Predição de Enlaces em Redes Oportunistas utilizando Redes Neurais Profundas
Resumo
As Redes oportunistas, caracterizadas pela sua natureza móvel e ausência de infraestrutura fixa, apresentam desafios singulares no contexto da predição de enlaces, um problema tradicional em redes de computadores. Embora diversas abordagens tenham sido propostas para solucionar esse problema, a aplicação de tais técnicas em redes oportunistas surge como uma estratégia promissora para otimizar a entrega de pacotes. Dessa forma, este trabalho realiza uma análise aprofundada, comparando métodos de Aprendizado de Máquina empregados na tarefa de predição de enlaces em redes oportunistas. Dentre as abordagens investigadas, incluem-se Redes Neurais Artificiais, Redes Neurais Convolucionais, Redes Neurais Recorrentes e Mecanismos de Atenção. Os experimentos conduzidos utilizam dois conjuntos de dados provenientes de redes oportunistas reais, o ITC e o Infocom06. Pela análise de desempenho dos resultados, foi possível observar que as Redes Neurais Convolucionais utilizando convoluções de duas dimensões são promissoras opções para a predição de enlaces, tendo obtido os valores de 0,9831 para AUC e de 0,7590 para PRAUC nos dados do Infocom06.Referências
Cai, X., Shu, J., and Al-Kali, M. (2018). Link prediction approach for opportunistic networks based on recurrent neural network. IEEE Access, 7:2017–2025.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Faceli, K., Lorena, A. C., Gama, J., Almeida, T. A. d., and Carvalho, A. C. P. d. L. F. d. (2021). Inteligência artificial: uma abordagem de aprendizado de máquina. LTC.
Fu, X., Yao, H., Postolache, O., and Yang, Y. (2019). Message forwarding for wsn-assisted opportunistic network in disaster scenarios. Journal of Network and Computer Applications, 137:11–24.
Galkin, M., Yuan, X., Mostafa, H., Tang, J., and Zhu, Z. (2023). Towards foundation models for knowledge graph reasoning. arXiv preprint arXiv:2310.04562.
Garg, P., Dixit, A., and Sethi, P. (2022). Ml-fresh: novel routing protocol in opportunistic networks using machine learning. Computer Systems Science & Engineering, Forthcoming.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
Liao, Z., Liu, L., and Chen, Y. (2020). A novel link prediction method for opportunistic networks based on random walk and a deep belief network. Ieee Access, 8:16236–16247.
Ma, Y. and Shu, J. (2019). Opportunistic networks link prediction method based on bayesian recurrent neural network. IEEE access, 7:185786–185795.
Schubert, S., Neubert, P., Pöschmann, J., and Protzel, P. (2019). Circular convolutional neural networks for panoramic images and laser data. In 2019 IEEE intelligent vehicles symposium (IV), pages 653–660. IEEE.
Scott, J., Gass, R., Crowcroft, J., Hui, P., Diot, C., and Chaintreau, A. (2022). Crawdad cambridge/haggle (v. 2009-05-29). DOI: 10.15783/C70011. [Online, acesso em 24-agosto-2023].
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c. (2015). Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28.
Shu, J., Li, J., and Zhang, X. (2022a). Link prediction based on 3d convolutional neural network. In 2022 IEEE/CIC International Conference on Communications in China (ICCC), pages 156–161. IEEE.
Shu, J., Shi, J., and Liao, L. (2022b). Link prediction model for opportunistic networks based on feature fusion. IEEE Access, 10:80900–80909.
Tan, C., Gao, Z., Li, S., and Li, S. Z. (2022). Simvp: Towards simple yet powerful spatiotemporal predictive learning. arXiv preprint arXiv:2211.12509.
Tan, C., Li, S., Gao, Z., Guan, W., Wang, Z., Liu, Z., Wu, L., and Li, S. Z. (2023). Openstl: A comprehensive benchmark of spatio-temporal predictive learning. In Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
The PyTorch Foundation (2023). Pytorch. Disponível em [link]. [Online, acesso em 30-novembro-2023].
Wang, T.-H., Huang, H.-J., Lin, J.-T., Hu, C.-W., Zeng, K.-H., and Sun, M. (2018). Omni-directional cnn for visual place recognition and navigation. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 2341–2348. IEEE.
Yemeni, Z., Shu, J., Zhang, X., and Liu, L. (2019). A dbn approach to predict the link in opportunistic networks. In Recent Developments in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2017, pages 575–587. Springer.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Faceli, K., Lorena, A. C., Gama, J., Almeida, T. A. d., and Carvalho, A. C. P. d. L. F. d. (2021). Inteligência artificial: uma abordagem de aprendizado de máquina. LTC.
Fu, X., Yao, H., Postolache, O., and Yang, Y. (2019). Message forwarding for wsn-assisted opportunistic network in disaster scenarios. Journal of Network and Computer Applications, 137:11–24.
Galkin, M., Yuan, X., Mostafa, H., Tang, J., and Zhu, Z. (2023). Towards foundation models for knowledge graph reasoning. arXiv preprint arXiv:2310.04562.
Garg, P., Dixit, A., and Sethi, P. (2022). Ml-fresh: novel routing protocol in opportunistic networks using machine learning. Computer Systems Science & Engineering, Forthcoming.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
Liao, Z., Liu, L., and Chen, Y. (2020). A novel link prediction method for opportunistic networks based on random walk and a deep belief network. Ieee Access, 8:16236–16247.
Ma, Y. and Shu, J. (2019). Opportunistic networks link prediction method based on bayesian recurrent neural network. IEEE access, 7:185786–185795.
Schubert, S., Neubert, P., Pöschmann, J., and Protzel, P. (2019). Circular convolutional neural networks for panoramic images and laser data. In 2019 IEEE intelligent vehicles symposium (IV), pages 653–660. IEEE.
Scott, J., Gass, R., Crowcroft, J., Hui, P., Diot, C., and Chaintreau, A. (2022). Crawdad cambridge/haggle (v. 2009-05-29). DOI: 10.15783/C70011. [Online, acesso em 24-agosto-2023].
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c. (2015). Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28.
Shu, J., Li, J., and Zhang, X. (2022a). Link prediction based on 3d convolutional neural network. In 2022 IEEE/CIC International Conference on Communications in China (ICCC), pages 156–161. IEEE.
Shu, J., Shi, J., and Liao, L. (2022b). Link prediction model for opportunistic networks based on feature fusion. IEEE Access, 10:80900–80909.
Tan, C., Gao, Z., Li, S., and Li, S. Z. (2022). Simvp: Towards simple yet powerful spatiotemporal predictive learning. arXiv preprint arXiv:2211.12509.
Tan, C., Li, S., Gao, Z., Guan, W., Wang, Z., Liu, Z., Wu, L., and Li, S. Z. (2023). Openstl: A comprehensive benchmark of spatio-temporal predictive learning. In Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
The PyTorch Foundation (2023). Pytorch. Disponível em [link]. [Online, acesso em 30-novembro-2023].
Wang, T.-H., Huang, H.-J., Lin, J.-T., Hu, C.-W., Zeng, K.-H., and Sun, M. (2018). Omni-directional cnn for visual place recognition and navigation. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 2341–2348. IEEE.
Yemeni, Z., Shu, J., Zhang, X., and Liu, L. (2019). A dbn approach to predict the link in opportunistic networks. In Recent Developments in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2017, pages 575–587. Springer.
Publicado
24/05/2024
Como Citar
THIAGO, Vinícius S.; SALLES, Ronaldo M.; DUARTE, Julio Cesar; DIAS, Gabriela M. S..
Predição de Enlaces em Redes Oportunistas utilizando Redes Neurais Profundas. In: WORKSHOP DE TESTES E TOLERÂNCIA A FALHAS (WTF), 25. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 29-42.
ISSN 2595-2684.
DOI: https://doi.org/10.5753/wtf.2024.2834.
