Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses

Precision agriculture and smart farming are concepts that are acquiring an important boom due to their relationship with the Internet of Things (IoT), especially in the search for new mechanisms and procedures that allow for sustainable and efficient agriculture to meet future demand from an increas...

Full description

Autores:
Cama-Pinto, Dora
Holgado-Terriza, Juan Antonio
Damas, Miguel
Gómez Mula, Francisco
Cama-Pinto, Alejandro
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8854
Acceso en línea:
https://hdl.handle.net/11323/8854
https://doi.org/10.3390/inventions6040066
https://repositorio.cuc.edu.co/
Palabra clave:
Wireless sensor networks
WSN
Received signal strength indicator
RSSI
Internet of Things
IoT
Free space pathloss
Smart farming
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_56301be966af72fc9f7f8d20d08a388b
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8854
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
title Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
spellingShingle Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
Wireless sensor networks
WSN
Received signal strength indicator
RSSI
Internet of Things
IoT
Free space pathloss
Smart farming
title_short Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
title_full Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
title_fullStr Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
title_full_unstemmed Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
title_sort Radio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhouses
dc.creator.fl_str_mv Cama-Pinto, Dora
Holgado-Terriza, Juan Antonio
Damas, Miguel
Gómez Mula, Francisco
Cama-Pinto, Alejandro
dc.contributor.author.spa.fl_str_mv Cama-Pinto, Dora
Holgado-Terriza, Juan Antonio
Damas, Miguel
Gómez Mula, Francisco
Cama-Pinto, Alejandro
dc.subject.spa.fl_str_mv Wireless sensor networks
WSN
Received signal strength indicator
RSSI
Internet of Things
IoT
Free space pathloss
Smart farming
topic Wireless sensor networks
WSN
Received signal strength indicator
RSSI
Internet of Things
IoT
Free space pathloss
Smart farming
description Precision agriculture and smart farming are concepts that are acquiring an important boom due to their relationship with the Internet of Things (IoT), especially in the search for new mechanisms and procedures that allow for sustainable and efficient agriculture to meet future demand from an increasing population. Both concepts require the deployment of sensor networks that monitor agricultural variables for the integration of spatial and temporal agricultural data. This paper presents a system that has been developed to measure the attenuation of radio waves in the 2.4 GHz free band (ISM- Industrial, Scientific and Medical) when propagating inside a tomato greenhouse based on the received signal strength indicator (RSSI), and a procedure for using the system to measure RSSI at different distances and heights. The system is based on Zolertia Re-Mote nodes with the Contiki operating system and a Raspberry Pi to record the data obtained. The receiver node records the RSSI at different locations in the greenhouse with the transmitter node and at different heights. In addition, a study of the radio wave attenuation was measured in a tomato greenhouse, and we publish the corresponding obtained dataset in order to share with the research community.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-11-08T18:56:25Z
dc.date.available.none.fl_str_mv 2021-11-08T18:56:25Z
dc.date.issued.none.fl_str_mv 2021-10-12
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 2411-5134
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8854
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/inventions6040066
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 2411-5134
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8854
https://doi.org/10.3390/inventions6040066
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Rangwani, D.; Sadhukhan, D.; Ray, S.; Khan, M.K.; Dasgupta, M. An improved privacy preserving remote user authentication scheme for agricultural wireless sensor network. Trans. Emerg. Telecommun. Technol. 2021, 32, e4218. [CrossRef]
2. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.; Gómez-Mula, F.; Calderin-Curtidor, A.; Martínez-Lao, J.; Cama-Pinto, A. 5G Mobile Phone Network Introduction in Colombia. Electronics 2021, 10, 922. [CrossRef]
3. Mentsiev, A.U.; Gatina, F.F. Data analysis and digitalisation in the agricultural industry. IOP Conf. Series Earth Environ. Sci. 2021, 677, 32101. [CrossRef]
4. Azman, A.S.; Lee, M.Y.; Subramaniam, S.K.; Feroz, F.S. Novel Wireless Sensor Network Routing Protocol Performance Evaluation using Diverse Packet Size for Agriculture Application. Int. J. Integr. Eng. 2021, 13, 16–28. [CrossRef]
5. Vanishree, K.; Nagaraja, G.S. Emerging Line of Research Approach in Precision Agriculture: An Insight Study. Int. J. Adv. Comput. Sci. Appl. 2021, 12. [CrossRef]
6. 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]
7. Caicedo-Ortiz, J.G.; De-La-Hoz-Franco, E.; Ortega, R.M.; 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]
8. Caicedo Ortiz, J.G.; Acosta Coll, M.A.; Cama-Pinto, A. WSN deployment model for measuring climate variables that cause strong precipitation. Prospectiva 2015, 13, 106–115. [CrossRef]
9. Miao, Y.; Zhao, C.; Wu, H. Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption. Int. J. Agric. Biol. Eng. 2021, 14, 142–150. [CrossRef]
10. Razafimandimby, C.; Loscri, V.; Vegni, A.M.; Neri, A. Efficient Bayesian Communication Approach for Smart Agriculture Applications. In Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, ON, Canada, 24–27 September 2017; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2017; pp. 1–5.
11. Salim, C.; Mitton, N. K-predictions based data reduction approach in WSN for smart agriculture. Computing 2020, 103, 509–532. [CrossRef]
12. Wu, H.; Miao, Y.; Li, F.; Zhu, L. Empirical Modeling and Evaluation of Multi-Path Radio Channels on Wheat Farmland Based on Communication Quality. Trans. ASABE 2016, 59, 759–767. [CrossRef]
13. 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. [CrossRef]
14. 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.
15. Hsiao, S.-J.; Sung, W.-T. A Study on Using a Wireless Sensor Network to Design a Plant Monitoring System. Intell. Autom. Soft Comput. 2021, 27, 359–377. [CrossRef]
16. Xuanrong, P.; Tingdong, Y.; Yuesheng, W. Research and design of precision irrigation system based on artificial neural network. In Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2018; pp. 3865–3870.
17. 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]
18. Zhang, H.; Li, H. Node Localization Technology of Wireless Sensor Network Based on RSSI Algorithm. Int. J. Online Eng. 2016, 12, 51–57. [CrossRef]
19. Azmi, N.; Kamarudin, L.; Zakaria, A.; Ndzi, D.; Rahiman, M.; Zakaria, S.; Mohamed, L. RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques. Sensors 2021, 21, 1875. [CrossRef]
20. Piñeres-Espitia, G.; Cama-Pinto, A.; De La Rosa Morrón, D.; Estevez, F.; Cama-Pinto, D. Design of a low cost weather station for detecting environmental changes. Espacios 2017, 38, 13.
21. Foerster, A.; Udugama, A.; Görg, C.; Kuladinithi, K.; Timm-Giel, A.; Cama-Pinto, A. A Novel Data Dissemination Model for Organic Data Flows. In Proceedings of the International Conference on Mobile Network and Management, Santander, Spain, 16–18 September 2015; Springer: Berlin/Heidelberg, Germany, 2015; pp. 239–252.
22. Cama-Pinto, A.; Gil Montoya, F.; Gómez, J.; De La Cruz, J.L.; Manzano-Agugliaro, F. Integration of communication technologies in sensor networks to monitor the Amazon environment. J. Clean. Prod. 2013, 59, 32–42. [CrossRef]
23. Farooqui, N.A.; Tyagi, A. Data Mining and Fusion Techniques for Wireless Intelligent Sensor Networks. In Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario’s; Springer: Berlin/Heidelberg, Germany, 2020; pp. 592–615.
24. 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]
25. Maiolo, L.; Polese, D. Advances in sensing technologies for smart monitoring in precise agriculture. In Proceedings of the SENSORNETS 2021—Proceedings of the 10th International Conference on Sensor Networks, Vienna, Austria, 9–10 February 2021; pp. 151–158.
26. Sathish, C.; Srinivasan, K. An artificial bee colony algorithm for efficient optimized data aggregation to agricultural IoT devices application. J. Appl. Sci. Eng. 2021, 24, 927–936. [CrossRef]
27. Saiz-Rubio, V.; Rovira-Más, F. From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy 2020, 10, 207. [CrossRef]
28. Subashini, M.M.; Das, S.; Heble, S.; Raj, U.; Karthik, R. Internet of Things based wireless plant sensor for smart farming. Indones. J. Electr. Eng. Comput. Sci. 2018, 10, 456–468. [CrossRef]
29. Abouzar, P.; Michelson, D.G.; Hamdi, M. RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture. IEEE Trans. Wirel. Commun. 2016, 15, 6638–6650. [CrossRef]
30. Xu, L. Design of a RSSI Location System for Greenhouse Environment. Int. J. Distrib. Sens. Netw. 2015, 11, 525861. [CrossRef]
31. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Gomez-Mula, F.; Cama-Pinto, A. Desarrollo de un sistema para medición y registro de RSSI en invernaderos. Av. En Arquit. Y Tecnol. De Comput. Actas De Las Jorn. SARTECO 2019, 649–654. [CrossRef]
32. Li, T.; Zhang, M.; Ji, Y.H.; Sha, S.; Jiang, Y.Q.; Li, M.Z. Management of CO2 in a tomato greenhouse using WSN and BPNN techniques. Int. J. Agric. Biol. Eng. 2015, 8, 43–51. [CrossRef]
33. García, L.; Parra, L.; Jimenez, J.; Parra, M.; Lloret, J.; Mauri, P.; Lorenz, P. Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas. Sensors 2021, 21, 1693. [CrossRef]
34. Aung, S.M.Y.; Pattanaik, K.K. Path Loss Measurement for Wireless Communication in Industrial Environments. In Proceedings of the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 13–14 March 2020; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2020; pp. 1–5.
35. Navarro, A.; Guevara, D.; Florez, G.A. An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields. In Proceedings of the 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, Rome, Italy, 29 August–5 September 2020; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2020.
36. Pal, P.; Sharma, R.P.; Tripathi, S.; Kumar, C.; Ramesh, D. 2.4 GHz RF Received Signal Strength Based Node Separation in WSN Monitoring Infrastructure for Millet and Rice Vegetation. IEEE Sens. J. 2021, 21, 18298–18306. [CrossRef]
37. Wang, J.; Peng, Y.; Li, P. Propagation Characteristics of Radio Wave in Plastic Greenhouse. In Proceedings of the International Conference on Computer and Computing Technologies in Agriculture, Beijing, China, 27–30 September 2015; Springer: Berlin/Heidelberg, Germany, 2016; pp. 208–215.
38. Widodo, S.; Pratama, E.A.; Pramono, S.; Basuki, S.B. Outdoor propagation modeling for wireless sensor networks 2.4 GHz. In Proceedings of the 2017 IEEE International Conference on Communication, Networks and Satellite (Comnetsat), Semarang, Indonesia, 5–7 October 2017; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2017; pp. 158–162.
39. Cama-Pinto, A.; Espitia, G.D.P.; Caicedo, J.G.; Ramirez-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]
40. Shue, S.; Johnson, L.E.; Conrad, J.M. Utilization of XBee ZigBee modules and MATLAB for RSSI localization applications. In Proceedings of the SoutheastCon 2017, Concord, NC, USA, 30 March–2 April 2017; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2017.
41. 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] [PubMed]
42. Van Herbruggen, B.; Jooris, B.; Rossey, J.; Ridolfi, M.; Macoir, N.; Van Den Brande, Q.; Lemey, S.; De Poorter, E. Wi-pos: A low-cost, open source ultra-wideband (UWB) hardware platform with long range sub-GHZ backbone. Sensors 2019, 19, 1548. [CrossRef]
43. Bezunartea, M.; Wang, C.; Braeken, A.; Steenhaut, K. Multi-radio Solution for Improving Reliability in RPL. In Proceedings of the 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Bologna, Italy, 9–12 September 2018; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2018; pp. 129–134.
44. Texas Instruments—Descripción CC2538. Available online: http://www.ti.com/product/CC2538/description (accessed on 21 July 2021).
45. Gomez, J.; Villar, E.; Molero, G.; Cama, A. Evaluation of high performance clusters in private cloud computing environments. In Distributed Computing and Artificial Intelligence; Springer: Berlin/Heidelberg, Germany, 2012; pp. 305–312.
46. ERCIM News. Contiki: Bringing IP to Sensor Networks. Available online: https://ercim-news.ercim.eu/en76/rd/contikibringing-ip-to-sensor-networks (accessed on 21 July 2021).
47. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Arrabal-Campos, F.M.; Gómez-Mula, F.; Martínez-Lao, J.A.M.; Cama-Pinto, A. Empirical Model of Radio Wave Propagation in the Presence of Vegetation inside Greenhouses Using Regularized Regressions. Sensors 2020, 20, 6621. [CrossRef] [PubMed]
48. Staudemeyer, R.C.; Pöhls, H.C.; Wójcik, M. What it takes to boost Internet of Things privacy beyond encryption with unobservable communication: A survey and lessons learned from the first implementation of DC-net. J. Reliab. Intell. Environ. 2019, 5, 41–64. [CrossRef]
49. Dunkels, A.; Gronvall, B.; Voigt, T. Contiki—A lightweight and flexible operating system for tiny networked sensors. In Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks, Tampa, FL, USA, 16–18 November 2004; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2004.
50. Dunkels, A.; Österlind, F.; He, Z. An adaptive communication architecture for wireless sensor networks. In Proceedings of the SenSys’07—Proceedings of the 5th ACM Conference on Embedded Networked Sensor Systems, Sydney, Australia, 6–9 November 2007; Machinery: New York, NY, USA, 2007; pp. 335–349.
51. Vougioukas, S.; Anastassiu, H.; 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]
52. 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]
53. Cama-Pinto, D.; Holgado-Terriza, J.A.; Damas, M.; Gómez-Mula, F.; Cama-Pinto, A. Tomato Greenhouse Measurement of RSSI in Almeria Spain. Available online: https://data.mendeley.com/datasets/nhk3gs7gmm/1 (accessed on 8 September 2021).
54. Zennaro, M.; Bagula, A.; Gascon, D.; Noveleta, A.B. Long distance wireless sensor networks: Simulation vs. reality. In Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions, NSDR’10, San Francisco, CA, USA, 15 June 2010.
dc.rights.spa.fl_str_mv CC0 1.0 Universal
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/publicdomain/zero/1.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Inventions
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://www.mdpi.com/2411-5134/6/4/66
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/dc5c5f70-e70d-4d2b-a87b-b4c00e34ad96/download
https://repositorio.cuc.edu.co/bitstreams/66a280bd-4c12-4781-9004-93b66114ded6/download
https://repositorio.cuc.edu.co/bitstreams/38a6a926-b322-477f-8af0-a30565bec767/download
https://repositorio.cuc.edu.co/bitstreams/58a18a89-c321-4e12-aa6e-ea17292e9b18/download
https://repositorio.cuc.edu.co/bitstreams/d5b1c8bf-753b-412e-ae38-f35a09e4ef8a/download
bitstream.checksum.fl_str_mv b2eff191132cc58ae7d87c48ea7ba1dc
42fd4ad1e89814f5e4a476b409eb708c
e30e9215131d99561d40d6b0abbe9bad
3a195e0670796da589984017532538d6
0d9f5e87b7a2b270558cdbbf297fe085
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760740526194688
spelling Cama-Pinto, DoraHolgado-Terriza, Juan AntonioDamas, MiguelGómez Mula, FranciscoCama-Pinto, Alejandro2021-11-08T18:56:25Z2021-11-08T18:56:25Z2021-10-122411-5134https://hdl.handle.net/11323/8854https://doi.org/10.3390/inventions6040066Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Precision agriculture and smart farming are concepts that are acquiring an important boom due to their relationship with the Internet of Things (IoT), especially in the search for new mechanisms and procedures that allow for sustainable and efficient agriculture to meet future demand from an increasing population. Both concepts require the deployment of sensor networks that monitor agricultural variables for the integration of spatial and temporal agricultural data. This paper presents a system that has been developed to measure the attenuation of radio waves in the 2.4 GHz free band (ISM- Industrial, Scientific and Medical) when propagating inside a tomato greenhouse based on the received signal strength indicator (RSSI), and a procedure for using the system to measure RSSI at different distances and heights. The system is based on Zolertia Re-Mote nodes with the Contiki operating system and a Raspberry Pi to record the data obtained. The receiver node records the RSSI at different locations in the greenhouse with the transmitter node and at different heights. In addition, a study of the radio wave attenuation was measured in a tomato greenhouse, and we publish the corresponding obtained dataset in order to share with the research community.Cama-Pinto, Dora-will be generated-orcid-0000-0003-0726-196X-600Holgado-Terriza, Juan Antonio-will be generated-orcid-0000-0002-8031-1276-600Damas, Miguel-will be generated-orcid-0000-0003-2599-8076-600Gómez Mula, Francisco-will be generated-orcid-0000-0002-8953-6826-600Cama-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_abf2Inventionshttps://www.mdpi.com/2411-5134/6/4/66Wireless sensor networksWSNReceived signal strength indicatorRSSIInternet of ThingsIoTFree space pathlossSmart farmingRadio wave attenuation measurement system based on RSSI for precision agriculture: application to tomato greenhousesArtículo 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. Rangwani, D.; Sadhukhan, D.; Ray, S.; Khan, M.K.; Dasgupta, M. An improved privacy preserving remote user authentication scheme for agricultural wireless sensor network. Trans. Emerg. Telecommun. Technol. 2021, 32, e4218. [CrossRef]2. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.; Gómez-Mula, F.; Calderin-Curtidor, A.; Martínez-Lao, J.; Cama-Pinto, A. 5G Mobile Phone Network Introduction in Colombia. Electronics 2021, 10, 922. [CrossRef]3. Mentsiev, A.U.; Gatina, F.F. Data analysis and digitalisation in the agricultural industry. IOP Conf. Series Earth Environ. Sci. 2021, 677, 32101. [CrossRef]4. Azman, A.S.; Lee, M.Y.; Subramaniam, S.K.; Feroz, F.S. Novel Wireless Sensor Network Routing Protocol Performance Evaluation using Diverse Packet Size for Agriculture Application. Int. J. Integr. Eng. 2021, 13, 16–28. [CrossRef]5. Vanishree, K.; Nagaraja, G.S. Emerging Line of Research Approach in Precision Agriculture: An Insight Study. Int. J. Adv. Comput. Sci. Appl. 2021, 12. [CrossRef]6. 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]7. Caicedo-Ortiz, J.G.; De-La-Hoz-Franco, E.; Ortega, R.M.; 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]8. Caicedo Ortiz, J.G.; Acosta Coll, M.A.; Cama-Pinto, A. WSN deployment model for measuring climate variables that cause strong precipitation. Prospectiva 2015, 13, 106–115. [CrossRef]9. Miao, Y.; Zhao, C.; Wu, H. Non-uniform clustering routing protocol of wheat farmland based on effective energy consumption. Int. J. Agric. Biol. Eng. 2021, 14, 142–150. [CrossRef]10. Razafimandimby, C.; Loscri, V.; Vegni, A.M.; Neri, A. Efficient Bayesian Communication Approach for Smart Agriculture Applications. In Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, ON, Canada, 24–27 September 2017; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2017; pp. 1–5.11. Salim, C.; Mitton, N. K-predictions based data reduction approach in WSN for smart agriculture. Computing 2020, 103, 509–532. [CrossRef]12. Wu, H.; Miao, Y.; Li, F.; Zhu, L. Empirical Modeling and Evaluation of Multi-Path Radio Channels on Wheat Farmland Based on Communication Quality. Trans. ASABE 2016, 59, 759–767. [CrossRef]13. 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. [CrossRef]14. 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, 2584–2586.15. Hsiao, S.-J.; Sung, W.-T. A Study on Using a Wireless Sensor Network to Design a Plant Monitoring System. Intell. Autom. Soft Comput. 2021, 27, 359–377. [CrossRef]16. Xuanrong, P.; Tingdong, Y.; Yuesheng, W. Research and design of precision irrigation system based on artificial neural network. In Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2018; pp. 3865–3870.17. 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]18. Zhang, H.; Li, H. Node Localization Technology of Wireless Sensor Network Based on RSSI Algorithm. Int. J. Online Eng. 2016, 12, 51–57. [CrossRef]19. Azmi, N.; Kamarudin, L.; Zakaria, A.; Ndzi, D.; Rahiman, M.; Zakaria, S.; Mohamed, L. RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques. Sensors 2021, 21, 1875. [CrossRef]20. Piñeres-Espitia, G.; Cama-Pinto, A.; De La Rosa Morrón, D.; Estevez, F.; Cama-Pinto, D. Design of a low cost weather station for detecting environmental changes. Espacios 2017, 38, 13.21. Foerster, A.; Udugama, A.; Görg, C.; Kuladinithi, K.; Timm-Giel, A.; Cama-Pinto, A. A Novel Data Dissemination Model for Organic Data Flows. In Proceedings of the International Conference on Mobile Network and Management, Santander, Spain, 16–18 September 2015; Springer: Berlin/Heidelberg, Germany, 2015; pp. 239–252.22. Cama-Pinto, A.; Gil Montoya, F.; Gómez, J.; De La Cruz, J.L.; Manzano-Agugliaro, F. Integration of communication technologies in sensor networks to monitor the Amazon environment. J. Clean. Prod. 2013, 59, 32–42. [CrossRef]23. Farooqui, N.A.; Tyagi, A. Data Mining and Fusion Techniques for Wireless Intelligent Sensor Networks. In Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario’s; Springer: Berlin/Heidelberg, Germany, 2020; pp. 592–615.24. 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]25. Maiolo, L.; Polese, D. Advances in sensing technologies for smart monitoring in precise agriculture. In Proceedings of the SENSORNETS 2021—Proceedings of the 10th International Conference on Sensor Networks, Vienna, Austria, 9–10 February 2021; pp. 151–158.26. Sathish, C.; Srinivasan, K. An artificial bee colony algorithm for efficient optimized data aggregation to agricultural IoT devices application. J. Appl. Sci. Eng. 2021, 24, 927–936. [CrossRef]27. Saiz-Rubio, V.; Rovira-Más, F. From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy 2020, 10, 207. [CrossRef]28. Subashini, M.M.; Das, S.; Heble, S.; Raj, U.; Karthik, R. Internet of Things based wireless plant sensor for smart farming. Indones. J. Electr. Eng. Comput. Sci. 2018, 10, 456–468. [CrossRef]29. Abouzar, P.; Michelson, D.G.; Hamdi, M. RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture. IEEE Trans. Wirel. Commun. 2016, 15, 6638–6650. [CrossRef]30. Xu, L. Design of a RSSI Location System for Greenhouse Environment. Int. J. Distrib. Sens. Netw. 2015, 11, 525861. [CrossRef]31. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Gomez-Mula, F.; Cama-Pinto, A. Desarrollo de un sistema para medición y registro de RSSI en invernaderos. Av. En Arquit. Y Tecnol. De Comput. Actas De Las Jorn. SARTECO 2019, 649–654. [CrossRef]32. Li, T.; Zhang, M.; Ji, Y.H.; Sha, S.; Jiang, Y.Q.; Li, M.Z. Management of CO2 in a tomato greenhouse using WSN and BPNN techniques. Int. J. Agric. Biol. Eng. 2015, 8, 43–51. [CrossRef]33. García, L.; Parra, L.; Jimenez, J.; Parra, M.; Lloret, J.; Mauri, P.; Lorenz, P. Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas. Sensors 2021, 21, 1693. [CrossRef]34. Aung, S.M.Y.; Pattanaik, K.K. Path Loss Measurement for Wireless Communication in Industrial Environments. In Proceedings of the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 13–14 March 2020; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2020; pp. 1–5.35. Navarro, A.; Guevara, D.; Florez, G.A. An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields. In Proceedings of the 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science, Rome, Italy, 29 August–5 September 2020; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2020.36. Pal, P.; Sharma, R.P.; Tripathi, S.; Kumar, C.; Ramesh, D. 2.4 GHz RF Received Signal Strength Based Node Separation in WSN Monitoring Infrastructure for Millet and Rice Vegetation. IEEE Sens. J. 2021, 21, 18298–18306. [CrossRef]37. Wang, J.; Peng, Y.; Li, P. Propagation Characteristics of Radio Wave in Plastic Greenhouse. In Proceedings of the International Conference on Computer and Computing Technologies in Agriculture, Beijing, China, 27–30 September 2015; Springer: Berlin/Heidelberg, Germany, 2016; pp. 208–215.38. Widodo, S.; Pratama, E.A.; Pramono, S.; Basuki, S.B. Outdoor propagation modeling for wireless sensor networks 2.4 GHz. In Proceedings of the 2017 IEEE International Conference on Communication, Networks and Satellite (Comnetsat), Semarang, Indonesia, 5–7 October 2017; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2017; pp. 158–162.39. Cama-Pinto, A.; Espitia, G.D.P.; Caicedo, J.G.; Ramirez-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]40. Shue, S.; Johnson, L.E.; Conrad, J.M. Utilization of XBee ZigBee modules and MATLAB for RSSI localization applications. In Proceedings of the SoutheastCon 2017, Concord, NC, USA, 30 March–2 April 2017; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2017.41. 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] [PubMed]42. Van Herbruggen, B.; Jooris, B.; Rossey, J.; Ridolfi, M.; Macoir, N.; Van Den Brande, Q.; Lemey, S.; De Poorter, E. Wi-pos: A low-cost, open source ultra-wideband (UWB) hardware platform with long range sub-GHZ backbone. Sensors 2019, 19, 1548. [CrossRef]43. Bezunartea, M.; Wang, C.; Braeken, A.; Steenhaut, K. Multi-radio Solution for Improving Reliability in RPL. In Proceedings of the 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Bologna, Italy, 9–12 September 2018; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2018; pp. 129–134.44. Texas Instruments—Descripción CC2538. Available online: http://www.ti.com/product/CC2538/description (accessed on 21 July 2021).45. Gomez, J.; Villar, E.; Molero, G.; Cama, A. Evaluation of high performance clusters in private cloud computing environments. In Distributed Computing and Artificial Intelligence; Springer: Berlin/Heidelberg, Germany, 2012; pp. 305–312.46. ERCIM News. Contiki: Bringing IP to Sensor Networks. Available online: https://ercim-news.ercim.eu/en76/rd/contikibringing-ip-to-sensor-networks (accessed on 21 July 2021).47. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Arrabal-Campos, F.M.; Gómez-Mula, F.; Martínez-Lao, J.A.M.; Cama-Pinto, A. Empirical Model of Radio Wave Propagation in the Presence of Vegetation inside Greenhouses Using Regularized Regressions. Sensors 2020, 20, 6621. [CrossRef] [PubMed]48. Staudemeyer, R.C.; Pöhls, H.C.; Wójcik, M. What it takes to boost Internet of Things privacy beyond encryption with unobservable communication: A survey and lessons learned from the first implementation of DC-net. J. Reliab. Intell. Environ. 2019, 5, 41–64. [CrossRef]49. Dunkels, A.; Gronvall, B.; Voigt, T. Contiki—A lightweight and flexible operating system for tiny networked sensors. In Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks, Tampa, FL, USA, 16–18 November 2004; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2004.50. Dunkels, A.; Österlind, F.; He, Z. An adaptive communication architecture for wireless sensor networks. In Proceedings of the SenSys’07—Proceedings of the 5th ACM Conference on Embedded Networked Sensor Systems, Sydney, Australia, 6–9 November 2007; Machinery: New York, NY, USA, 2007; pp. 335–349.51. Vougioukas, S.; Anastassiu, H.; 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]52. 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]53. Cama-Pinto, D.; Holgado-Terriza, J.A.; Damas, M.; Gómez-Mula, F.; Cama-Pinto, A. Tomato Greenhouse Measurement of RSSI in Almeria Spain. Available online: https://data.mendeley.com/datasets/nhk3gs7gmm/1 (accessed on 8 September 2021).54. Zennaro, M.; Bagula, A.; Gascon, D.; Noveleta, A.B. Long distance wireless sensor networks: Simulation vs. reality. In Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions, NSDR’10, San Francisco, CA, USA, 15 June 2010.PublicationORIGINALRadio wave attenuation measurement system based on RSSI for precision agriculture application to tomato greenhouses.pdfRadio wave attenuation measurement system based on RSSI for precision agriculture application to tomato greenhouses.pdfapplication/pdf3079447https://repositorio.cuc.edu.co/bitstreams/dc5c5f70-e70d-4d2b-a87b-b4c00e34ad96/downloadb2eff191132cc58ae7d87c48ea7ba1dcMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/66a280bd-4c12-4781-9004-93b66114ded6/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/38a6a926-b322-477f-8af0-a30565bec767/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILRadio wave attenuation measurement system based on RSSI for precision agriculture application to tomato greenhouses.pdf.jpgRadio wave attenuation measurement system based on RSSI for precision agriculture application to tomato greenhouses.pdf.jpgimage/jpeg76315https://repositorio.cuc.edu.co/bitstreams/58a18a89-c321-4e12-aa6e-ea17292e9b18/download3a195e0670796da589984017532538d6MD54TEXTRadio wave attenuation measurement system based on RSSI for precision agriculture application to tomato greenhouses.pdf.txtRadio wave attenuation measurement system based on RSSI for precision agriculture application to tomato greenhouses.pdf.txttext/plain60099https://repositorio.cuc.edu.co/bitstreams/d5b1c8bf-753b-412e-ae38-f35a09e4ef8a/download0d9f5e87b7a2b270558cdbbf297fe085MD5511323/8854oai:repositorio.cuc.edu.co:11323/88542024-09-17 10:54:58.263http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares