MOCHA: Um framework para caracterização e comparação de traces de mobilidade
Resumo
Existem diversos modelos de mobilidade na literatura, em formatos diversos bem como suas origens. Apesar da existência de diversos trabalhos que caracterizem esses modelos, há a necessidade de um framework que possa compará-los de maneira fácil. O MOCHA é uma ferramenta que caracteriza e possibilita a comparação de modelos de mobilidade de forma simples. Foram implementadas 9 características sociais, espaciais e temporais, e essas características foram extraídas de diversos traces (reais e sintéticos) de mobilidade diferentes. A ferramenta provê diversas métricas que podem se tornar padrão para a comparação de traces e permite um entendimento de onde um trace se encontra em relação aos outros.
Referências
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Bai, F., Sadagopan, N., e Helmy, A. (2003). Important: A framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc In INFOCOM 2003. Twenty-second annual joint conference of the IEEE networks. computer and communications. IEEE societies, volume 2, pages 825–835. IEEE.
Baumann, R., Legendre, F., e Sommer, P. (2008). Generic mobility simulation framework (gmsf). In Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models, pages 49–56. ACM.
Bezerra, R. L., Campos, C. A. V., e de Moraes, L. F. M. (2009). Uma proposta de técnica para o ajuste de modelos de mobilidade em redes ad hoc e questionamentos sobre a adequação dos parâmetros envolvidos com base em dados reais. Simpósio Brasileiro de Redes de Computadores (SBRC)(Brasil, 2009).
de Melo, P. O. V., Viana, A. C., Fiore, M., Jaffrès-Runser, K., Le Mouël, F., Loureiro, A. A., Addepalli, L., e Guangshuo, C. (2015). Recast: Telling apart social and random relationships in dynamic networks. Performance Evaluation, 87:19–36.
Dimatteo, S., Hui, P., Han, B., e Li, V. O. (2011). Cellular trafc ofoading through wi networks. In Mobile Adhoc and Sensor Systems (MASS), 2011 IEEE 8th International Conference on, pages 192–201. IEEE.
Ekman, F., Keränen, A., Karvo, J., e Ott, J. (2008a). Working day movement model. In Proceedings of the 1st ACM SIGMOBILE Workshop on Mobility Models, MobilityModels ’08, pages 33–40, New York, NY, USA. ACM.
Ekman, F., Keränen, A., Karvo, J., e Ott, J. (2008b). Working day movement model. In Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models, pages 33–40. ACM.
Ferreira, F., Silva, T. H., e Loureiro, A. A. F. (2017). Slkit: An r package for property extraction and analysis of multiple sensing layers. In Computer Networks and Distributed Systems (SBRC), 2017 XXXV Brazilian Symposium on, pages 1184–1191. IEEE.
Helgason, Ó., Kouyoumdjieva, S. T., e Karlsson, G. (2014). Opportunistic communication and human mobility. IEEE Transactions on Mobile Computing, 13(7):1597–1610.
John Lu, Z. Q. (2010). The elements of statistical learning: Data mining, inference, and prediction. Journal of the Royal Statistical Society: Series A (Statistics in Society), 173(3):693–694.
Karamshuk, D., Boldrini, C., Conti, M., e Passarella, A. (2011). Human mobility models for opportunistic networks. IEEE Communications Magazine, 49(12):157–165.
Kim, M., Kotz, D., e Kim, S. (2006). Extracting a mobility model from real user traces. In Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications, pages 1–13.
Kosta, S., Mei, A., e Stefa, J. (2014). Large-scale synthetic social mobile networks with swim. IEEE Transactions on Mobile Computing, 13(1):116–129.
Lee, K., Hong, S., Kim, S. J., Rhee, I., e Chong, S. (2009). Slaw: A new mobility model for human walks. In IEEE INFOCOM 2009, pages 855–863.
Maaten, L. v. d. e Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(Nov):2579–2605.
Mei, A. e Stefa, J. (2009). Swim: A simple model to generate small mobile worlds. In IEEE INFOCOM 2009, pages 2106–2113.
Munjal, A., Camp, T., e Navidi, W. C. (2010). Constructing rigorous manet simulation scenarios with realistic mobility. In Wireless Conference (EW), 2010 European, pages 817–824. IEEE.
Piorkowski, M., Sarajanovic-Djukic, N., e Grossglauser, M. (2009). CRAWDAD dataset ep/mobility (v. 2009-02-24). Downloaded from https://crawdad.org/epfl/mobility/20090224.
Scholz, F. W. (2004). Maximum Likelihood Estimation. John Wiley Sons, Inc.
Song, L. e Kotz, D. F. (2007). Evaluating opportunistic routing protocols with large realistic contact traces. In Proceedings of the second ACM workshop on Challenged networks, pages 35–42. ACM.
Thakur, G. S. e Helmy, A. (2013). Cobra: A framework for the analysis of realistic mobility models. In INFOCOM, 2013 Proceedings IEEE, pages 3351–3356. IEEE.
Yang, D., Zhang, D., Chen, L., e Qu, B. (2015). Nationtelescope: Monitoring and visualizing large-scale collective behavior in lbsns. Journal of Network and Computer Applications, 55:170–180.
Bai, F., Sadagopan, N., e Helmy, A. (2003). Important: A framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc In INFOCOM 2003. Twenty-second annual joint conference of the IEEE networks. computer and communications. IEEE societies, volume 2, pages 825–835. IEEE.
Baumann, R., Legendre, F., e Sommer, P. (2008). Generic mobility simulation framework (gmsf). In Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models, pages 49–56. ACM.
Bezerra, R. L., Campos, C. A. V., e de Moraes, L. F. M. (2009). Uma proposta de técnica para o ajuste de modelos de mobilidade em redes ad hoc e questionamentos sobre a adequação dos parâmetros envolvidos com base em dados reais. Simpósio Brasileiro de Redes de Computadores (SBRC)(Brasil, 2009).
de Melo, P. O. V., Viana, A. C., Fiore, M., Jaffrès-Runser, K., Le Mouël, F., Loureiro, A. A., Addepalli, L., e Guangshuo, C. (2015). Recast: Telling apart social and random relationships in dynamic networks. Performance Evaluation, 87:19–36.
Dimatteo, S., Hui, P., Han, B., e Li, V. O. (2011). Cellular trafc ofoading through wi networks. In Mobile Adhoc and Sensor Systems (MASS), 2011 IEEE 8th International Conference on, pages 192–201. IEEE.
Ekman, F., Keränen, A., Karvo, J., e Ott, J. (2008a). Working day movement model. In Proceedings of the 1st ACM SIGMOBILE Workshop on Mobility Models, MobilityModels ’08, pages 33–40, New York, NY, USA. ACM.
Ekman, F., Keränen, A., Karvo, J., e Ott, J. (2008b). Working day movement model. In Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models, pages 33–40. ACM.
Ferreira, F., Silva, T. H., e Loureiro, A. A. F. (2017). Slkit: An r package for property extraction and analysis of multiple sensing layers. In Computer Networks and Distributed Systems (SBRC), 2017 XXXV Brazilian Symposium on, pages 1184–1191. IEEE.
Helgason, Ó., Kouyoumdjieva, S. T., e Karlsson, G. (2014). Opportunistic communication and human mobility. IEEE Transactions on Mobile Computing, 13(7):1597–1610.
John Lu, Z. Q. (2010). The elements of statistical learning: Data mining, inference, and prediction. Journal of the Royal Statistical Society: Series A (Statistics in Society), 173(3):693–694.
Karamshuk, D., Boldrini, C., Conti, M., e Passarella, A. (2011). Human mobility models for opportunistic networks. IEEE Communications Magazine, 49(12):157–165.
Kim, M., Kotz, D., e Kim, S. (2006). Extracting a mobility model from real user traces. In Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications, pages 1–13.
Kosta, S., Mei, A., e Stefa, J. (2014). Large-scale synthetic social mobile networks with swim. IEEE Transactions on Mobile Computing, 13(1):116–129.
Lee, K., Hong, S., Kim, S. J., Rhee, I., e Chong, S. (2009). Slaw: A new mobility model for human walks. In IEEE INFOCOM 2009, pages 855–863.
Maaten, L. v. d. e Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(Nov):2579–2605.
Mei, A. e Stefa, J. (2009). Swim: A simple model to generate small mobile worlds. In IEEE INFOCOM 2009, pages 2106–2113.
Munjal, A., Camp, T., e Navidi, W. C. (2010). Constructing rigorous manet simulation scenarios with realistic mobility. In Wireless Conference (EW), 2010 European, pages 817–824. IEEE.
Piorkowski, M., Sarajanovic-Djukic, N., e Grossglauser, M. (2009). CRAWDAD dataset ep/mobility (v. 2009-02-24). Downloaded from https://crawdad.org/epfl/mobility/20090224.
Scholz, F. W. (2004). Maximum Likelihood Estimation. John Wiley Sons, Inc.
Song, L. e Kotz, D. F. (2007). Evaluating opportunistic routing protocols with large realistic contact traces. In Proceedings of the second ACM workshop on Challenged networks, pages 35–42. ACM.
Thakur, G. S. e Helmy, A. (2013). Cobra: A framework for the analysis of realistic mobility models. In INFOCOM, 2013 Proceedings IEEE, pages 3351–3356. IEEE.
Yang, D., Zhang, D., Chen, L., e Qu, B. (2015). Nationtelescope: Monitoring and visualizing large-scale collective behavior in lbsns. Journal of Network and Computer Applications, 55:170–180.
Publicado
10/05/2018
Como Citar
SOUZA, Fabrício R.; DOMINGUES, Augusto C. S. A.; MELO, Pedro O. Vaz de; LOUREIRO, Antonio A. F..
MOCHA: Um framework para caracterização e comparação de traces de mobilidade. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 36. , 2018, Campos do Jordão.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 894-906.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2018.2466.
