Algoritmo para redução de dados em redes de sensores baseado em Teoria da Informação
Resumo
Este trabalho propõe um algoritmo de redução do fluxo de dados baseado no comportamento de séries temporais no plano Complexidade-Entropia para redes de sensores sem fio (RSSF). A variação da dinâmica do sistema é identificada em tempo real através de um delimitador construı́do dentro do plano, denominado Ponto de Corte de Complexidade Máxima. Assim, podemos determinar em quais instantes se deve atualizar o intervalo de amostragem, de modo a maximizar a complexidade estatı́stica da amostra de dados resultante. Este método foi aplicado a uma base de dados caóticos e os resultados obtidos foram comparados com os de outros algoritmos de amostragem, apresentando melhor desempenho nas métricas de estatı́stica avaliadas.
Palavras-chave:
Data reduction, Sampling, Wireless Sensor Networks, Information Theory
Referências
Aquino, A. L., Oliveira, R. A., and Wanner, E. F. (2010). A wavelet-based sampling algorithm for wireless sensor networks applications. In Proceedings of the 2010 ACM Symposium on Applied Computing, pages 1604–1608. ACM.
Arampatzis, T., Lygeros, J., and Manesis, S. (2005). A survey of applications of wireless sensors and wireless sensor networks. In Proceedings of the 2005 IEEE Internati- onal Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005., pages 719–724. IEEE.
Bandt, C. and Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17):174102.
Corder, G. W. and Foreman, D. I. (2009). Nonparametric statistics for non-statisticians: a step-by-step approach. John Wiley & Sons.
Daniel, W. W. et al. (1978). Applied nonparametric statistics. Houghton Mifflin.
de Aquino, A. L., Figueiredo, C. M. S., Nakamura, E. F., Buriol, L. S., Loureiro, A. A. F., Fernandes, A. O., and Coelho Jr, C. J. N. (2007). A sampling data stream algorithm forwireless sensor networks. In 2007 IEEE International Conference on Communications, pages 3207–3212. IEEE.
Eisencraft, M., Attux, R., and Suyama, R. (2018). Chaotic signals in digital communica- tions. CRC Press.
Feldman, D. P., McTague, C. S., and Crutchfield, J. P. (2008). The organization of intrinsic computation: Complexity-entropy diagrams and the diversity of natural information processing. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18(4):043106.
Hayes, S., Grebogi, C., and Ott, E. (1993). Communicating with chaos. Physical review letters, 70(20):3031.
Jain, S. and Chawla, M. (2014). Survey of buffer management policies for delay tolerant networks. The Journal of Engineering, 2014(3):117–123.
Kocarev, L., Makraduli, J., and Amato, P. (2005). Public-key encryption based on chebyshev polynomials. Circuits, Systems and Signal Processing, 24(5):497–517.
Lamberti, P., Martin, M., Plastino, A., and Rosso, O. (2004). Intensive entropic non- triviality measure. Physica A: Statistical Mechanics and its Applications, 334(1- 2):119–131.
Lins, A., Nakamura, E. F., Loureiro, A. A., and Coelho, C. J. (2003). Beanwatcher: a tool to generate multimedia monitoring applications for wireless sensor networks. In IFIP/IEEE International Conference on Management of Multimedia Networks and Services, pages 128–141. Springer.
Lopez-Ruiz, R., Mancini, H. L., and Calbet, X. (1995). A statistical measure of comple- xity. Physics Letters A, 209(5-6):321–326.
Masmoudi, A. and Puech, W. (2014). Lossless chaos-based crypto-compression scheme for image protection. IET Image Processing, 8(12):671–686.
Muthukrishnan, S. et al. (2005). Data streams: Algorithms and applications. Foundations and Trends R in Theoretical Computer Science, 1(2):117–236.
Rosso, O., Larrondo, H., Martin, M., Plastino, A., and Fuentes, M. (2007). Distinguishing noise from chaos. Physical review letters, 99(15):154102.
Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3):379–423.
Sprott, J. C. and Sprott, J. C. (2003). Chaos and time-series analysis, volume 69. Citeseer.
Vasconcelos, I. L., Lima, D. H., Figueiredo, C. M., and Aquino, A. L. (2015). A sampling algorithm for intermittently connected delay tolerant wireless sensor networks. In 2015 IEEE Symposium on Computers and Communication (ISCC), pages 538–543. IEEE.
Arampatzis, T., Lygeros, J., and Manesis, S. (2005). A survey of applications of wireless sensors and wireless sensor networks. In Proceedings of the 2005 IEEE Internati- onal Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005., pages 719–724. IEEE.
Bandt, C. and Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17):174102.
Corder, G. W. and Foreman, D. I. (2009). Nonparametric statistics for non-statisticians: a step-by-step approach. John Wiley & Sons.
Daniel, W. W. et al. (1978). Applied nonparametric statistics. Houghton Mifflin.
de Aquino, A. L., Figueiredo, C. M. S., Nakamura, E. F., Buriol, L. S., Loureiro, A. A. F., Fernandes, A. O., and Coelho Jr, C. J. N. (2007). A sampling data stream algorithm forwireless sensor networks. In 2007 IEEE International Conference on Communications, pages 3207–3212. IEEE.
Eisencraft, M., Attux, R., and Suyama, R. (2018). Chaotic signals in digital communica- tions. CRC Press.
Feldman, D. P., McTague, C. S., and Crutchfield, J. P. (2008). The organization of intrinsic computation: Complexity-entropy diagrams and the diversity of natural information processing. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18(4):043106.
Hayes, S., Grebogi, C., and Ott, E. (1993). Communicating with chaos. Physical review letters, 70(20):3031.
Jain, S. and Chawla, M. (2014). Survey of buffer management policies for delay tolerant networks. The Journal of Engineering, 2014(3):117–123.
Kocarev, L., Makraduli, J., and Amato, P. (2005). Public-key encryption based on chebyshev polynomials. Circuits, Systems and Signal Processing, 24(5):497–517.
Lamberti, P., Martin, M., Plastino, A., and Rosso, O. (2004). Intensive entropic non- triviality measure. Physica A: Statistical Mechanics and its Applications, 334(1- 2):119–131.
Lins, A., Nakamura, E. F., Loureiro, A. A., and Coelho, C. J. (2003). Beanwatcher: a tool to generate multimedia monitoring applications for wireless sensor networks. In IFIP/IEEE International Conference on Management of Multimedia Networks and Services, pages 128–141. Springer.
Lopez-Ruiz, R., Mancini, H. L., and Calbet, X. (1995). A statistical measure of comple- xity. Physics Letters A, 209(5-6):321–326.
Masmoudi, A. and Puech, W. (2014). Lossless chaos-based crypto-compression scheme for image protection. IET Image Processing, 8(12):671–686.
Muthukrishnan, S. et al. (2005). Data streams: Algorithms and applications. Foundations and Trends R in Theoretical Computer Science, 1(2):117–236.
Rosso, O., Larrondo, H., Martin, M., Plastino, A., and Fuentes, M. (2007). Distinguishing noise from chaos. Physical review letters, 99(15):154102.
Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3):379–423.
Sprott, J. C. and Sprott, J. C. (2003). Chaos and time-series analysis, volume 69. Citeseer.
Vasconcelos, I. L., Lima, D. H., Figueiredo, C. M., and Aquino, A. L. (2015). A sampling algorithm for intermittently connected delay tolerant wireless sensor networks. In 2015 IEEE Symposium on Computers and Communication (ISCC), pages 538–543. IEEE.
Publicado
12/07/2019
Como Citar
JÚNIOR, Givanildo; FREITAS, Cristopher ; ROSSO, Osvaldo ; AQUINO, André .
Algoritmo para redução de dados em redes de sensores baseado em Teoria da Informação. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 11. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2019.6598.
