Redução de Dados em Redes de Sensores sem Fio Baseada em Análise de Dispersão
Resumo
Nas aplicações de monitoramento com Redes de Sensores sem Fio (RSSF), os sensores podem depender de fontes de energia limitada. Estudos apontam que a principal fonte de consumo de energia em nós sensores está relacionada à transmissão de dados. Neste artigo, apresenta-se uma abordagem para redução de dados baseada na análise da dispersão dos dados de sensores, visando evitar o envio de detecções cujos valores sejam pouco dispersos. Os experimentos realizados com dados de sensores reais e com o simulador Castalia mostraram que a abordagem proposta atingiu uma redução maior que 84%, mantendo um baixo nível de erros e baixo consumo de energia.
Palavras-chave:
RSSF, Análise de Dispersão, Redução de Dados
Referências
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Santini, S. and Romer, K. (2006). An adaptive strategy for quality-based data reduction in wireless sensor networks. In Proceedings of the 3rd international conference on networked sensing systems (INSS 2006), pages 29–36. UK Power Networks (2015). SmartMeter Energy Consumption Data in London Households. https://data:london:gov:uk/dataset/smartmeter-energyuse- data-in-london-households. [Online; acessado em 08/03/2018].
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Willmott, C. J. and Matsuura, K. (2005). Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate research, 30(1):79–82.
Yick, J., Mukherjee, B., and Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12):2292–2330.
Zegarra, E. T., Schouery, R. C. S., Miyazawa, F. K., and Villas, L. A. (2016). A continuous enhancement routing solution aware of data aggregation for wireless sensor networks. In 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA), pages 93–100.
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Castañeda, W. A. C. (2016). Metodologia de gestão ubíqua para tecnologia médico hospitalar utilizando tecnologias pervasivas. PhD thesis, Universidade Federal de Santa Catarina.
Dhand, G. and Tyagi, S. (2016). Data aggregation techniques in WSN:survey. Procedia Computer Science, 92:378–384.
Dias, G. M., Bellalta, B., and Oechsner, S. (2016). Using data prediction techniques to reduce data transmissions in the IoT. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT). IEEE.
El-Telbany, M. E. and Maged, M. A. (2017). Exploiting sparsity in wireless sensor networks for energy saving: A comparative study. International Journal of Applied Engineering Research, 12(4):452–460.
Fathy, Y., Barnaghi, P., and Tafazolli, R. (2018). An adaptive method for data reduction in the internet of things. In Proceedings of IEEE 4th World Forum on Internet of Things. IEEE.
Huang, Z., Li, M., Song, Y., Zhang, Y., and Chen, Z. (2017). Adaptive compressive data gathering for wireless sensor networks. In 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pages 362–367.
Jaber, A., Taam, M. A., Makhoul, A., Jaoude, C. A., Zahwe, O., and Harb, H. (2017). Reducing the data transmission in sensor networks through kruskal-wallis model. In 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE.
Karim, S. (2017). Energy efficiency in wireless sensor networks, through data compression. Master’s thesis, University of Oslo.
Li, Z., Zhang, W., Qiao, D., and Peng, Y. (2017). Lifetime balanced data aggregation for the internet of things. Computers & Electrical Engineering, 58:244–264.
Madden, S. (2004). Intel Lab Data. http://db:lcs:mit:edu/labdata/labdata.html. [Online acessado em 08/03/2018].
Masoum, A., Meratnia, N., and Havinga, P. J. (2013). A distributed compressive sensing technique for data gathering in wireless sensor networks. Procedia Computer Science, 21:207 – 216. The 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2013) and the 3rd International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH).
Queensland Government (2015). Ambient estuarine water quality monitoring data (includes near real-time sites) - 2012 to present day. https: //data:qld:gov:au/dataset/ambient-estuarine-water-qualitymonitoring- data-near-real-time-sites-2012-to-present-day. [Online; acessado em 08/03/2018].
Santini, S. and Romer, K. (2006). An adaptive strategy for quality-based data reduction in wireless sensor networks. In Proceedings of the 3rd international conference on networked sensing systems (INSS 2006), pages 29–36. UK Power Networks (2015). SmartMeter Energy Consumption Data in London Households. https://data:london:gov:uk/dataset/smartmeter-energyuse- data-in-london-households. [Online; acessado em 08/03/2018].
Vito, S. D., Massera, E., Piga, M., Martinotto, L., and Francia, G. D. (2008). On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sensors and Actuators B: Chemical, 129(2):750–757.
Willmott, C. J. and Matsuura, K. (2005). Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate research, 30(1):79–82.
Yick, J., Mukherjee, B., and Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12):2292–2330.
Zegarra, E. T., Schouery, R. C. S., Miyazawa, F. K., and Villas, L. A. (2016). A continuous enhancement routing solution aware of data aggregation for wireless sensor networks. In 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA), pages 93–100.
Zheng, H., Li, J., Feng, X., Guo, W., Chen, Z., and Xiong, N. (2017). Spatialtemporal data collection with compressive sensing in mobile sensor networks. Sensors, 17(11):2575.
Publicado
06/05/2019
Como Citar
DE OLIVEIRA, Samuel Silva; KNIESS, Janine.
Redução de Dados em Redes de Sensores sem Fio Baseada em Análise de Dispersão. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 37. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1-14.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2019.7346.
