Localização de Usuários Móveis Baseada em Fingerprint de Rádio Frequência: Redução de Espaço de Busca usando Parâmetros de Atraso de Onda das Redes Celulares
Resumo
As técnicas de radiolocalização baseadas em fingerprint de rádio frequência têm se mostrado uma alternativa interessante frente aos sistemas de posicionamento global. Um dos atrativos da técnica de fingerprint é a redução do consumo de energia por meio de implementações simplificadas de suas etapas, como, por exemplo, na etapa de predição da posição do usuário móvel. Este trabalho apresenta uma modificação na técnica de redução de espaço de busca que utiliza o timing advance (TA), parâmetro de atraso de onda das redes celulares. Os resultados mostraram que o tempo de predição foi reduzido, em média, 72,63% em relação à técnica original, enquanto o tempo de treinamento sofreu um acréscimo médio de 60,18%, sem comprometer a acurácia da localização.
Palavras-chave:
Localização de usuários, fingerprint, rádio frequência, espaço de busca, atraso de onda
Referências
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Deville, P., Lenard, C., Martin, S., and Gilbert, M. (2014). Dynamic population mapping using mobile phone data. Proc. of the National Academy of Sciences, 111(45):15888– 15893.
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Nyhan, M. M., Kloog, I., Britter, R., Ratti, C., and Koutrakis, P. (2019). Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data. Journal of Exposure Science & Environmental Epidemiology, 29(2):238.
Peral-Rosado, J. A., Raulefs, R., López-Salcedo, J. A., and Seco-Granados, G. (2018). Survey of cellular mobile radio localization methods: From 1G to 5G. IEEE Communic. Surveys & Tutorials, 20(2, Second Quarter 2018):1124–1148.
Salomon, A. and Mahaffey, K. P. (2019). Mobile communications device payment method utilizing location information. US Patent App. 10/181,118.
Saunders, S. and Aragón-Zavala, A. (2007). Antennas and Propagation for Wireless Communication Systems. John Wiley & Sons, 2nd edition.
Timoteo, R. D., Silva, L. N., Cunha, D. C., and Cavalcanti, G. D. (2016). An approach using support vector regression for mobile location in cellular networks. Elsevier Computer Networks, 95:51–61.
Trogh, J., Plets, D., Surewaard, E., Spiessens, M., Versishele, M., Martens, L., and Joseph, W. (2019). Outdoor location tracking of mobile devices in cellular networks. EURASIP Journal on Wireless Communications and Networking 2019, (115):1–18.
Viel, A., Gallo, P., Montanari, A., Gubiani, D., Dalla Torre, A., Pittino, F., and Marshall, C. (2017). Dealing with network changes in cellular fingerprint positioning systems. In Proc. of the IEEE Int. Conf. on Localization and GNSS (ICL-GNSS 2017), pages 1–6.
Vo, Q. D. and De, P. (2016). A survey of fingerprint-based outdoor localization. IEEE Communications Surveys & Tutorials, 18(1):491–506.
Zekavat, R. and Buehrer, R. M. (2019). Handbook of Position Location: Theory, Practice and Advances. John Wiley & Sons, 2nd edition.
Zhang, H., Zhang, Z., Zhang, S., Xu, S., and Cao, S. (2019). Fingerprint-based localization using commercial lte signals: a field-trial study. In Proc. of the IEEE 90th Vehicular Technology Conf. (VTC-2019 Fall), pages 1–5.
Bittencourt, G. P., Urbano, A. A., and Cunha, D. C. (2018). A proposal of an RF fingerprint-based outdoor localization technique using irregular grid maps. In Proc. of the IEEE Wireless Communications and Networking Conference (WCNC 2018), pages 1–6.
Campos, R. and Lovisolo, L. (2019). Genetic algorithm-based cellular network optimisation considering positioning applications. IET Communications, 13(7):879–891.
Campos, R. S. and Lovisolo, L. (2009). A fast database correlation algorithm for localization of wireless network mobile nodes using coverage prediction and round trip delay. In Proc. of the IEEE Vehicular Technology Conference (VTC 2009), pages 1–5.
Chen, K., Tan, G., Cao, J., Lu, M., and Fan, X. (2019). Modeling and improving the energy performance of gps receivers for location services. IEEE Sensors Journal, 20(8):4512–4523.
Deville, P., Lenard, C., Martin, S., and Gilbert, M. (2014). Dynamic population mapping using mobile phone data. Proc. of the National Academy of Sciences, 111(45):15888– 15893.
Hammad, A. and Faith, P. (2017). Location based authentication. US Patent 9,721,250.
Huazhou, Chen, Z., Liu, K., Cai, L., Xu, A., and Chen (2018). Grid search parametric optimization for ft-nir quantitative analysis of solid soluble content in strawberry samples. Vibrational Spectroscopy, 94(1):7–15.
Kose, M., Tascioglu, S., and Telatar, Z. (2019). RF fingerprinting of IoT devices based on transient energy spectrum. IEEE Access, 7:18715–18726.
Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer, 1nd edition.
Mondal, R. U., Ristaniemi, T., and Turkka, J. (2015). Genetic algorithm optimized grid-based RF fingerprint positioning in heterogeneous small cell networks. In Proc. of the IEEE Int. Conf. on Location and GNSS (ICL-GNSS 2015), pages 1–7.
Noi, P. T. and Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors, 18(1):1–20.
Nouichi, D., Abdelsalam, M., Nasir, Q., and Abbas, S. (2019). IoT devices security using RF fingerprinting. In Proc. of the Int. Conf. on Advances in Science and Engineering Technology (ASET 2019), pages 1–7.
Nyhan, M. M., Kloog, I., Britter, R., Ratti, C., and Koutrakis, P. (2019). Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data. Journal of Exposure Science & Environmental Epidemiology, 29(2):238.
Peral-Rosado, J. A., Raulefs, R., López-Salcedo, J. A., and Seco-Granados, G. (2018). Survey of cellular mobile radio localization methods: From 1G to 5G. IEEE Communic. Surveys & Tutorials, 20(2, Second Quarter 2018):1124–1148.
Salomon, A. and Mahaffey, K. P. (2019). Mobile communications device payment method utilizing location information. US Patent App. 10/181,118.
Saunders, S. and Aragón-Zavala, A. (2007). Antennas and Propagation for Wireless Communication Systems. John Wiley & Sons, 2nd edition.
Timoteo, R. D., Silva, L. N., Cunha, D. C., and Cavalcanti, G. D. (2016). An approach using support vector regression for mobile location in cellular networks. Elsevier Computer Networks, 95:51–61.
Trogh, J., Plets, D., Surewaard, E., Spiessens, M., Versishele, M., Martens, L., and Joseph, W. (2019). Outdoor location tracking of mobile devices in cellular networks. EURASIP Journal on Wireless Communications and Networking 2019, (115):1–18.
Viel, A., Gallo, P., Montanari, A., Gubiani, D., Dalla Torre, A., Pittino, F., and Marshall, C. (2017). Dealing with network changes in cellular fingerprint positioning systems. In Proc. of the IEEE Int. Conf. on Localization and GNSS (ICL-GNSS 2017), pages 1–6.
Vo, Q. D. and De, P. (2016). A survey of fingerprint-based outdoor localization. IEEE Communications Surveys & Tutorials, 18(1):491–506.
Zekavat, R. and Buehrer, R. M. (2019). Handbook of Position Location: Theory, Practice and Advances. John Wiley & Sons, 2nd edition.
Zhang, H., Zhang, Z., Zhang, S., Xu, S., and Cao, S. (2019). Fingerprint-based localization using commercial lte signals: a field-trial study. In Proc. of the IEEE 90th Vehicular Technology Conf. (VTC-2019 Fall), pages 1–5.
Publicado
07/12/2020
Como Citar
SILVA, Guilherme Henrique Sousa; DOMINGUES, Thiago; SILVA, Gabriel Wanderley; CUNHA, Daniel.
Localização de Usuários Móveis Baseada em Fingerprint de Rádio Frequência: Redução de Espaço de Busca usando Parâmetros de Atraso de Onda das Redes Celulares. 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. 700-713.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12319.
