Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions

Spain is Europe’s leading exporter of tomatoes harvested in greenhouses. The production of tomatoes should be kept and increased, supported by precision agriculture to meet food and commercial demand. The wireless sensor network (WSN) has demonstrated to be a tool to provide farmers with useful info...

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Autores:
Cama-Pinto, Dora
Damas, Miguel
Holgado-Terriza, Juan Antonio
Arrabal-Campos, Francisco Manuel
Gómez-Mula, Francisco
Martínez-Lao, Juan Antonio
Cama-Pinto, Alejandro
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7645
Acceso en línea:
https://hdl.handle.net/11323/7645
https://repositorio.cuc.edu.co/
Palabra clave:
Wireless propagation model
Precision agriculture
COST235
FITU-R
ITU-R
Weisbberger model
Propagation model
Regularized regressions
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_d06a3debc41b14757d051b1286d40d87
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7645
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
title Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
spellingShingle Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
Wireless propagation model
Precision agriculture
COST235
FITU-R
ITU-R
Weisbberger model
Propagation model
Regularized regressions
title_short Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
title_full Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
title_fullStr Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
title_full_unstemmed Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
title_sort Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions
dc.creator.fl_str_mv Cama-Pinto, Dora
Damas, Miguel
Holgado-Terriza, Juan Antonio
Arrabal-Campos, Francisco Manuel
Gómez-Mula, Francisco
Martínez-Lao, Juan Antonio
Cama-Pinto, Alejandro
dc.contributor.author.spa.fl_str_mv Cama-Pinto, Dora
Damas, Miguel
Holgado-Terriza, Juan Antonio
Arrabal-Campos, Francisco Manuel
Gómez-Mula, Francisco
Martínez-Lao, Juan Antonio
Cama-Pinto, Alejandro
dc.subject.spa.fl_str_mv Wireless propagation model
Precision agriculture
COST235
FITU-R
ITU-R
Weisbberger model
Propagation model
Regularized regressions
topic Wireless propagation model
Precision agriculture
COST235
FITU-R
ITU-R
Weisbberger model
Propagation model
Regularized regressions
description Spain is Europe’s leading exporter of tomatoes harvested in greenhouses. The production of tomatoes should be kept and increased, supported by precision agriculture to meet food and commercial demand. The wireless sensor network (WSN) has demonstrated to be a tool to provide farmers with useful information on the state of their plantations due to its practical deployment. However, in order to measure its deployment within a crop, it is necessary to know the communication coverage of the nodes that make up the network. The multipath propagation of radio waves between the transceivers of the WSN nodes inside a greenhouse is degraded and attenuated by the intricate complex of stems, branches, leaf twigs, and fruits, all randomly oriented, that block the line of sight, consequently generating a signal power loss as the distance increases. Although the COST235 (European Cooperation in Science and Technology - COST), ITU-R (International Telecommunications Union—Radiocommunication Sector), FITU-R (Fitted ITU-R), and Weisbberger models provide an explanation of the radio wave propagation in the presence of vegetation in the 2.4 GHz ICM band, some significant discrepancies were found when they are applied to field tests with tomato greenhouses. In this paper, a novel method is proposed for determining an empirical model of radio wave attenuation for vegetation in the 2.4 GHz band, which includes the vegetation height as a parameter in addition to the distance between transceivers of WNS nodes. The empirical attenuation model was obtained applying regularized regressions with a multiparametric equation using experimental signal RSSI measurements achieved by our own RSSI measurement system for our field tests in four plantations. The evaluation parameters gave 0.948 for R2 , 0.946 for R2 Adj considering fifth grade polynomial (20 parameters), and 0.942 for R2 , and 0.940 for R2 Adj when a reduction of parameters was applied using the cross validation (15 parameters). These results verify the rationality and reliability of the empirical model. Finally, the model was validated considering experimental data from other plantations, reaching similar results to our proposed model.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019-11-19
dc.date.accessioned.none.fl_str_mv 2020-12-29T17:38:00Z
dc.date.available.none.fl_str_mv 2020-12-29T17:38:00Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 1424-3210
1424-8220
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7645
dc.identifier.doi.spa.fl_str_mv doi:10.3390/s20226621
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1424-3210
1424-8220
doi:10.3390/s20226621
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7645
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Suman, S.; Kumar, S.; De, S. Path Loss Model for UAV-Assisted RFET. IEEE Commun. Lett. 2018, 22, 2048–2051. [CrossRef]
2. Caicedo-Ortiz, J.G.; De-la-Hoz-Franco, E.; Morales Ortega, R.; Piñeres-Espitia, G.; Combita-Niño, H.; Estévez, F.; Cama-Pinto, A. Monitoring system for agronomic variables based in WSN technology on cassava crops. Comput. Electron. Agric. 2018, 145, 275–281. [CrossRef]
3. Zapata-Sierra, A.J.; Cama-Pinto, A.; Montoya, F.G.; Alcayde, A.; Manzano-Agugliaro, F. Wind missing data arrangement using wavelet based techniques for getting maximum likelihood. Energy Convers. Manag. 2019, 185, 552–561. [CrossRef]
4. Cama-Pinto, D.; Chávez-Muñoz, P.D.; Solano-Escorcia, A.F.; Cama-Pinto, A. Data supporting the reconstruction study of missing wind speed logs using wavelet techniques for getting maximum likelihood. Data Brief 2020, 31, 105835. [CrossRef] [PubMed]
5. Hamasaki, T. Propagation Characteristics of A 2.4 GHz Wireless Sensor Module with A Pattern Antenna in Forestry and Agriculture Field. In Proceedings of the 2019 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), Nanjing, China, 28–30 August 2019; p. 8929207. [CrossRef]
6. Montoya, F.G.; Gomez, J.; Manzano-Agugliaro, F.; Cama, A.; García-Cruz, A.; De La Cruz, J.L. 6LoWSoft: A software suite for the design of outdoor environmental measurements. J. Food Agric. Environ. 2013, 11, 2584–2586.
7. Picallo, I.; Klaina, H.; Lopez-Iturri, P.; Aguirre, E.; Celaya-Echarri, M.; Azpilicueta, L.; Eguizábal, A.; Falcone, F.; Alejos, A. A radio channel model for D2D communications blocked by single trees in forest environments. Sensors 2019, 19, 4606. [CrossRef]
8. Foerster, A.; Udugama, A.; Görg, C.; Kuladinithi, K.; Timm-Giel, A.; Cama-Pinto, A. A novel data dissemination model for organic data flows. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering; Springer International Publishing: Cham, Switzerland, 2015; Volume 158, pp. 239–252. [CrossRef]
9. Cama-Pinto, A.; Piñeres-Espitia, G.; Comas-González, Z.; Vélez-Zapata, J.; Gómez-Mula, F. Design of a monitoring network of meteorological variables related to tornadoes in Barranquilla-Colombia and its metropolitan area. Ingeniare 2017, 25, 585–598. [CrossRef]
10. Cama-Pinto, A.; Piñeres-Espitia, G.; Caicedo-Ortiz, J.; Ramírez-Cerpa, E.; Betancur-Agudelo, L.; Gómez-Mula, F. Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules. Int. J. Distrib. Sens. Netw. 2017, 13. [CrossRef]
11. Brinkhoff, J.; Hornbuckle, J. Characterization of WiFi signal range for agricultural WSNs. In Proceedings of the 2017 23rd Asia-Pacific Conference on Communications: Bridging the Metropolitan and the Remote, (APCC), Perth, Australia, 11–13 December 2017; pp. 1–6. [CrossRef]
12. Montoya, F.G.; Gómez, J.; Cama, A.; Zapata-Sierra, A.; Martínez, F.; De La Cruz, J.L.; Manzano-Agugliaro, F. A monitoring system for intensive agriculture based on mesh networks and the android system. Comput. Electron. Agric. 2013, 99, 14–20. [CrossRef]
13. Khairunnniza-Bejo, S.; Ramli, N.; Muharam, F.M. Wireless sensor network (WSN) applications in plantation canopy areas: A review. Asian J. Sci. Res. 2018, 11, 151–161. [CrossRef]
14. Saeed, N.; Alouini, M.-S.; Al-Naffouri, T.Y. Toward the Internet of Underground Things: A Systematic Survey. IEEE Commun. Surv. Tutor. 2019, 21, 3443–3466. [CrossRef]
15. Razafimandimby, C.; Loscrí, V.; Vegni, A.M.; Neri, A. Efficient Bayesian communication approach for smart agriculture applications. In Proceedings of the IEEE Vehicular Technology Conference, Toronto, ON, Canada, 24–27 September 2017; pp. 1–5. [CrossRef]
16. Srisooksai, T.; Kaemarungsi, K.; Takada, J.; Saito, K. Radio propagation measurement and characterization in outdoor tall food grass agriculture field for wireless sensor network at 2.4 GHz band. Prog. Electromagn. Res. C 2018, 88, 43–58. [CrossRef]
17. Cama-Pinto, A.; Gil-Montoya, F.; Gómez-López, J.; García-Cruz, A.; Manzano-Agugliaro, F. Wireless surveillance sytem for greenhouse crops. DYNA 2014, 81, 164–170. [CrossRef]
18. Peng, Y.; Xiao, Y.; Fu, Z.; Dong, Y.; Zheng, Y.; Yan, H.; Li, X. Precision irrigation perspectives on the sustainable water-saving of field crop production in China: Water demand prediction and irrigation scheme optimization. J. Clean. Prod. 2019, 230, 365–377. [CrossRef]
19. Peng, X.; Ye, T.; Wang, Y. Research and design of precision irrigation system based on artificial neural network. In Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, Shenyang, China, 9–11 June 2018; pp. 3865–3870. [CrossRef]
20. Li, Z.; Sun, Z.; Singh, T.; Oware, E. Large range soil moisture sensing for inhomogeneous environments using magnetic induction networks. In Proceedings of the 2019 IEEE Global Communications Conference, (GLOBECOM), Waikoloa, HI, USA, 9–13 December 2019. [CrossRef]
21. Caparrós-Martínez, J.L.; Rueda-Lópe, N.; Milán-García, J.; de Pablo Valenciano, J. Public policies for sustainability and water security: The case of Almeria (Spain). Glob. Ecol. Conserv. 2020, 23, e01037. [CrossRef]
22. Parlato, M.C.M.; Valenti, F.; Porto, S.M.C. Covering plastic films in greenhouses system: A GIS-based model to improve post use suistainable management. J. Environ. Manag. 2020, 263, 110389. [CrossRef]
23. Massa, D.; Magán, J.J.; Montesano, F.F.; Tzortzakis, N. Minimizing water and nutrient losses from soilless cropping in southern Europe. Agric. Water Manag. 2020, 241, 106395. [CrossRef]
24. Manríquez-Altamirano, A.; Sierra-Pérez, J.; Muñoz, P.; Gabarrell, X. Analysis of urban agriculture solid waste in the frame of circular economy: Case study of tomato crop in integrated rooftop greenhouse. Sci. Total Environ. 2020, 734, 139375. [CrossRef]
25. Téllez, M.M.; Cabello, T.; Gámez, M.; Burguillo, F.J.; Rodríguez, E. Comparative study of two predatory mites Amblyseius swirskii Athias-Henriot and Transeius montdorensis (Schicha) by predator-prey models for improving biological control of greenhouse cucumber. Ecol. Model. 2020, 431, 109197. [CrossRef]
26. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Gómez-Mula, F.; Cama-Pinto, A. Path loss determination using linear and cubic regression inside a classic tomato greenhouse. Int. J. Environ. Res. Public Health 2019, 16, 1744. [CrossRef]
27. Echarri, M.C.; Azpilicueta, L.; Iturri, P.L.; Aguirre, E.; Falcone, F. Performance evaluation and interference characterization of wireless sensor networks for complex high-node density scenarios. Sensors 2019, 19, 3516. [CrossRef] [PubMed]
28. Rahim, H.M.; Leow, C.Y.; Rahman, T.A.; Arsad, A.; Malek, M.A. Foliage attenuation measurement at millimeter wave frequencies in tropical vegetation. In Proceedings of the 2017 IEEE 13th Malaysia International Conference on Communications (MICC), Johor Bahru, Malaysia, 28–30 November 2017; pp. 241–246. [CrossRef]
29. Yang, S.; Zhang, J.; Zhang, J. Impact of Foliage on Urban MmWave Wireless Propagation Channel: A Ray-tracing Based Analysis. In Proceedings of the 2019 International Symposium on Antennas and Propagation (ISAP), Xi’an, China, 27–30 October 2019.
30. Caicedo, J.G.; Acosta, M.; Cama-Pinto, A. WSN deployment model for measuring climate variables that cause strong precipitation. Prospectiva 2015. [CrossRef]
31. Popov, V. Cross-polarization effect of radio waves propagation by forest vegetation in wireless communication systems on transport. Procedia Comput. Sci. 2019, 149, 195–201. [CrossRef]
32. Raheemah, A.; Sabri, N.; Salim, M.S.; Ehkan, P.; Kamaruddin, R.; Ahmad, R.B.; Jaafar, M.N.; Aljunid, S.A.; Chemat, M.H. Influences of parts of tree on propagation path losses for wsn deployment in greenhouse environments. J. Theor. Appl. Inf. Technol. 2015, 81, 552–557.
33. Li, P.; Peng, Y.; Wang, J. Propagation characteristics of 2.4 GHz radio wave in greenhouse of green peppers. Nongye Jixie Xuebao/Trans. Chin. Soc. Agric. Mach. 2014, 45, 251–255. [CrossRef]
34. Vougioukas, S.; Anastassiu, H.T.; Regen, C.; Zude, M. Influence of foliage on radio path losses (PLs) for Wireless Sensor Network (WSN) planning in orchards. Biosyst. Eng. 2013, 114, 454–465. [CrossRef]
35. Raheemah, A.; Sabri, N.; Salim, M.S.; Ehkan, P.; Ahmad, R.B. New empirical path loss model for wireless sensor networks in mango greenhouses. Comput. Electron. Agric. 2016, 127, 553–560. [CrossRef]
36. Montero, O.R.; Araque, J.L. Approximate modeling of Electromagnetic Propagation through Vegetation. In Proceedings of the 2018 8th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), Cartagena des Indias, Colombia, 10–14 September 2018; pp. 928–931. [CrossRef]
37. Sabri, N.; Mohammed, S.S.; Fouad, S.; Syed, A.A.; Al-Dhief, F.T.; Raheemah, A. Investigation of Empirical Wave Propagation Models in Precision Agriculture. MATEC Web Conf. 2018, 150, 6020. [CrossRef]
38. Lytaev, M.S.; Vladyko, A.G. Split-step Padé Approximations of the Helmholtz Equation for Radio Coverage Prediction over Irregular Terrain. In Proceedings of the 2018 Advances in Wireless and Optical Communications (RTUWO), Riga, Latvia, 15–16 November 2018; pp. 179–184. [CrossRef]
39. Militaru, L.G.; Popescu, D.; Mateescu, C.; Ichim, L. Correlation between Distance and Frequency Bands in Hybrid Air-Ground Sensor Networks. In Proceedings of the 5th International Conference on Control, Decision and Information Technologies (CoDIT), Thessaloniki, Greece, 10–13 April 2018; pp. 247–252. [CrossRef]
40. Granda, F.; Azpilicueta, L.; Vargas-Rosales, C.; Lopez-Iturri, P.; Aguirre, E.; Falcone, F. Integration of Wireless Sensor Networks in Intelligent Transportation Systems within Smart City Context. In Proceedings of the 2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting (APSURSI), Boston, MA, USA, 8–13 July 2018; pp. 375–376. [CrossRef]
41. Montero, O.; Pantoja, J.J.; Patino, M.; Pineda, E.; Martinez, D.; Angel, G.; Cruz, J.; Suarez, M.; Vega, F. Attenuation of Radiofrequency Waves due to Vegetation in Colombia. In Proceedings of the 2018 8th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), Cartagena des Indias, Colombia, 10–14 September 2018; pp. 940–943. [CrossRef]
42. Shutimarrungson, N.; Wuttidittachotti, P. Realistic propagation effects on wireless sensor networks for landslide management. Eurasip J. Wirel. Commun. Netw. 2019, 94. [CrossRef]
43. Zolertia. Re-Mote Datasheet. 2017. Available online: https://github.com/Zolertia/Resources/wiki/RE-Mote (accessed on 23 August 2020).
44. Galvan-Tejada, G.M.; Aguilar-Torrentera, J. Analysis of propagation for wireless sensor networks in outdoors. Prog. Electromagn. Res. B 2019, 83, 153–175. [CrossRef]
45. Anil, G.N. Designing an Energy Efficient Routing for Subsystems Sensors in Internet of Things Eco-System Using Distributed Approach. Adv. Intell. Syst. Comput. 2020, 1224, 111–121. [CrossRef]
46. Lagarias, J.C.; Reeds, J.A.; Wright, M.H.; Wright, P.E. Convergence properties of the Nelder-Mead simplex method in low dimensions. Siam J. Optim. 1998, 9, 112–147. [CrossRef]
47. Provencher, S.W. A constrained regularization method for inverting data represented by linear algebraic or integral equations. Comput. Phys. Commun. 1982, 27, 213–227. [CrossRef]
48. Provencher, S.W. CONTIN: A general purpose constrained regularization program for inverting noisy linear algebraic and integral equations. Comput. Phys. Commun. 1982, 27, 229–242. [CrossRef]
49. Tikhonov, A.N. On the Solution of Ill-Posed Problems and the Method of Regularization. Dokl. Akad. Nauk SSSR 1963, 151, 501–504. [CrossRef]
50. Ansah, M.R.; Sowah, R.A.; Melià-Seguí, J.; Katsriku, F.A.; Vilajosana, X.; Banahene, W.O. Characterising foliage influence on LoRaWAN pathloss in a tropical vegetative environment. IET Wirel. Sens. Syst. 2020, 10, 181–197. [CrossRef]
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spelling Cama-Pinto, DoraDamas, MiguelHolgado-Terriza, Juan AntonioArrabal-Campos, Francisco ManuelGómez-Mula, FranciscoMartínez-Lao, Juan AntonioCama-Pinto, Alejandro2020-12-29T17:38:00Z2020-12-29T17:38:00Z2019-11-191424-32101424-8220https://hdl.handle.net/11323/7645doi:10.3390/s20226621Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Spain is Europe’s leading exporter of tomatoes harvested in greenhouses. The production of tomatoes should be kept and increased, supported by precision agriculture to meet food and commercial demand. The wireless sensor network (WSN) has demonstrated to be a tool to provide farmers with useful information on the state of their plantations due to its practical deployment. However, in order to measure its deployment within a crop, it is necessary to know the communication coverage of the nodes that make up the network. The multipath propagation of radio waves between the transceivers of the WSN nodes inside a greenhouse is degraded and attenuated by the intricate complex of stems, branches, leaf twigs, and fruits, all randomly oriented, that block the line of sight, consequently generating a signal power loss as the distance increases. Although the COST235 (European Cooperation in Science and Technology - COST), ITU-R (International Telecommunications Union—Radiocommunication Sector), FITU-R (Fitted ITU-R), and Weisbberger models provide an explanation of the radio wave propagation in the presence of vegetation in the 2.4 GHz ICM band, some significant discrepancies were found when they are applied to field tests with tomato greenhouses. In this paper, a novel method is proposed for determining an empirical model of radio wave attenuation for vegetation in the 2.4 GHz band, which includes the vegetation height as a parameter in addition to the distance between transceivers of WNS nodes. The empirical attenuation model was obtained applying regularized regressions with a multiparametric equation using experimental signal RSSI measurements achieved by our own RSSI measurement system for our field tests in four plantations. The evaluation parameters gave 0.948 for R2 , 0.946 for R2 Adj considering fifth grade polynomial (20 parameters), and 0.942 for R2 , and 0.940 for R2 Adj when a reduction of parameters was applied using the cross validation (15 parameters). These results verify the rationality and reliability of the empirical model. Finally, the model was validated considering experimental data from other plantations, reaching similar results to our proposed model.Cama-Pinto, Dora-will be generated-orcid-0000-0003-0726-196X-600Damas, Miguel-will be generated-orcid-0000-0003-2599-8076-600Holgado-Terriza, Juan Antonio-will be generated-orcid-0000-0002-8031-1276-600Arrabal-Campos, Francisco ManuelGómez-Mula, FranciscoMartínez-Lao, Juan AntonioCama-Pinto, Alejandro-will be generated-orcid-0000-0002-1364-7394-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sensorshttps://www.mdpi.com/1424-8220/20/22/6621Wireless propagation modelPrecision agricultureCOST235FITU-RITU-RWeisbberger modelPropagation modelRegularized regressionsEmpirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressionsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Suman, S.; Kumar, S.; De, S. Path Loss Model for UAV-Assisted RFET. IEEE Commun. Lett. 2018, 22, 2048–2051. [CrossRef]2. Caicedo-Ortiz, J.G.; De-la-Hoz-Franco, E.; Morales Ortega, R.; Piñeres-Espitia, G.; Combita-Niño, H.; Estévez, F.; Cama-Pinto, A. Monitoring system for agronomic variables based in WSN technology on cassava crops. Comput. Electron. Agric. 2018, 145, 275–281. [CrossRef]3. Zapata-Sierra, A.J.; Cama-Pinto, A.; Montoya, F.G.; Alcayde, A.; Manzano-Agugliaro, F. Wind missing data arrangement using wavelet based techniques for getting maximum likelihood. Energy Convers. Manag. 2019, 185, 552–561. [CrossRef]4. Cama-Pinto, D.; Chávez-Muñoz, P.D.; Solano-Escorcia, A.F.; Cama-Pinto, A. Data supporting the reconstruction study of missing wind speed logs using wavelet techniques for getting maximum likelihood. Data Brief 2020, 31, 105835. [CrossRef] [PubMed]5. Hamasaki, T. Propagation Characteristics of A 2.4 GHz Wireless Sensor Module with A Pattern Antenna in Forestry and Agriculture Field. In Proceedings of the 2019 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), Nanjing, China, 28–30 August 2019; p. 8929207. [CrossRef]6. Montoya, F.G.; Gomez, J.; Manzano-Agugliaro, F.; Cama, A.; García-Cruz, A.; De La Cruz, J.L. 6LoWSoft: A software suite for the design of outdoor environmental measurements. J. Food Agric. Environ. 2013, 11, 2584–2586.7. Picallo, I.; Klaina, H.; Lopez-Iturri, P.; Aguirre, E.; Celaya-Echarri, M.; Azpilicueta, L.; Eguizábal, A.; Falcone, F.; Alejos, A. A radio channel model for D2D communications blocked by single trees in forest environments. Sensors 2019, 19, 4606. [CrossRef]8. Foerster, A.; Udugama, A.; Görg, C.; Kuladinithi, K.; Timm-Giel, A.; Cama-Pinto, A. A novel data dissemination model for organic data flows. 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