010, 2022, doi: 10.4236/ojapps.2022.126068. [10] A. Zappone, M. Di Renzo, M. Debbah, T. T. Lam, and X. Qian, “ModelAided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization,” IEEE Veh. Technol. Mag., vol. 14, no. 3, pp. 60–69, 2019, doi: 10.1109/MVT.2019.2921627. [11] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure,” IEEE Trans. Ind. Informatics, vol. 18, no. 10, pp. 6981–6990, 2022, doi: 10.1109/TII.2022.3142318. [12] S. Duangsuwan, P. Juengkittikul, and M. Myint Maw, “Path Loss Characterization Using Machine Learning Models for GS-to-UAVEnabled Communication in Smart Farming Scenarios,” Int. J. Antennas Propag., vol. 2021, 2021, doi: 10.1155/2021/5524709. [13] C. A. Oroza, Z. Zhang, T. Watteyne, and S. D. Glaser, “A MachineLearning-Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, pp. 576–584, 2017, doi: 10.1109/TCCN.2017.2741468. [14] N. Moraitis, L. Tsipi, D. Vouyioukas, A. Gkioni, and S. Louvros, “Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz,” Wirel. Networks, vol. 27, no. 6, pp. 4169–4188, 2021, doi: 10.1007/s11276-021-02682-3. [15] N. Sabri, S. S. Mohammed, S. Fouad, A. A. Syed, F. T. Al-Dhief, and A. Raheemah, “Investigation of Empirical Wave Propagation Models in Precision Agriculture,” in MATEC Web of Conferences, 2018, vol. 150, p. 06020, doi: https://doi.org/10.1051/matecconf/201815006020. [16] H. Dogan, “A new empirical propagation model depending on volumetric density in citrus orchards for wireless sensornetwork applications at sub-6 GHz frequency region,” Int. J. RF Microw. Comput. Eng., vol. 31, no. 9, p. e22778, 2021, doi: https://doi.org/10.1002/mmce.22778. [17] D. Cama-Pinto, M. Damas, J. A. Holgado-Terriza, F. Gómez-Mula, and A. Cama-Pinto, “Path loss determination using linear and cubic regression inside a classic tomato greenhouse,” Int. J. Environ. Res. Public Health, vol. 16, no. 10, p. 1744, 2019, doi: https://doi.org/10.3390/ijerph16101744. [18] İ. Yazici, I. Shayea, and J. Din, “A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems,” Eng. Sci. Technol. an Int. J., vol. 44, 2023, doi: 10.1016/j.jestch.2023.101455. [19] G. Vergos, S. P. Sotiroudis, G. Athanasiadou, G. V. Tsoulos, and S. K. Goudos, “Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction,” in 2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021, 2021, pp. 1–4, doi:10.1109/MOCAST52088.2021.9493374. [20] M. K. Elmezughi, O. Salih, T. J. Afullo, and K. J. Duffy, “Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels,” Sensors, vol. 22, no. 13, pp. 1–25, 2022, doi: 10.3390/s22134967. [21] [J. Zhang, L. Liu, Y. Fan, L. Zhuang, T. Zhou, and Z. Piao, “Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning,” IEEE Access, vol. 8, pp. 47797–47806, 2020, doi: 10.1109/ACCESS.2020.2979220. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© Copyright 2024 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfBarrios-Ulloa, Alexis52708f16454abbcd1efa672b0305fdb4600De-La-Hoz-Franco, Emiro7f8bc6c4d65f444fb00bd3778bc623fc600Cama-Pinto, Alejandro18a1bc13fe69004170fb453d42826bfb6002024-07-04T13:14:06Z2026-11-222024-07-04T13:14:06Z2023-11-22A. Barrios-Ulloa, E. De-La Hoz-Franco and A. Cama-Pinto, "Comparison of machine learning path loss model for wireless sensor networks in cassava crops," 2023 IEEE Colombian Caribbean Conference (C3), Barranquilla, Colombia, 2023, pp. 1-6, doi: 10.1109/C358072.2023.10436224.979-8-3503-4180-5https://hdl.handle.net/11323/1311210.1109/C358072.2023.10436224Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/979-8-3503-4179-9Wireless sensor networks play an essential role in modern agriculture, as they facilitate the monitoring of different variables that have an impact on crop yields. The successful operation of WSNs is highly dependent on their accurate deployment in the field, which requires proper modeling of radio wave propagation. In this study, we evaluate three path loss models obtained from machine learning: K-Nearest-Neighbors, Random Forest, and Decision Tree. The measurements were carried out on a cassava crop, one of Colombia's most important agricultural products. Compared to vegetation models, the use of ML allows for predictions with reduced error.6 páginasapplication/pdfengIEEEBarranquilla, Colombiahttps://ieeexplore-ieee-org.ezproxy.cuc.edu.co/document/10436224Comparison of machine learning path loss model for wireless sensor networks in cassava cropsCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a852023 IEEE Colombian Caribbean Conference (C3)[1] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091908.[2] A. Barrios-Ulloa, P. Ariza-Colpas, H. Sánchez-Moreno, A. P. QuinteroLinero, and E. De la Hoz-Franco, “Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145285.[3] R. O. Abolade, S. O. Famakinde, S. I. Popoola, O. F. Oseni, A. A. Atayero, and S. Misra, Support Vector Machine for Path Loss Predictions in Urban Environment, vol. 12255 LNCS. Springer International Publishing, 2020.[4] A. Navarro, D. Guevara, and G. A. Florez, “An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields,” in 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020, no. September, pp. 1–3, doi: 10.23919/URSIGASS49373.2020.9232365.[5] L. Sevgi, “Groundwave modeling and simulation strategies and path loss prediction virtual tools,” IEEE Trans. Antennas Propag., vol. 55, no. 6 I, pp. 1591–1598, 2007, doi: 10.1109/TAP.2007.897256.[6] O. O. Shoewu, L. A. Akinyemi, and L. Oborkhale, “Towards Developing Path loss Models for Dryland and Wetland Environments,” IEEE AFRICON Conf., vol. 2019-Septe, 2019, doi: 10.1109/AFRICON46755.2019.9134041.[7] E. Greenberg and E. Klodzh, “Comparison of deterministic, empirical and physical propagation models in urban environments,” in 2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), 2015, pp. 1–5, doi: 10.1109/COMCAS.2015.7360394.[8] M. Ayadi, A. Ben-Zineb, and S. Tabbane, “A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks,” IEEE Trans. Antennas Propag., vol. 65, no. 7, pp. 3675–3683, 2017, doi: 10.1109/TAP.2017.2705112.[9] S. Ojo, A. Sari, and T. P. Ojo, “Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models,” Open J. Appl. Sci., vol. 12, no. 06, pp. 990– 1010, 2022, doi: 10.4236/ojapps.2022.126068.[10] A. Zappone, M. Di Renzo, M. Debbah, T. T. Lam, and X. Qian, “ModelAided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization,” IEEE Veh. Technol. Mag., vol. 14, no. 3, pp. 60–69, 2019, doi: 10.1109/MVT.2019.2921627.[11] P. Pal, R. P. Sharma, S. Tripathi, C. Kumar, and D. Ramesh, “Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure,” IEEE Trans. Ind. Informatics, vol. 18, no. 10, pp. 6981–6990, 2022, doi: 10.1109/TII.2022.3142318.[12] S. Duangsuwan, P. Juengkittikul, and M. Myint Maw, “Path Loss Characterization Using Machine Learning Models for GS-to-UAVEnabled Communication in Smart Farming Scenarios,” Int. J. Antennas Propag., vol. 2021, 2021, doi: 10.1155/2021/5524709.[13] C. A. Oroza, Z. Zhang, T. Watteyne, and S. D. Glaser, “A MachineLearning-Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, pp. 576–584, 2017, doi: 10.1109/TCCN.2017.2741468.[14] N. Moraitis, L. Tsipi, D. Vouyioukas, A. Gkioni, and S. Louvros, “Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz,” Wirel. Networks, vol. 27, no. 6, pp. 4169–4188, 2021, doi: 10.1007/s11276-021-02682-3.[15] N. Sabri, S. S. Mohammed, S. Fouad, A. A. Syed, F. T. Al-Dhief, and A. Raheemah, “Investigation of Empirical Wave Propagation Models in Precision Agriculture,” in MATEC Web of Conferences, 2018, vol. 150, p. 06020, doi: https://doi.org/10.1051/matecconf/201815006020.[16] H. Dogan, “A new empirical propagation model depending on volumetric density in citrus orchards for wireless sensornetwork applications at sub-6 GHz frequency region,” Int. J. RF Microw. Comput. Eng., vol. 31, no. 9, p. e22778, 2021, doi: https://doi.org/10.1002/mmce.22778.[17] D. Cama-Pinto, M. Damas, J. A. Holgado-Terriza, F. Gómez-Mula, and A. Cama-Pinto, “Path loss determination using linear and cubic regression inside a classic tomato greenhouse,” Int. J. Environ. Res. Public Health, vol. 16, no. 10, p. 1744, 2019, doi: https://doi.org/10.3390/ijerph16101744.[18] İ. Yazici, I. Shayea, and J. Din, “A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems,” Eng. Sci. Technol. an Int. J., vol. 44, 2023, doi: 10.1016/j.jestch.2023.101455.[19] G. Vergos, S. P. Sotiroudis, G. Athanasiadou, G. V. Tsoulos, and S. K. Goudos, “Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction,” in 2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021, 2021, pp. 1–4, doi:10.1109/MOCAST52088.2021.9493374.[20] M. K. Elmezughi, O. Salih, T. J. Afullo, and K. J. Duffy, “Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels,” Sensors, vol. 22, no. 13, pp. 1–25, 2022, doi: 10.3390/s22134967.[21] [J. Zhang, L. Liu, Y. Fan, L. Zhuang, T. Zhou, and Z. Piao, “Wireless Channel Propagation Scenarios Identification: A Perspective of Machine Learning,” IEEE Access, vol. 8, pp. 47797–47806, 2020, doi: 10.1109/ACCESS.2020.2979220.61Decision treeK-Nearest-NeighborsMachine learningPath loss modelRadio wave propagationRandom Forest (RF)ORIGINALComparison of machine learning path loss model.pdfComparison of machine learning path loss model.pdfArtículoapplication/pdf1351662https://repositorio.cuc.edu.co/bitstream/11323/13112/1/Comparison%20of%20machine%20learning%20path%20loss%20model.pdf4e4d0b98b583f5bba23cac2dc8dd5e69MD51restricted accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstream/11323/13112/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessTEXTComparison of machine learning path loss model.pdf.txtComparison of machine learning path loss model.pdf.txtExtracted 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0000-0002-6525-453352708f16454abbcd1efa672b0305fdb46000000-0002-4926-74147f8bc6c4d65f444fb00bd3778bc623fc6000000-0002-1364-739418a1bc13fe69004170fb453d42826bfb600 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