Autoencoders Assimétricos para a Compressão de Dados IoT
Resumo
Os dispositivos IoT possuem severas limitações em consumo de energia e número de computações locais. Assim, encontrar soluções que diminuam esses dois problemas é sempre bem-vindo. Os dados gerados podem apresentar redundâncias intrínsecas que permitam a sua compressão sem perdas de informação, reduzindo a quantidade de dados transmitidos pela rede, uma das tarefas com maior consumo de energia para dispositivos IoT. Consequentemente, muitas soluções que recorrem a redes neurais têm aparecido para reduzir a transmissão de dados em redes IoT. Este artigo segue essa tendência para propor os Autoencoders Assimetricos (AAEs), que possuem menos camadas de redes neurais no codificador que no decodificador. A estrutura proposta modifica autoencoders típicos com o mesmo número de camadas em ambos o codificador e o decodificador. A ideia chave do projeto assimétrico é minimizar o número de parâmetros armazenados e computações realizadas nos dispositivos IoT. Os experimentos mostraram melhorias em comparação aos autoencoders simétricos, atingindo menores erros de reconstrução usando amostras temporais de um único sensor.
Referências
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Nesterov, Y. E. (1983). A method for solving the convex programming problem with convergence rate o (1/k^ 2). In Dokl. akad. nauk Sssr, volume 269, pages 543-547.
Razzaque, M. A., Bleakley, C., and Dobson, S. (2013). Compression in wireless sensor networks: A survey and comparative evaluation. ACM Transactions on Sensor Networks (TOSN), 10(1):1-44.
Schoellhammer, T., Greenstein, B., Osterweil, E., Wimbrow, M., and Estrin, D. (2004). Lightweight temporal compression of microclimate datasets [wireless sensor networks]. In 29th Annual IEEE International Conference on Local Computer Networks, pages 516-524.
Smith, L. N. (2017). Cyclical learning rates for training neural networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 464-472. IEEE.
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018). Understanding convolution for semantic segmentation. In 2018 IEEE winter conference on applications of computer vision (WACV), pages 1451-1460. Ieee.
Yu, F. and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.
Yu, T., Wang, X., and Shami, A. (2018). Uav-enabled spatial data sampling in large-scale iot systems using denoising autoencoder neural network. IEEE Internet of Things Journal, 6(2):1856-1865.
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). Wireless sensor networks: a survey. Computer networks, 38(4):393-422.
Alsheikh, M. A., Lin, S., Niyato, D., and Tan, H.-P. (2016). Rate-distortion balanced data compression for wireless sensor networks. IEEE Sensors Journal, 16(12):5072-5083.
Bales, R., Cui, G., Rice, R., Meng, X., Zhang, Z., Hartsough, P., Glaser, S., and Conklin, M. (2020). Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO). https://doi.org/10.6071/M39Q2V. Accessed in 08/06/2020.
Bochie, K., Gilbert, M. S., Gantert, L., Barbosa, M. S., Medeiros, D. S., and Campista, M. E. M. (2021). A survey on deep learning for challenged networks: Applications and trends. Journal of Network and Computer Applications, page 103213.
Charte, D., Charte, F., García, S., del Jesus, M. J., and Herrera, F. (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Information Fusion, 44:78-96.
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4):303-314.
Dozat, T. (2016). Incorporating nesterov momentum into adam. In International Conference on Learning Representations (ICLR).
Dumoulin, V. and Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.
Ghosh, A. M. and Grolinger, K. (2019). Deep learning: Edge-cloud data analytics for IoT. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pages 1-7. IEEE.
Gilbert, M. d. S. (2021). Redução de dados em redes de sensores utilizando redes neurais. Projeto Final de Graduação. [link].
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645-1660.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825):357-362.
Iwana, B. K. and Uchida, S. (2021). An empirical survey of data augmentation for time series classification with neural networks. Plos one, 16(7):e0254841.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017). Self-normalizing neural networks. Advances in neural information processing systems, 30:971-980.
LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436-444.
Mohammadi, M., Al-Fuqaha, A., Sorour, S., and Guizani, M. (2018). Deep learning for iot big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4):2923-2960.
Nesterov, Y. E. (1983). A method for solving the convex programming problem with convergence rate o (1/k^ 2). In Dokl. akad. nauk Sssr, volume 269, pages 543-547.
Razzaque, M. A., Bleakley, C., and Dobson, S. (2013). Compression in wireless sensor networks: A survey and comparative evaluation. ACM Transactions on Sensor Networks (TOSN), 10(1):1-44.
Schoellhammer, T., Greenstein, B., Osterweil, E., Wimbrow, M., and Estrin, D. (2004). Lightweight temporal compression of microclimate datasets [wireless sensor networks]. In 29th Annual IEEE International Conference on Local Computer Networks, pages 516-524.
Smith, L. N. (2017). Cyclical learning rates for training neural networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 464-472. IEEE.
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018). Understanding convolution for semantic segmentation. In 2018 IEEE winter conference on applications of computer vision (WACV), pages 1451-1460. Ieee.
Yu, F. and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.
Yu, T., Wang, X., and Shami, A. (2018). Uav-enabled spatial data sampling in large-scale iot systems using denoising autoencoder neural network. IEEE Internet of Things Journal, 6(2):1856-1865.
Publicado
22/05/2023
Como Citar
GILBERT, Mateus da Silva; CAMPOS, Marcello Luiz R. de; CAMPISTA, Miguel Elias M..
Autoencoders Assimétricos para a Compressão de Dados IoT. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 41. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 421-434.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2023.521.
