Algorithm for data reduction in sensor networks based on Information Theory
Abstract
This work proposes a data flow reduction algorithm based on the behavior of time series in the Complexity-Entropy plane for wireless sensor networks (WSNs). The system dynamic variation is identified in real time th- rough a delimiter built into the plane, called the Maximum Complexity Cut-off Point. Thus, we can determine at which instants the sample interval must be updated in order to maximize the statistical complexity of the resulting data sample. This method was applied to a chaotic database and the obtained re- sults were compared with those of other sampling algorithms, presenting better performance in the statistical metrics evaluated.
Keywords:
Data reduction, Sampling, Wireless Sensor Networks, Information Theory
References
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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.
Published
2019-07-12
How to Cite
JÚNIOR, Givanildo; FREITAS, Cristopher ; ROSSO, Osvaldo ; AQUINO, André .
Algorithm for data reduction in sensor networks based on Information Theory. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 11. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2019.6598.
