Compressão de Vocalizações de Anuros para Classificação de Espécies Usando Redes de Sensores Sem Fio
Resumo
Um dos problemas mais comuns em redes de sensores sem fio (RSSFs) é coletar dados suficientes para analisar um determinado fenômeno maximizando o tempo de vida da rede. Utilizando o exemplo de classificação de anuros (sapos e rãs), este trabalho mostra como é possível reduzir a quantidade de amostras coletadas sem comprometer a precisão da classificação. Para isso, é proposta uma metodologia utilizando Compressive Sensing (CS) que permite a reconstrução de sinais parcialmente coletados. Isto é feito utilizando uma base esparsa que represente as informações usadas pelo classificador. Mostramos como é possível manter uma taxa de classificação de cerca de 98% utilizando apenas 10% da informação original, além do impacto desta compressão no consumo de energia, na taxa de entrega e no atraso médio da rede.
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Gowrishankar, S., Basavaraju, T. G., Manjaiah, D. H., and Sarkar, S. K. (2008). Issues in wireless sensor networks. Proceedings of the World Congress on Engineering.
Hempstead, Q. W. and Yang, M. (2006). A realistic power consumption model for wireless sensor network devices. Sensor and Ad Hoc Communications and Networks (SECON).
Hu, W., Tran, V. N., Bulusu, N., Chou, C. T., Jha, S., and Taylor, A. (2005). The design and evaluation of a hybrid sensor network for cane-toad monitoring. Symposium on Information Processing in Sensor Networks.
Huang, J. C., Yang, Y. J., Yang, D. X., and Chen, J. Y. (2009). Frog classification using machine learning techniques. Expert Systems with Applications.
Ji, S. and Carin, L. (2007). Bayesian compressive sensing and projection optimization. International Conference on Machine Learning (ICML).
Ji, S., Dunson, D., and Carin, L. (2009). Multi-task compressive sensing. IEEE Transactions on Signal Processing.
Luo, J., Xiang, L., and Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks? IEEE International Conference on Communications (ICC).
Mainwaring, A., Polastre, J., Szewczyk, R., and Culler, D. (2002). Wireless sensor networks for habitat monitoring. Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications.
Riede, K. (1993). Monitoring biodiversity: analysis of amazonian rainforest sounds. Ambio.
Rocha, V. and Gonçalves, G. (2008). Sensing the world: Challenges on wsns. Automation, Quality and Testing, Robotics. (AQTR).
Taylor, A., Watson, G., Grigg, G., and a. McCallum, H. (1996). Monitoring frog communities: An application of machine learning. Proceedings of the 8th Innovative Applications of Artificial Intelligence Conference (IAAI).
Wang, J., Cho, J., and Lee, S. (2011). An adaptive power-aware multi-hop routing algorithm for wireless sensor networks. Information Technology: New Generations (ITNG).
Baraniuk, R. (2007). Compreesive sensing. IEEE Signal Processing Magazine.
Buluso, N. and Hu, W. (2008). Lightweight acoustic classification for cane toad monitoring. Asimolar Conference on Signals, Systems and Computers.
Candes, E. J. and Waking, M. B. (2008). An introduction to compressed sampling. Signal Processing Magazine.
Carey, C., Heyer, W. R., Wilkinson, J., a. Alford, R., Arntzen, J. W., Halliday, T., Hungerford, L., Lips, K. R., Middleton, E. M., a. Orchard, S., and Rand, S. (2001). Amphibian declines and environmental change: Use of remote-sensing data to identify environmental correlates. Conservation Biology.
Cheng, X., Wang, F., and Liu, J. (2008). Hybrid push-pull for data diffusion in sensor networks without location information. International Conference on Communications (ICC).
Colonna, J. G., Nakamura, E. F., and dos Santos, E. M. (2011). Classificação de anuros baseado em vocalizações para monitoramento ambiental pervasivo. Simpósio Brasileiro de Computação Ubíqua (SBCUP).
Davis, S. and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing.
Fall, K. and Varadhan, K. (2010). The ns manual. [link].
Figueiredo, M. A. T., Nowak, R. D., and Wright, S. J. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing.
Gowrishankar, S., Basavaraju, T. G., Manjaiah, D. H., and Sarkar, S. K. (2008). Issues in wireless sensor networks. Proceedings of the World Congress on Engineering.
Hempstead, Q. W. and Yang, M. (2006). A realistic power consumption model for wireless sensor network devices. Sensor and Ad Hoc Communications and Networks (SECON).
Hu, W., Tran, V. N., Bulusu, N., Chou, C. T., Jha, S., and Taylor, A. (2005). The design and evaluation of a hybrid sensor network for cane-toad monitoring. Symposium on Information Processing in Sensor Networks.
Huang, J. C., Yang, Y. J., Yang, D. X., and Chen, J. Y. (2009). Frog classification using machine learning techniques. Expert Systems with Applications.
Ji, S. and Carin, L. (2007). Bayesian compressive sensing and projection optimization. International Conference on Machine Learning (ICML).
Ji, S., Dunson, D., and Carin, L. (2009). Multi-task compressive sensing. IEEE Transactions on Signal Processing.
Luo, J., Xiang, L., and Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks? IEEE International Conference on Communications (ICC).
Mainwaring, A., Polastre, J., Szewczyk, R., and Culler, D. (2002). Wireless sensor networks for habitat monitoring. Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications.
Riede, K. (1993). Monitoring biodiversity: analysis of amazonian rainforest sounds. Ambio.
Rocha, V. and Gonçalves, G. (2008). Sensing the world: Challenges on wsns. Automation, Quality and Testing, Robotics. (AQTR).
Taylor, A., Watson, G., Grigg, G., and a. McCallum, H. (1996). Monitoring frog communities: An application of machine learning. Proceedings of the 8th Innovative Applications of Artificial Intelligence Conference (IAAI).
Wang, J., Cho, J., and Lee, S. (2011). An adaptive power-aware multi-hop routing algorithm for wireless sensor networks. Information Technology: New Generations (ITNG).
Publicado
16/07/2012
Como Citar
DIAZ, Javier J. M.; COLONNA, Juan G.; SOARES, Rodrigo B.; NAKAMURA, Eduardo F; FIGUEIREDO, Carlos M. S..
Compressão de Vocalizações de Anuros para Classificação de Espécies Usando Redes de Sensores Sem Fio. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 4. , 2012, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2012
.
p. 31-40.
ISSN 2595-6183.
