Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules
Today, through the monitoring of agronomic variables, the wireless sensor networks are playing an increasingly important role in precision agriculture. Among the emerging technologies used to develop prototypes related to wireless sensor network, we find the Arduino platform and XBee radio modules f...
- Autores:
-
Cama Pinto, Alejandro
Piñeres Espitia, Gabriel Dario
Caicedo Ortiz, Jose Gregorio
Ramirez Cerpa, Elkin Duvan
Betancur Agudelo, Leonardo
Gómez Mula, Francisco
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/1789
- Acceso en línea:
- https://hdl.handle.net/11323/1789
https://repositorio.cuc.edu.co/
- Palabra clave:
- packet loss
radio propagation model
received strength signal intensity level
wireless sensor network
XBee
- Rights
- openAccess
- License
- Atribución – No comercial – Compartir igual
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Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules |
| title |
Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules |
| spellingShingle |
Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules packet loss radio propagation model received strength signal intensity level wireless sensor network XBee |
| title_short |
Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules |
| title_full |
Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules |
| title_fullStr |
Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules |
| title_full_unstemmed |
Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules |
| title_sort |
Received strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modules |
| dc.creator.fl_str_mv |
Cama Pinto, Alejandro Piñeres Espitia, Gabriel Dario Caicedo Ortiz, Jose Gregorio Ramirez Cerpa, Elkin Duvan Betancur Agudelo, Leonardo Gómez Mula, Francisco |
| dc.contributor.author.spa.fl_str_mv |
Cama Pinto, Alejandro Piñeres Espitia, Gabriel Dario Caicedo Ortiz, Jose Gregorio Ramirez Cerpa, Elkin Duvan Betancur Agudelo, Leonardo Gómez Mula, Francisco |
| dc.subject.eng.fl_str_mv |
packet loss radio propagation model received strength signal intensity level wireless sensor network XBee |
| topic |
packet loss radio propagation model received strength signal intensity level wireless sensor network XBee |
| description |
Today, through the monitoring of agronomic variables, the wireless sensor networks are playing an increasingly important role in precision agriculture. Among the emerging technologies used to develop prototypes related to wireless sensor network, we find the Arduino platform and XBee radio modules from the DIGI Company. In this article, based on field tests, we conducted a comparative analysis of received strength signal intensity levels, calculation of path loss with “log-normal shadowing” and free-space path loss models. In addition, we measure packet loss for different transmission, distances and environments with respect to an “Arduino Mega” board, and radio modules XBee PRO S1 and XBee Pro S2. The tests for the packet loss and received strength signal intensity level show the best performance for the XBee Pro S2 in the indoor, outdoor, and rural scenarios. |
| publishDate |
2017 |
| dc.date.issued.none.fl_str_mv |
2017-07-02 |
| dc.date.accessioned.none.fl_str_mv |
2018-11-23T22:02:51Z |
| dc.date.available.none.fl_str_mv |
2018-11-23T22:02:51Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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15501329 |
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https://hdl.handle.net/11323/1789 |
| dc.identifier.instname.spa.fl_str_mv |
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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eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
1. Srbinovska M, Gavrovski C, Dimcev V, et al. Environmental parameters monitoring in precision agriculture using wireless sensor networks. J Clean Prod 2015; 88: 297–307.2. Montoya FG, Go´mez J, Cama A, et al. A monitoring system for intensive agriculture based on mesh networks and the android system. Comput Electron Agr 2013; 99: 14–20. 3. Cama-Pinto A, Gil-Montoya F, Go´mez-Lo´pez J, et al. Wireless surveillance system for greenhouse crops (Dyna. 81,184,164.), Revista DYNA, 2014, http://www.scielo.org.co/pdf/dyna/v81n184/v81n184a22.pdf 4. Urbano-molano Aparicio F. Redes de Sensores Inala´mbricos Aplicadas a Optimizacio´n en Agricultura de Precisio´n para Cultivos de Cafe´ en Colombia. J Cienc Ing 2012; 5(1): 46–52. 5. Xiao L and Guo L. The realization of precision agriculture monitoring system based on wireless sensor network. In: Proceedings of the international conference on computer and communication technologies in agriculture engineering (CCTAE’10), Chengdu, China, 12–13 June 2010, pp.89–92. New York: IEEE. 6. Ministerio de Educacio´n, 2009, http://www.mineducacion.gov.co/cvn/1665/w3-article-200749.html (accessed 28 August 2016). 7. Cirstea C, Cernaianu M and Gontean A. Packet loss analysis in wireless sensor networks routing protocols. In: Proceedings of the 2012 35th international conference on telecommunications and signal processing (TSP), Prague, 3–4 July 2012, pp.37–41. New York: IEEE. 8. Bas CU and Ergen SC. Spatio-temporal characteristics of link quality in wireless sensor networks. In: Proceedings of the 2010 IEEE wireless communications and networking conference (WCNC), Shanghai, China, 1–4 April 2012, pp.1152–1157. New York: IEEE. 9. Goldsmith A. Wireless communications. New York: Cambridge University Press, 2005. 10. Al-Busaidi AM. Development of an educational environment for online control of a biped robot using MATLAB and Arduino. In: Proceedings of the 2012 9th FranceJapan & 7th Europe-Asia congress on mechatronics (MECATRONICS)/13th int’l workshop on research and education in mechatronics (REM), Paris, France, 21–23 November 2012, pp.337–344. New York: IEEE. 11. Al-kadi T, Al-tuwaijri Z and Al-omran A. Arduino Wi-Fi network analyzer. Proced Comput Sci 2013; 21: 522–529. 12. Kornuta JA, Nipper ME and Dixon JB. Low-cost microcontroller platform for studying lymphatic biomechanics in vitro. J Biom 2013; 46(1): 183–186. 13. Xu J, Liu W, Lang F, et al. Distance measurement model based on RSSI in WSN. Wirel Sens Netw 2010; 2(8): 606–611. 14. Lee J-H, Choi J, Lee W-H, et al. Measurement and analysis on land-to-ship offshore wireless channel in 2.4 GHz. IEEE Wirel Commun Lett 2017; 6(2): 222–225. 15. Biaou U, Sadoudi L, Bocquet M, et al. Modeling of ZigBee (IEEE 802.15.4) channel in rail environment for intelligent transport. In: Proceedings of the 2015 4th IEEE international conference on advanced logistics and transport, IEEE ICALT 2015, art. no. 7136637, 2015, pp.293– 298. 16. Seybold J. Introduction to RF propagation. Hoboken, NJ: Wiley Interscience, 2005. 17. Chrysikos T, Georgopoulos G and Kotsopoulos S. Wireless channel characterization for a home indoor propagation topology at 2.4 GHz. Proceedings of the wireless telecommunications symposium, New York, 13– 15 April 2011, article no. 5960879. New York: IEEE. 18. Mahalin NH, Sharifah HS, Syed Yusof SK, et al. RSSI measurements for enabling IEEE 802.15.4 coexistence with IEEE 802.11b/g. In: Proceedings of the IEEE region 10 annual international conference on TENCON, Singapore, 23–26 January 2009, pp.1–4. New York: IEEE. 19. Hamida E, Ben Lyon I, Chelius G, et al. Investigating the impact of human activity on the performance of wireless networks—an experimental approach. In: Proceedings of the 2010 IEEE international symposium on a world of wireless mobile and multimedia networks (WoWMoM), Montreal, QC, Canada, 14–17 June 2010. New York: IEEE. 20. Pellegrini RM, Persia S, Volponi D, et al. 2011; RF propagation analysis for ZigBee Sensor Network using RSSI measurements. In: Proceedings of the 2011 2nd international conference on wireless communication, vehicular technology, information theory and aerospace & electronic systems technology (Wireless VITAE), Chennai, India, 28 Febraury–3 March 2011, pp.1–5. New York: IEEE. 21. Harun A, Ramli MF, Kamarudin LM, et al. Comparative performance analysis of wireless RSSI in wireless sensor networks motes in tropical mixed-crop precision farm. In: Proceedings of the 2012 third international conference on intelligent systems modelling and simulation, Kota Kinabalu, Malaysia, 8–10 February 2012, pp.606– 610. New York: IEEE. 22. Kodali RK, Rawat N and Boppana L. WSN sensors for precision agriculture. In: Proceedings of the region 10 symposium, Kuala Lumpur, Malaysia, 14–16 April 2014, pp.643–648. New York: IEEE. 23. Howell B, Anderson E and Flores A. A low cost wireless sensor network for landslide hazard monitoring. In Proceedings of the geoscience and remote sensing symposium (IGARSS), Munich, 22–27 July 2012, pp.4793–4796. New York: IEEE. 24. Zamora R. Ana´lisis de requerimiento para la implementacio´n de Laboratorios Remotos. Barranquilla, Colombia: Educosta, 2011. 25. Mahmoud KH. Data collection and processing from distributed system of wireless sensors. Master Thesis, Masaryk University, Brno, 2013. 26. Caicedo-Ortiz J, Coll MAA and Cama-Pinto A. Modelo de despliegue de una WSN para la medicio´n de las variables clima´ticas que causan fuertes precipitaciones. WSN deployment model for measuring climate variables that cause strong precipitation, pp.106–115, http://www.scielo.org.co/pdf/prosp/v13n1/v13n1a11.pdf 27. Libelium, http://www.libelium.com/products/waspmote 28. Faludi R. Building wireless sensor networks. 4th ed. USA: Brian Jepson, 2012, p.32. 29. Arnil J, Punsawad Y and Wongsawat Y. Wireless sensor network-based smart room system for healthcare monitoring. In: Proceedings of the 2011 IEEE international conference on robotics and biomimetics (ROBIO), Karon Beach, Phuket, Thailand, 7–11 December 2011, pp.2073– 2076. New York: IEEE. 30. Benkic K, Malajner M, Planinsic P, et al. Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. In: Proceedings of the 15th international conference on systems, signals and image processing, Bratislava, 25–28 June 2008, pp.303–306. New York: IEEE. 31. Chrysikos T and Kotsopoulos S. Characterization of large-scale fading for the 2.4 GHz channel in obstacledense indoor propagation topologies. Proceedings of the 2012 IEEE vehicular technology conference, Quebec City, QC, Canada, 3–6 September 2012, article no. 6399239. New York: IEEE. 32. Kurt S and Tavli B. Path-loss modeling for wireless sensor networks: a review of models and comparative evaluations. IEEE Antenn Propag M 2016; 59(1): 18–37. 33. Farahani S. ZigBee wireless networks and transceivers. 1st ed. Oxford: Elsevier Ltd., 2008, p.173. |
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Cama Pinto, AlejandroPiñeres Espitia, Gabriel DarioCaicedo Ortiz, Jose GregorioRamirez Cerpa, Elkin DuvanBetancur Agudelo, LeonardoGómez Mula, Francisco2018-11-23T22:02:51Z2018-11-23T22:02:51Z2017-07-0215501329https://hdl.handle.net/11323/1789Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Today, through the monitoring of agronomic variables, the wireless sensor networks are playing an increasingly important role in precision agriculture. Among the emerging technologies used to develop prototypes related to wireless sensor network, we find the Arduino platform and XBee radio modules from the DIGI Company. In this article, based on field tests, we conducted a comparative analysis of received strength signal intensity levels, calculation of path loss with “log-normal shadowing” and free-space path loss models. In addition, we measure packet loss for different transmission, distances and environments with respect to an “Arduino Mega” board, and radio modules XBee PRO S1 and XBee Pro S2. The tests for the packet loss and received strength signal intensity level show the best performance for the XBee Pro S2 in the indoor, outdoor, and rural scenarios.Cama Pinto, Alejandro-0000-0002-1364-7394-600Piñeres Espitia, Gabriel Dario-0000-0002-8165-2697-600Caicedo Ortiz, Jose Gregorio-0000-0003-2832-8625-600Ramirez Cerpa, Elkin Duvan-0000-0002-6634-5170-600Betancur Agudelo, Leonardo-4afc18b7-63c8-436a-8669-002b99a03c0c-0Gómez Mula, Francisco-9f826790-d8ff-432e-b8ff-a261a7f6885b-0engInternational Journal of Distributed Sensor NetworksAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2packet lossradio propagation modelreceived strength signal intensity levelwireless sensor networkXBeeReceived strength signal intensity performance analysis in wireless sensor network using Arduino platform and XBee wireless modulesArtículo 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. Srbinovska M, Gavrovski C, Dimcev V, et al. Environmental parameters monitoring in precision agriculture using wireless sensor networks. J Clean Prod 2015; 88: 297–307.2. Montoya FG, Go´mez J, Cama A, et al. A monitoring system for intensive agriculture based on mesh networks and the android system. Comput Electron Agr 2013; 99: 14–20. 3. Cama-Pinto A, Gil-Montoya F, Go´mez-Lo´pez J, et al. Wireless surveillance system for greenhouse crops (Dyna. 81,184,164.), Revista DYNA, 2014, http://www.scielo.org.co/pdf/dyna/v81n184/v81n184a22.pdf 4. Urbano-molano Aparicio F. Redes de Sensores Inala´mbricos Aplicadas a Optimizacio´n en Agricultura de Precisio´n para Cultivos de Cafe´ en Colombia. J Cienc Ing 2012; 5(1): 46–52. 5. Xiao L and Guo L. The realization of precision agriculture monitoring system based on wireless sensor network. In: Proceedings of the international conference on computer and communication technologies in agriculture engineering (CCTAE’10), Chengdu, China, 12–13 June 2010, pp.89–92. New York: IEEE. 6. Ministerio de Educacio´n, 2009, http://www.mineducacion.gov.co/cvn/1665/w3-article-200749.html (accessed 28 August 2016). 7. Cirstea C, Cernaianu M and Gontean A. Packet loss analysis in wireless sensor networks routing protocols. In: Proceedings of the 2012 35th international conference on telecommunications and signal processing (TSP), Prague, 3–4 July 2012, pp.37–41. New York: IEEE. 8. Bas CU and Ergen SC. Spatio-temporal characteristics of link quality in wireless sensor networks. In: Proceedings of the 2010 IEEE wireless communications and networking conference (WCNC), Shanghai, China, 1–4 April 2012, pp.1152–1157. New York: IEEE. 9. Goldsmith A. Wireless communications. New York: Cambridge University Press, 2005. 10. Al-Busaidi AM. Development of an educational environment for online control of a biped robot using MATLAB and Arduino. In: Proceedings of the 2012 9th FranceJapan & 7th Europe-Asia congress on mechatronics (MECATRONICS)/13th int’l workshop on research and education in mechatronics (REM), Paris, France, 21–23 November 2012, pp.337–344. New York: IEEE. 11. Al-kadi T, Al-tuwaijri Z and Al-omran A. Arduino Wi-Fi network analyzer. Proced Comput Sci 2013; 21: 522–529. 12. Kornuta JA, Nipper ME and Dixon JB. Low-cost microcontroller platform for studying lymphatic biomechanics in vitro. J Biom 2013; 46(1): 183–186. 13. Xu J, Liu W, Lang F, et al. Distance measurement model based on RSSI in WSN. Wirel Sens Netw 2010; 2(8): 606–611. 14. Lee J-H, Choi J, Lee W-H, et al. Measurement and analysis on land-to-ship offshore wireless channel in 2.4 GHz. IEEE Wirel Commun Lett 2017; 6(2): 222–225. 15. Biaou U, Sadoudi L, Bocquet M, et al. Modeling of ZigBee (IEEE 802.15.4) channel in rail environment for intelligent transport. In: Proceedings of the 2015 4th IEEE international conference on advanced logistics and transport, IEEE ICALT 2015, art. no. 7136637, 2015, pp.293– 298. 16. Seybold J. Introduction to RF propagation. Hoboken, NJ: Wiley Interscience, 2005. 17. Chrysikos T, Georgopoulos G and Kotsopoulos S. Wireless channel characterization for a home indoor propagation topology at 2.4 GHz. Proceedings of the wireless telecommunications symposium, New York, 13– 15 April 2011, article no. 5960879. New York: IEEE. 18. Mahalin NH, Sharifah HS, Syed Yusof SK, et al. RSSI measurements for enabling IEEE 802.15.4 coexistence with IEEE 802.11b/g. In: Proceedings of the IEEE region 10 annual international conference on TENCON, Singapore, 23–26 January 2009, pp.1–4. New York: IEEE. 19. Hamida E, Ben Lyon I, Chelius G, et al. Investigating the impact of human activity on the performance of wireless networks—an experimental approach. In: Proceedings of the 2010 IEEE international symposium on a world of wireless mobile and multimedia networks (WoWMoM), Montreal, QC, Canada, 14–17 June 2010. New York: IEEE. 20. Pellegrini RM, Persia S, Volponi D, et al. 2011; RF propagation analysis for ZigBee Sensor Network using RSSI measurements. In: Proceedings of the 2011 2nd international conference on wireless communication, vehicular technology, information theory and aerospace & electronic systems technology (Wireless VITAE), Chennai, India, 28 Febraury–3 March 2011, pp.1–5. New York: IEEE. 21. Harun A, Ramli MF, Kamarudin LM, et al. Comparative performance analysis of wireless RSSI in wireless sensor networks motes in tropical mixed-crop precision farm. In: Proceedings of the 2012 third international conference on intelligent systems modelling and simulation, Kota Kinabalu, Malaysia, 8–10 February 2012, pp.606– 610. New York: IEEE. 22. Kodali RK, Rawat N and Boppana L. WSN sensors for precision agriculture. In: Proceedings of the region 10 symposium, Kuala Lumpur, Malaysia, 14–16 April 2014, pp.643–648. New York: IEEE. 23. Howell B, Anderson E and Flores A. A low cost wireless sensor network for landslide hazard monitoring. In Proceedings of the geoscience and remote sensing symposium (IGARSS), Munich, 22–27 July 2012, pp.4793–4796. New York: IEEE. 24. Zamora R. Ana´lisis de requerimiento para la implementacio´n de Laboratorios Remotos. Barranquilla, Colombia: Educosta, 2011. 25. Mahmoud KH. Data collection and processing from distributed system of wireless sensors. Master Thesis, Masaryk University, Brno, 2013. 26. Caicedo-Ortiz J, Coll MAA and Cama-Pinto A. Modelo de despliegue de una WSN para la medicio´n de las variables clima´ticas que causan fuertes precipitaciones. WSN deployment model for measuring climate variables that cause strong precipitation, pp.106–115, http://www.scielo.org.co/pdf/prosp/v13n1/v13n1a11.pdf 27. Libelium, http://www.libelium.com/products/waspmote 28. Faludi R. Building wireless sensor networks. 4th ed. USA: Brian Jepson, 2012, p.32. 29. Arnil J, Punsawad Y and Wongsawat Y. Wireless sensor network-based smart room system for healthcare monitoring. In: Proceedings of the 2011 IEEE international conference on robotics and biomimetics (ROBIO), Karon Beach, Phuket, Thailand, 7–11 December 2011, pp.2073– 2076. New York: IEEE. 30. Benkic K, Malajner M, Planinsic P, et al. Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. In: Proceedings of the 15th international conference on systems, signals and image processing, Bratislava, 25–28 June 2008, pp.303–306. New York: IEEE. 31. Chrysikos T and Kotsopoulos S. Characterization of large-scale fading for the 2.4 GHz channel in obstacledense indoor propagation topologies. Proceedings of the 2012 IEEE vehicular technology conference, Quebec City, QC, Canada, 3–6 September 2012, article no. 6399239. New York: IEEE. 32. Kurt S and Tavli B. Path-loss modeling for wireless sensor networks: a review of models and comparative evaluations. IEEE Antenn Propag M 2016; 59(1): 18–37. 33. Farahani S. ZigBee wireless networks and transceivers. 1st ed. Oxford: Elsevier Ltd., 2008, p.173.PublicationORIGINALReceived strength signal intensity performance analysis in wireless sensor network.pdfReceived strength signal intensity performance analysis in wireless sensor network.pdfapplication/pdf1844721https://repositorio.cuc.edu.co/bitstreams/056a74b2-fd8c-42df-a33e-44b8456389ea/downloadbb0f96343169a7dd9132a6d5a91271c6MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/d5d187a0-6e06-48ec-b248-87719b752bb6/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILReceived strength signal intensity performance analysis in wireless sensor network.pdf.jpgReceived strength signal intensity performance analysis in wireless sensor network.pdf.jpgimage/jpeg63536https://repositorio.cuc.edu.co/bitstreams/c7579227-3d0c-40cf-8725-c5a6a4c147fe/download31f04aa3f6d3bc3ed7525c11d4b8df4dMD54TEXTReceived strength signal intensity performance analysis in wireless sensor network.pdf.txtReceived strength signal intensity performance analysis in wireless sensor network.pdf.txttext/plain28451https://repositorio.cuc.edu.co/bitstreams/cf1dad5e-47e7-4dca-b547-01d8e3cc501d/downloada93228eac60258482c999342c88b20d6MD5511323/1789oai:repositorio.cuc.edu.co:11323/17892024-09-17 14:13:54.58open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |