Etiquetas RFID Passivas e Aprendizagem de Máquina para Sistema de Localização Indoor de Alta Precisão
Resumo
A localização de objetos em ambientes internos é uma tarefa desafiadora onde não há contato físico ou campo de visão e existe a presença de materiais reflexivos além do excesso de obstáculos. Para contornar tais problemas, nós propomos o uso da tecnologia RFID e métodos de aprendizagem de máquina para a construção de sistemas de localização em ambientes internos. As principais contribuições deste artigo são: um sistema de localização de alta precisão, modelagem específica para ambientes com prateleiras e o módulo de aprendizagem de máquina, o qual extrai e deriva estatisticamente características das leituras RFID. O resultado obtido com a implantação em um ambiente real foi de 100% de acurácia com o classificador Random Forest.
Palavras-chave:
RFID, localização indoor, Aprendizagem de Máquina
Referências
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Akre, J.-M., Zhang, X., Baey, S., Kervella, B., Fladenmuller, A., Zancanaro, M., and Fonseca, M. (2014). Accurate 2-D localization of RFID tags using antenna transmission power control. 2014 IFIP Wireless Days (WD), pages 1–6.
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Dinh-Van, N., Nashashibi, F., Thanh-Huong, N., and Castelli, E. (2017). Indoor Intelligent Vehicle localization using WiFi received signal strength indicator. 2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility.
Gomes, E. L., Fonseca, M., Munaretto, A., and Guerber, C. R. (2017). Arquitetura RF-Miner - Uma solução para localização indoor. XXII Workshop de Gerência e Operação de Redes e Serviços(WGRS), pages 111–122.
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Hatem, E., El-Hassan, B., Laheurte, J.-M., Abou-Chakra, S., Colin, E., and Marechal, C. (2018). Study the estimated distance error in indoor localization using uhf-rfid. 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM).
Hori, T., Wada, T., Ota, Y., Uchitomi, N., Mutsuura, K., and Okada, H. (2008). A multi-sensing-range method for position estimation of passive RFID tags. 4th IEEE WiMob 2008, pages 208–213.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In 14th International Joint Conference on Artificial Intelligence, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Kvam, P. and Vidakovic, B. (2007). Nonparametric statistics with applications to science and engineering. 123.
Ma, H., Wang, Y., and Wang, K. (2018). Automatic detection of false positive RFID readings using machine learning algorithms. Expert Systems with Applications.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., New York, NY, USA.
Nakamori, E., Tsukuda, D., Fujimoto, M., Oda, Y., Wada, T., Okada, H., and Mutsuura, K. (2012). A new indoor position estimation method of RFID tags for continuous moving navigation systems. 2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012 - Conference Proceedings, (November).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
R. Smith, J. (2013). Wirelessly Powered Sensor Networks and Computational RFID.
Rappaport, T. (2001). Wireless Communications: Principles and Practice. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2nd edition.
Sabr, O. and Belton, J. (2019). Identifying and Tracking Individuals in a Smart Indoor Environment. IEEE-Fifth International Engineering Conference on Developments in Civil & Computer Engineering Applications.
Spiegel, M. R. and Stephens, L. J. (2015). Estatı́stica: Coleção Schaum. Bookman Companhia Editora Ltda., Porto Alegre, RS, Brasil, 4rd edition.
Steele, A. M., Bopp, M. M., Rock, L., Boppvagov, M. M., Taylor, T. S., Sullivan, D. H., and Motivation, A. (2019). Patient Activity Monitoring Based on Real-Time Location Data. 2019 IEEE Int. Conf. on Bioinformatics and Biomedicine.
Ting, S. L., Kwok, S. K., Tsang, A. H. C. A., and Ho, G. G. T. S. (2011). The Study on Using Passive RFID Tags for Indoor Positioning. International Journal of . . . , (1).
Torres-Sospedra, J., Montoliu, R., Trilles, S., Belmonte, Ó., and Huerta, J. (2015). Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems. Expert Systems with Applications, 42(23):9263–9278.
Wang, J., Dhanapal, R. K., Ramakrishnan, P., Balasingam, B., Souza, T., and Maev, R. (2019). Active RFID Based Indoor Localization. IEEE-22nd International Conference on Information Fusion.
Westcott, D. A., Coleman, D. D., Miller, B., and Mackenzie, P. (2011). CWAP Certified Wireless Analysis Professional Official Study Guide. SYBEX Inc., CA, USA.
Zhang, X. b., Akre, J.-M. b., Baey, S. b., Fladenmuller, A. b., Kervella, B. b., Zancanaro, M. b. c., and Fonseca, M. (2015). Towards localization of RFID tags based on experimental analysis of RSSI. IFIP Wireless Days, (January).
Akre, J.-M., Zhang, X., Baey, S., Kervella, B., Fladenmuller, A., Zancanaro, M., and Fonseca, M. (2014). Accurate 2-D localization of RFID tags using antenna transmission power control. 2014 IFIP Wireless Days (WD), pages 1–6.
Berz, E. L. and Hessel, F. P. (2015). Sistema hı́brido de localização indoor baseado em rfid e análise visual. Faculdade de Informática, PUCRS. Porto Alegre-RS.
Bonaccorso, G. (2017). Machine Learning Algorithms: A Reference Guide to Popular Algorithms for Data Science and Machine Learning. Packt Publishing.
Breiman, L. (2001). Random forests. Mach. Learn., 45(1):5–32.
Calderoni, L., Ferrara, M., Franco, A., and Maio, D. (2015). Indoor localization in a hospital environment using RF classifiers. Expert Systems with Applications.
Dinh-Van, N., Nashashibi, F., Thanh-Huong, N., and Castelli, E. (2017). Indoor Intelligent Vehicle localization using WiFi received signal strength indicator. 2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility.
Gomes, E. L., Fonseca, M., Munaretto, A., and Guerber, C. R. (2017). Arquitetura RF-Miner - Uma solução para localização indoor. XXII Workshop de Gerência e Operação de Redes e Serviços(WGRS), pages 111–122.
Han, J., Kamber, M., and Pei, J. (2012). Data mining concepts and techniques, third edition. Morgan Kaufmann Publishers, Waltham, Mass.
Hatem, E., El-Hassan, B., Laheurte, J.-M., Abou-Chakra, S., Colin, E., and Marechal, C. (2018). Study the estimated distance error in indoor localization using uhf-rfid. 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM).
Hori, T., Wada, T., Ota, Y., Uchitomi, N., Mutsuura, K., and Okada, H. (2008). A multi-sensing-range method for position estimation of passive RFID tags. 4th IEEE WiMob 2008, pages 208–213.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In 14th International Joint Conference on Artificial Intelligence, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Kvam, P. and Vidakovic, B. (2007). Nonparametric statistics with applications to science and engineering. 123.
Ma, H., Wang, Y., and Wang, K. (2018). Automatic detection of false positive RFID readings using machine learning algorithms. Expert Systems with Applications.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., New York, NY, USA.
Nakamori, E., Tsukuda, D., Fujimoto, M., Oda, Y., Wada, T., Okada, H., and Mutsuura, K. (2012). A new indoor position estimation method of RFID tags for continuous moving navigation systems. 2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012 - Conference Proceedings, (November).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
R. Smith, J. (2013). Wirelessly Powered Sensor Networks and Computational RFID.
Rappaport, T. (2001). Wireless Communications: Principles and Practice. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2nd edition.
Sabr, O. and Belton, J. (2019). Identifying and Tracking Individuals in a Smart Indoor Environment. IEEE-Fifth International Engineering Conference on Developments in Civil & Computer Engineering Applications.
Spiegel, M. R. and Stephens, L. J. (2015). Estatı́stica: Coleção Schaum. Bookman Companhia Editora Ltda., Porto Alegre, RS, Brasil, 4rd edition.
Steele, A. M., Bopp, M. M., Rock, L., Boppvagov, M. M., Taylor, T. S., Sullivan, D. H., and Motivation, A. (2019). Patient Activity Monitoring Based on Real-Time Location Data. 2019 IEEE Int. Conf. on Bioinformatics and Biomedicine.
Ting, S. L., Kwok, S. K., Tsang, A. H. C. A., and Ho, G. G. T. S. (2011). The Study on Using Passive RFID Tags for Indoor Positioning. International Journal of . . . , (1).
Torres-Sospedra, J., Montoliu, R., Trilles, S., Belmonte, Ó., and Huerta, J. (2015). Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems. Expert Systems with Applications, 42(23):9263–9278.
Wang, J., Dhanapal, R. K., Ramakrishnan, P., Balasingam, B., Souza, T., and Maev, R. (2019). Active RFID Based Indoor Localization. IEEE-22nd International Conference on Information Fusion.
Westcott, D. A., Coleman, D. D., Miller, B., and Mackenzie, P. (2011). CWAP Certified Wireless Analysis Professional Official Study Guide. SYBEX Inc., CA, USA.
Zhang, X. b., Akre, J.-M. b., Baey, S. b., Fladenmuller, A. b., Kervella, B. b., Zancanaro, M. b. c., and Fonseca, M. (2015). Towards localization of RFID tags based on experimental analysis of RSSI. IFIP Wireless Days, (January).
Publicado
07/12/2020
Como Citar
GOMES, Eduardo Luis; FONSECA, Mauro Sergio Pereira; GUERBER, Carlos Rafael; FONSECA, Anelise Munaretto.
Etiquetas RFID Passivas e Aprendizagem de Máquina para Sistema de Localização Indoor de Alta Precisão. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 225-238.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12285.
