Show simple item record

dc.creatorGonzález-Palacio M.
dc.creatorSepúlveda-Cano L.M.
dc.creatorQuiza-Montealegre J.
dc.creatorD’amato J.
dc.descriptionThe 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.
dc.publisherAssociacao Iberica de Sistemas e Tecnologias de Informacao
dc.sourceRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
dc.subjectEnergy consumptionspa
dc.subjectIndustry 4.0spa
dc.subjectInternet of Thingsspa
dc.subjectMachine Learningspa
dc.titleImprovement 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.publisher.programIngeniería de Telecomunicacionesspa
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationGonzález-Palacio, M., Ingeniería de Telecomunicaciones, Universidad de Medellín, Medellín, 050082, Colombia
dc.affiliationSepúlveda-Cano, L.M., Ingeniería de Sistemas, Universidad de Medellín, Medellín, 050082, Colombia
dc.affiliationQuiza-Montealegre, J., Ingeniería de Telecomunicaciones, Universidad de Medellín, Medellín, 050082, Colombia
dc.affiliationD’amato, J., Instituto Pladema, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, B7001BBO, Argentina
dc.relation.referencesAbdelfadeel, 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
dc.relation.referencesAdelantado, 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
dc.relation.referencesBouguera, 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
dc.relation.referencesChen, M., Mao, S., Liu, Y., Big data: A survey (2014) Mobile Networks and Applications, 19 (2), pp. 171-209
dc.relation.referencesde 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
dc.relation.referencesGao, 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)
dc.relation.referencesLasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M., Industry 4.0 (2014) Business & Information Systems Engineering, 6 (4), pp. 239-242
dc.relation.referencesLavric, 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
dc.relation.referencesLee, J., Davari, H., Singh, J., Pandhare, V., Industrial Artificial Intelligence for industry 4.0-based manufacturing systems (2018) Manufacturing Letters, 18, pp. 20-23
dc.relation.referencesLi, 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)
dc.relation.referencesNoble, W.S., What is a support vector machine? (2006) Nature Biotechnology, 24 (12), pp. 1565-1567
dc.relation.referencesPetrasch, 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)
dc.relation.referencesRatasuk, 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
dc.relation.referencesReynders, 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)
dc.relation.referencesReynders, 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).
dc.relation.referencesSan 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).
dc.relation.referencesSandoval, 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
dc.relation.referencesSandoval, 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
dc.relation.referencesSchwab, K., (2017) The Fourth Industrial Revolution, , Penguin Random House Grupo Editorial España, 2016
dc.relation.referencesSlabicki, 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.
dc.relation.referencesSornin, N., Luis, M., Eirich, T., Kramp, T., Hersent, O., (2015) Lorawan Specification. Lora Alliance, ,
dc.relation.referencesWollschlaeger, 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

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record