Metodologia Automatizada para Descrever a Cobertura de Sinal Móvel em Rodovias: Estudo de Caso Considerando Incerteza de Dados
Resumo
Devido à grande distribuição geográfica ou à existência de ampla infraestrutura rodoviária, é desafiador garantir que operadoras de dados móveis forneçam um serviço adequado ao longo das rodovias. Uma metodologia automatizada para descrever e avaliar a cobertura de sinal de rede móvel em rodovias pode colaborar para que governos e operadoras planejem com eficiência ações para melhorar a qualidade do serviço provido pelas operadoras. Neste trabalho, é proposto uma metodologia automatizada para descrever e permitir a avaliação da cobertura de sinal de rede móvel em rodovias usando dados de rastreamento de veículos. A abordagem proposta agrega, usando um critério espaço-temporal, amostras de dados obtidos através de um sistema de rastreamento de veículos. A saída resultante deste processo de agregação é um mapa das rodovias com um esquema de cores gradiente, para indicar a disponibilidade do sinal e a confiança desta informação. Como o tráfego é dinâmico e os dados de tráfego não seguem uma distribuição uniforme, a medida de incerteza associada ao mapa indica a confiança da informação provida. Como estudo de caso, foi usado um conjunto de dados relativos a viagens no sul do Brasil coletados através de um serviço privado de rastreamento de veículos.
Palavras-chave:
mapa de cobertura, disponibilidade do sinal, rodovias, rastreamento de veículos
Referências
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A. Rago, G. Piro, H. D. Trinh, G. Boggia, and P. Dini, “Unveiling radio resource utilization dynamics of mobile traffic through unsupervised learning,” in 2019 Network Traffic Measurement and Analysis Conference (TMA), 2019, pp. 209–214.
V. Adarsh, M. Nekrasov, U. Paul, T. Mangla, A. Gupta, M. Vigil-Hayes, E. Zegura, and E. Belding, “Coverage is not binary: Quantifying mobile broadband quality in urban, rural, and tribal contexts,” in 2021 International Conference on Computer Communications and Networks (ICCCN), 07 2021, pp. 1–9.
A. S. Khatouni, M. Mellia, M. A. Marsan, S. Alfredsson, J. Karlsson, A. Brunstrom, O. Alay, A. Lutu, C. Midoglu, and V. Mancuso, “Speedtest-like measurements in 3g/4g networks: The monroe experience,” in 2017 29th International Teletraffic Congress (ITC 29), vol. 1, 2017, pp. 169–177.
M. J. Siedner, A. Lankowski, D. Musinga, and et al., “Optimizing network connectivity for mobile health technologies in sub-Saharan Africa,” PLoS ONE 7(9): e45643, September 2012.
M. K. Marina, V. Radu, and K. Balampekos, “Impact of indoor-outdoor context on crowdsourcing based mobile coverage analysis,” in Proceedings of the 5th Workshop on All Things Cellular: Operations, Applications and Challenges, 2015, pp. 45–50.
H. Wang, S. Xie, and M. Li, K.and Ahmad, “Big data-driven cellular information detection and coverage identification,” Sensors, vol. 19, no. 4, pp. 937–, 2019.
Y. Liu, Z. Yang, X. Wang, and L. Jian, “Location, localization, and localizability,” Journal of Computer Science and Technology, vol. 25, no. 2, pp. 274–297, 2010.
V. Freschi, S. Delpriori, L. C. Klopfenstein, E. Lattanzi, G. Luchetti, and A. Bogliolo, “Geospatial data aggregation and reduction in vehicular sensing applications: The case of road surface monitoring,” in 2014 International Conference on Connected Vehicles and Expo (ICCVE), 2014, pp. 711–716.
A. Bokani, M. Hassan, S. S. Kanhere, J. Yao, and G. Zhong, “Comprehensive mobile bandwidth traces from vehicular networks,” in Proceedings of the 7th International Conference on Multimedia Systems, ser. MMSys ’16. New York, NY, USA: Association for Computing Machinery, 2016.
E. Alimpertis, A. Markopoulou, C. Butts, and K. Psounis, “Citywide signal strength maps: Prediction with random forests,” in The World Wide Web Conference. New York, NY, USA: Association for Computing Machinery, 2019, pp. 2536–2542.
M. Fida, A. Lutu, M. K. Marina, and Özgü. Alay, “Zipweave: Towards efficient and reliable measurement based mobile coverage maps,” in IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, 2017, pp. 1–9.
A. Lutu, Y. R. Siwakoti, Özgü. Alay, D. Baltrũnas, and A. Elmokashfi, “The good, the bad and the implications of profiling mobile broadband coverage,” Computer Networks, vol. 107, pp. 76–93, 2016.
M.-R. Fida and M. K. Marina, “Impact of device diversity on crowd-sourced mobile coverage maps,” in 2018 14th International Conference on Network and Service Management (CNSM), 2018, pp. 348–352.
H. Abrahamsson, F. B. Abdesslem, B. Ahlgren, A. Brunstrom, I. Marsh, and M. Bjorkman, “Connected vehicles in cellular networks: Multi-access versus single-access performance,” in 2018 Network Traffic Measurement and Analysis Conference (TMA), 2018, pp. 1–6.
W. Z. Xavier, F. H. Z. Xavier, and H. T. Marques-Neto, “Visualizing and analyzing georeferenced workloads of mobile networks,” in 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017, pp. 306–310.
B. Engiz and C. Kurnaz, “Comparison of signal strengths of 2G/3G/4G services on a university campus,” International Journal of Applied Mathematics, Electronics and Computers (IJAMEC), pp. 37–42, December 2016.
Y. Liu, W. Huangfu, H. Zhang, and K. Long, “Multi-criteria coverage map construction based on adaptive triangulation-induced interpolation for cellular networks,” IEEE Access, vol. 7, pp. 80 767–80 777, 2019.
C. Jarvis, C. Midoglu, A. Lutu, and O. Alay, “Visualizing mobile coverage from repetitive measurements on defined trajectories,” in 2018 Network Traffic Measurement and Analysis Conference (TMA), 2018, pp. 1–6.
D. Madariaga, J. Madariaga, J. Bustos, and B. Bustos, “Improving signal-strength aggregation for mobile crowdsourcing scenarios,” Sensors, vol. 21, p. 1084, 02 2021.
A. S. Khatouni, M. Trevisan, D. Giordano, M. Rajiullah, S. Alfredsson, A. Brunstrom, C. Midoglu, and O. Alay, “An open dataset of operational mobile networks,” in Proceedings of the 18th ACM Symposium on Mobility Management and Wireless Access, ser. MobiWac ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 83–90. [Online]. Available: https://doi.org/10.1145/3416012.3424619
K. Kousias, C. Midoglu, O. Alay, A. Lutu, A. Argyriou, and M. Riegler, “The same, only different: Contrasting mobile operator behavior from crowdsourced dataset,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017, pp. 1–6.
A. Schwind, F. Wamser, T. Hossfeld, S. Wunderer, E. Tarnvik, and A. Hall, “Crowdsourced network measurements in germany: mobile internet experience from end user perspective,” in Broadband Coverage in Germany; 14. ITG Symposium, 2020, pp. 1–7.
Publicado
21/11/2022
Como Citar
RÖPKE, Leonardo L.; PASIN, Marcia; SCHNORR, Lucas M..
Metodologia Automatizada para Descrever a Cobertura de Sinal Móvel em Rodovias: Estudo de Caso Considerando Incerteza de Dados. In: ARTIGOS COMPLETOS - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 48-55.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2022.227163.
