Drift Detection e Machine Learning para Sistemas de Localização indoor RFID em Ambientes Dinâmicos
Resumo
A localização de objetos em ambientes internos e dinâmicos é uma tarefa desafiadora, pois além da presença de materiais reflexivos e excesso de obstáculos, a posição dos objetos são alteradas constantemente. Para contornar tais problemas, nós propomos o uso da tecnologia RFID e métodos de aprendizagem de máquina em conjunto com técnicas de Drift Detection para a construção de sistemas de localização indoor. A principal contribuição deste artigo é a proposta de um sistema de localização RFID de alta precisão (5 cm) para ambientes onde há mudanças incrementais na posição dos objetos. O resultado obtido com a utilização de técnicas de Drift Detection permitiu ao sistema manter acurácia acima de 96.90% ao longo das 110.000 instâncias.
Referências
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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.
Dongre, P. B. and Malik, L. G. (2014). A review on real time data stream classification and adapting to various concept drift scenarios. In 2014 IEEE International Advance Computing Conference (IACC), pages 533–537.
Frías-Blanco, I., d. Campo Ávila, J., Ramos-Jiménez, G., Morales-Bueno, R., Ortiz-Díaz, A., and Caballero-Mota, Y. (2015). Online and non-parametric drift detection methods based on hoeffding’s bounds. IEEE Transactions on Knowledge and Data Engineering, 27(3):810–823.
Gama, J., Medas, P., Castillo, G., and Rodrigues, P. P. (2004). Learning with drift detection. In SBIA, volume 3171, pages 286–295. Springer.
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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Goswami, S. (2013). Indoor location technologies. New York, Springer, 1st edition.
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Hernández, N., Alonso, J. M., and Oca˜na, M. (2017). Fuzzy classifier ensembles for hierarchical wi-fi based semantic indoor localization. Expert Systems with Applications, 90:394–404.
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.
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Rappaport, T. (2001). Wireless Communications: Principles and Practice. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2nd edition.
Rohei, M. S., Salwana, E., Shah, N. B. A. K., and Kakar, A. S. (2021). Design and testing of an epidermal rfid mechanism in a smart indoor human tracking system. IEEE Sensors Journal, 21:5476–5486.
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.
Santos, R., Leonardo, R., Barandas, M., Moreira, D., Rocha, T., Alves, P., Oliveira, J. P., and Gamboa, H. (2021). Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings. IEEE Access, pages 1–1.
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Torres-Sospedra, J., Montoliu, R., Trilles, S., Belmonte, ´O., 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, H., Wang, C., and Xie, L. (2021). Intensity-slam: Intensity assisted localization and mapping for large scale environment. IEEE Robotics and Automation Letters, pages 1–1.
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.
Yao, C. Y. and Hsia, W. C. (2018). An indoor positioning system based on the dual-channel passive rfid technology. IEEE Sensors Journal, 18:4654–4663.
Zhang, C., Qin, N., Xue, Y., and Yang, L. (2020). Received signal strength-based indoor localization using hierarchical classification. Sensors (Switzerland), 20.
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).
Baena-Garca, M., Ávila, J. D. C., Fidalgo, R., Bifet, A., Gavaldà, R., and Bueno, R. M. (2006). Early drift detection method. In Fourth International Workshop on Knowledge Discovery from Data Streams.
Batra, U., of Computer Science Engineering, I. U. D., Technology., I., of Electrical, I., and Engineers, E. (2014). Souvenir of the 2014 IEEE International Advance Computing Conference (IACC) : February 21-22, 2014 : Gurgaon, India.
Bifet, A. and Gavaldà, R. (2007). Learning from time-changing data with adaptive windowing. In Proceedings of the Seventh SIAM International Conference on Data Mining, April 26-28, 2007, Minneapolis, Minnesota, USA, pages 443–448. SIAM.
Bifet, A., Gavaldà, R., Holmes, G., and Pfahringer, B. (2018). Machine Learning for Data Streams with Practical Examples in MOA. MIT Press.
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.
Dongre, P. B. and Malik, L. G. (2014). A review on real time data stream classification and adapting to various concept drift scenarios. In 2014 IEEE International Advance Computing Conference (IACC), pages 533–537.
Frías-Blanco, I., d. Campo Ávila, J., Ramos-Jiménez, G., Morales-Bueno, R., Ortiz-Díaz, A., and Caballero-Mota, Y. (2015). Online and non-parametric drift detection methods based on hoeffding’s bounds. IEEE Transactions on Knowledge and Data Engineering, 27(3):810–823.
Gama, J., Medas, P., Castillo, G., and Rodrigues, P. P. (2004). Learning with drift detection. In SBIA, volume 3171, pages 286–295. Springer.
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.
Gomes, E. L., Fonseca, M., Munaretto, A., and Guerber, C. R. (2020). Etiquetas rfid passivas e aprendizagem de máquina para sistema de localização indoor de alta precisão. Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), pages 252–265.
Goswami, S. (2013). Indoor location technologies. New York, Springer, 1st edition.
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).
Hernández, N., Alonso, J. M., and Oca˜na, M. (2017). Fuzzy classifier ensembles for hierarchical wi-fi based semantic indoor localization. Expert Systems with Applications, 90:394–404.
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.
Lai, J., Luo, C., Wu, J., Li, J., Wang, J., Chen, J., Feng, G., and Song, H. (2020). Tagsort: Accurate relative IEEE Internet of Things localization exploring rfid phase spectrum matching for internet of things. Journal, 7:389–399.
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.
Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2018). Foundations of Machine Learning. The MIT Press, 2nd edition.
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.
Raab, C., Heusinger, M., and Schleif, F.-M. (2020). Reactive soft prototype computing for concept drift streams. Neurocomputing, 416:340–351.
Rappaport, T. (2001). Wireless Communications: Principles and Practice. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2nd edition.
Rohei, M. S., Salwana, E., Shah, N. B. A. K., and Kakar, A. S. (2021). Design and testing of an epidermal rfid mechanism in a smart indoor human tracking system. IEEE Sensors Journal, 21:5476–5486.
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.
Santos, R., Leonardo, R., Barandas, M., Moreira, D., Rocha, T., Alves, P., Oliveira, J. P., and Gamboa, H. (2021). Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings. IEEE Access, pages 1–1.
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.
Torres-Sospedra, J., Montoliu, R., Trilles, S., Belmonte, ´O., 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, H., Wang, C., and Xie, L. (2021). Intensity-slam: Intensity assisted localization and mapping for large scale environment. IEEE Robotics and Automation Letters, pages 1–1.
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.
Yao, C. Y. and Hsia, W. C. (2018). An indoor positioning system based on the dual-channel passive rfid technology. IEEE Sensors Journal, 18:4654–4663.
Zhang, C., Qin, N., Xue, Y., and Yang, L. (2020). Received signal strength-based indoor localization using hierarchical classification. Sensors (Switzerland), 20.
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
16/08/2021
Como Citar
GOMES, Eduardo L.; FONSECA, Mauro; LAZZARETTI, André; GUERBER, Carlos R.; MUNARETTO, Anelise.
Drift Detection e Machine Learning para Sistemas de Localização indoor RFID em Ambientes Dinâmicos. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 224-237.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16723.
