Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]

The Internet of Things (IoT) is an enabling paradigm for Industry 4.0, where sensors and actuators connect to the Internet. The protocol LoRaWAN (Long Range Area Network) is one of the most used in the IoT, and its primary objective is to transmit sensor information over long distances with minimal...

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Fecha de publicación:: 2020

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: spa

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network_acronym_str	REPOUDEM2
network_name_str	Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]
title	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]
spellingShingle	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina] Energy consumption Industry 4.0 Internet of Things LoRaWAN Machine Learning
title_short	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]
title_full	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]
title_fullStr	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]
title_full_unstemmed	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]
title_sort	Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]
dc.subject.spa.fl_str_mv	Energy consumption Industry 4.0 Internet of Things LoRaWAN Machine Learning
topic	Energy consumption Industry 4.0 Internet of Things LoRaWAN Machine Learning
description	The Internet of Things (IoT) is an enabling paradigm for Industry 4.0, where sensors and actuators connect to the Internet. The protocol LoRaWAN (Long Range Area Network) is one of the most used in the IoT, and its primary objective is to transmit sensor information over long distances with minimal energy consumption. This protocol implements Adaptive Data Rate scheme to optimize the energy consumed per node, which, when evaluated through exhaustive simulations in Omnet ++, has exhibited opportunities for improvement in convergence time. The present work shows machine learning models based on parametric and non-parametric methods based on Support Vector Machines (SVM) and Artificial Neural Networks (ANN). The results indicate that the SVM and ANN methods have a success rate greater than 90% in the estimated parameters. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2021-02-05T14:58:20Z
dc.date.available.none.fl_str_mv	2021-02-05T14:58:20Z
dc.date.none.fl_str_mv	2020
dc.type.eng.fl_str_mv	Article
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.identifier.issn.none.fl_str_mv	16469895
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/5968
dc.identifier.doi.none.fl_str_mv	10.17013/risti.39.67-83
identifier_str_mv	16469895 10.17013/risti.39.67-83
url	http://hdl.handle.net/11407/5968
dc.language.iso.none.fl_str_mv	spa
language	spa
dc.relation.isversionof.none.fl_str_mv	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096957529&doi=10.17013%2fristi.39.67-83&partnerID=40&md5=c79d738b60d7ba057aa7880318f398eb
dc.relation.citationvolume.none.fl_str_mv	2020
dc.relation.citationissue.none.fl_str_mv	39
dc.relation.citationstartpage.none.fl_str_mv	67
dc.relation.citationendpage.none.fl_str_mv	83
dc.relation.references.none.fl_str_mv	Abdelfadeel, K.Q., Cionca, V., Pesch, D., Fair adaptive data rate allocation and power control in lorawan (2018) 2018 IEEE 19Th International Symposium on” a World of Wireless, Mobile and Multimedia Networks”(WoWMoM)., , Paper presented at the Adelantado, F., Vilajosana, X., Tuset-Peiro, P., Martinez, B., Melia-Segui, J., Watteyne, T., Understanding the limits of LoRaWAN (2017) IEEE Communications Magazine, 55 (9), pp. 34-40 Bouguera, T., Diouris, J.-F., Chaillout, J.-J., Jaouadi, R., Andrieux, G., Energy consumption model for sensor nodes based on LoRa and LoRaWAN (2018) Sensors, 18 (7), p. 2104 Chen, M., Mao, S., Liu, Y., Big data: A survey (2014) Mobile Networks and Applications, 19 (2), pp. 171-209 de Carvalho-Silva, J., Rodrigues, J.J., Alberti, A.M., Solic, P., Aquino, A.L., LoRaWAN—A low power WAN protocol for Internet of Things: A review and opportunities (2017) Proceedings of the 2017 2Nd International Multidisciplinary Conference on Computer and Energy Science, , SpliTech Gao, W., Du, W., Zhao, Z., Min, G., Singhal, M., Towards Energy-Fairness in LoRa Networks (2019) Proceedings of the 2019 IEEE 39Th International Conference on Distributed Computing Systems (ICDCS) Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M., Industry 4.0 (2014) Business & Information Systems Engineering, 6 (4), pp. 239-242 Lavric, A., Petrariu, A.I., Popa, V., Long range sigfox communication protocol scalability analysis under large-scale, high-density conditions (2019) IEEE Access, 7, pp. 35816-35825 Lee, J., Davari, H., Singh, J., Pandhare, V., Industrial Artificial Intelligence for industry 4.0-based manufacturing systems (2018) Manufacturing Letters, 18, pp. 20-23 Li, S., Raza, U., Khan, A., How Agile is the Adaptive Data Rate Mechanism of LoRaWAN? (2018) Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM) Noble, W.S., What is a support vector machine? (2006) Nature Biotechnology, 24 (12), pp. 1565-1567 Petrasch, R., Hentschke, R., Process modeling for Industry 4.0 applications: Towards an Industry 4.0 process modeling language and method (2016) Proceedings of the 2016 13Th International Joint Conference on Computer Science and Software Engineering (JCSSE) Ratasuk, R., Vejlgaard, B., Mangalvedhe, N., Ghosh, A., NB-IoT system for M2M communication (2016) Proceedings of the The 2016 IEEE Wireless Communications and Networking Conference Reynders, B., Meert, W., Pollin, S., Power and spreading factor control in low power wide area networks (2017) Proceedings of the 2017 IEEE International Conference on Communications (ICC) Reynders, B., Pollin, S., Chirp spread spectrum as a modulation technique for long range communication (2016) Paper Presented at the 2016 Symposium on Communications and Vehicular Technologies (SCVT). San Cheong, P., Bergs, J., Hawinkel, C., Famaey, J., Comparison of LoRaWAN classes and their power consumption (2017) Paper Presented at the 2017 IEEE Symposium on Communications and Vehicular Technology (SCVT). Sandoval, R.M., Garcia-Sanchez, A.-J., Garcia-Haro, J., Performance optimization of LoRa nodes for the future smart city/industry (2019) EURASIP Journal on Wireless Communications and Networking, 2019 (1), pp. 1-13 Sandoval, R.M., Rodenas-Herraiz, D., Garcia-Sanchez, A.-J., Garcia-Haro, J., Deriving and Updating Optimal Transmission Configurations for Lora Networks (2020) IEEE Access, 8, pp. 38586-38595 Schwab, K., (2017) The Fourth Industrial Revolution, , Penguin Random House Grupo Editorial España, 2016 Slabicki, M., Premsankar, G., Di Francesco, M., Adaptive configuration of LoRa networks for dense IoT deployments (2018) Paper Presented at the NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium. Sornin, N., Luis, M., Eirich, T., Kramp, T., Hersent, O., (2015) Lorawan Specification. Lora Alliance, , https://www.lora-alliance.org Wollschlaeger, M., Sauter, T., Jasperneite, J., The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0 (2017) IEEE Industrial Electronics Magazine, 11 (1), pp. 17-27
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv	http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv	Associacao Iberica de Sistemas e Tecnologias de Informacao
dc.publisher.program.spa.fl_str_mv	Ingeniería de Telecomunicaciones
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingenierías
publisher.none.fl_str_mv	Associacao Iberica de Sistemas e Tecnologias de Informacao
dc.source.none.fl_str_mv	RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
institution	Universidad de Medellín
repository.name.fl_str_mv	Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv	repositorio@udem.edu.co
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spelling	20202021-02-05T14:58:20Z2021-02-05T14:58:20Z16469895http://hdl.handle.net/11407/596810.17013/risti.39.67-83The Internet of Things (IoT) is an enabling paradigm for Industry 4.0, where sensors and actuators connect to the Internet. The protocol LoRaWAN (Long Range Area Network) is one of the most used in the IoT, and its primary objective is to transmit sensor information over long distances with minimal energy consumption. This protocol implements Adaptive Data Rate scheme to optimize the energy consumed per node, which, when evaluated through exhaustive simulations in Omnet ++, has exhibited opportunities for improvement in convergence time. The present work shows machine learning models based on parametric and non-parametric methods based on Support Vector Machines (SVM) and Artificial Neural Networks (ANN). The results indicate that the SVM and ANN methods have a success rate greater than 90% in the estimated parameters. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.spaAssociacao Iberica de Sistemas e Tecnologias de InformacaoIngeniería de TelecomunicacionesFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85096957529&doi=10.17013%2fristi.39.67-83&partnerID=40&md5=c79d738b60d7ba057aa7880318f398eb2020396783Abdelfadeel, K.Q., Cionca, V., Pesch, D., Fair adaptive data rate allocation and power control in lorawan (2018) 2018 IEEE 19Th International Symposium on” a World of Wireless, Mobile and Multimedia Networks”(WoWMoM)., , Paper presented at theAdelantado, F., Vilajosana, X., Tuset-Peiro, P., Martinez, B., Melia-Segui, J., Watteyne, T., Understanding the limits of LoRaWAN (2017) IEEE Communications Magazine, 55 (9), pp. 34-40Bouguera, T., Diouris, J.-F., Chaillout, J.-J., Jaouadi, R., Andrieux, G., Energy consumption model for sensor nodes based on LoRa and LoRaWAN (2018) Sensors, 18 (7), p. 2104Chen, M., Mao, S., Liu, Y., Big data: A survey (2014) Mobile Networks and Applications, 19 (2), pp. 171-209de Carvalho-Silva, J., Rodrigues, J.J., Alberti, A.M., Solic, P., Aquino, A.L., LoRaWAN—A low power WAN protocol for Internet of Things: A review and opportunities (2017) Proceedings of the 2017 2Nd International Multidisciplinary Conference on Computer and Energy Science, , SpliTechGao, W., Du, W., Zhao, Z., Min, G., Singhal, M., Towards Energy-Fairness in LoRa Networks (2019) Proceedings of the 2019 IEEE 39Th International Conference on Distributed Computing Systems (ICDCS)Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M., Industry 4.0 (2014) Business & Information Systems Engineering, 6 (4), pp. 239-242Lavric, A., Petrariu, A.I., Popa, V., Long range sigfox communication protocol scalability analysis under large-scale, high-density conditions (2019) IEEE Access, 7, pp. 35816-35825Lee, J., Davari, H., Singh, J., Pandhare, V., Industrial Artificial Intelligence for industry 4.0-based manufacturing systems (2018) Manufacturing Letters, 18, pp. 20-23Li, S., Raza, U., Khan, A., How Agile is the Adaptive Data Rate Mechanism of LoRaWAN? (2018) Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM)Noble, W.S., What is a support vector machine? (2006) Nature Biotechnology, 24 (12), pp. 1565-1567Petrasch, R., Hentschke, R., Process modeling for Industry 4.0 applications: Towards an Industry 4.0 process modeling language and method (2016) Proceedings of the 2016 13Th International Joint Conference on Computer Science and Software Engineering (JCSSE)Ratasuk, R., Vejlgaard, B., Mangalvedhe, N., Ghosh, A., NB-IoT system for M2M communication (2016) Proceedings of the The 2016 IEEE Wireless Communications and Networking ConferenceReynders, B., Meert, W., Pollin, S., Power and spreading factor control in low power wide area networks (2017) Proceedings of the 2017 IEEE International Conference on Communications (ICC)Reynders, B., Pollin, S., Chirp spread spectrum as a modulation technique for long range communication (2016) Paper Presented at the 2016 Symposium on Communications and Vehicular Technologies (SCVT).San Cheong, P., Bergs, J., Hawinkel, C., Famaey, J., Comparison of LoRaWAN classes and their power consumption (2017) Paper Presented at the 2017 IEEE Symposium on Communications and Vehicular Technology (SCVT).Sandoval, R.M., Garcia-Sanchez, A.-J., Garcia-Haro, J., Performance optimization of LoRa nodes for the future smart city/industry (2019) EURASIP Journal on Wireless Communications and Networking, 2019 (1), pp. 1-13Sandoval, R.M., Rodenas-Herraiz, D., Garcia-Sanchez, A.-J., Garcia-Haro, J., Deriving and Updating Optimal Transmission Configurations for Lora Networks (2020) IEEE Access, 8, pp. 38586-38595Schwab, K., (2017) The Fourth Industrial Revolution, , Penguin Random House Grupo Editorial España, 2016Slabicki, M., Premsankar, G., Di Francesco, M., Adaptive configuration of LoRa networks for dense IoT deployments (2018) Paper Presented at the NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium.Sornin, N., Luis, M., Eirich, T., Kramp, T., Hersent, O., (2015) Lorawan Specification. Lora Alliance, , https://www.lora-alliance.orgWollschlaeger, M., Sauter, T., Jasperneite, J., The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0 (2017) IEEE Industrial Electronics Magazine, 11 (1), pp. 17-27RISTI - Revista Iberica de Sistemas e Tecnologias de InformacaoEnergy consumptionIndustry 4.0Internet of ThingsLoRaWANMachine LearningImprovement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]Articleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1González-Palacio, M., Ingeniería de Telecomunicaciones, Universidad de Medellín, Medellín, 050082, ColombiaSepúlveda-Cano, L.M., Ingeniería de Sistemas, Universidad de Medellín, Medellín, 050082, ColombiaQuiza-Montealegre, J., Ingeniería de Telecomunicaciones, Universidad de Medellín, Medellín, 050082, ColombiaD’amato, J., Instituto Pladema, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, B7001BBO, Argentinahttp://purl.org/coar/access_right/c_16ecGonzález-Palacio M.Sepúlveda-Cano L.M.Quiza-Montealegre J.D’amato J.11407/5968oai:repository.udem.edu.co:11407/59682021-02-05 09:58:20.874Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

Improvement of the algorithm ADR in an internet of things network LoRaWAN by using machine learning [Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de máquina]

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