Continuous Monitoring in Wireless Sensor Networks: A Fuzzy-Probabilistic Approach
Resumo
This work presents and evaluates a fuzzy-probabilistic strategy to save energy in Wireless Sensor Networks (WSNs). The energy savings are obtained with the sensor nodes, no longer sensing and transmitting measurements. In this simple strategy, in each epoch each sensor node transmits its measurement with probability p, and does not transmit with probability (1 p), does not correlate with that of any other sensor node. The task at the sink node, which is to estimate the sensor field at non-sensed points, is solved using fuzzy inference to impute the non-transmitted data followed by regression or interpolation of the sensed scalar field. In this, Nadaraya-Watson regression, regression with Fuzzy Inference and Radial Base Functions Interpolation are compared. The compromise curve between the value of p and the accuracy of the sensor field estimation measured by root mean square error (RMSE) is investigated. When compared to a published linear prediction strategy of the literature, the results show a small loss of performance versus the great simplification of the procedure in the sensor node, making it advantageous in applications that require extremely simple network nodes.
Palavras-chave:
Computational Intelligence, Fuzzy Systems
Referências
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AlShawi, I. S., Yan, L., Pan, W., and Luo, B. (2012). Lifetime enhancement in wireless sensor networks using fuzzy approach and a-star algorithm. IEEE Sensors Journal, 12(10):3010–3018.
Bdiri, S. and Derbel, F. (2015). An ultra-low power wake-up receiver for realtime constrained wireless sensor networks. In Proceedings of the AMA Conferences, Nurenberg, Germany, pages 19–21.
Bouabdallah, N., Rivero-Angeles, M. E., and Sericola, B. (2009). Continuous monitoIEEE ring using event-driven reporting for cluster-based wireless sensor networks. Transactions on Vehicular Technology, 58(7):3460–3479.
de Carvalho, C. G. N., Gomes, D. G., de Souza, J. N., and Agoulmine, N. (2011). Multiple linear regression to improve prediction accuracy in wsn data reduction. In 2011 7th Latin American Network Operations and Management Symposium, pages 1–8. IEEE.
Deligiannakis, A. and Kotidis, Y. (2006). Exploiting spatio-temporal correlations for data processing in sensor networks. In International conference on GeoSensor Networks, pages 45–65. Springer.
Diwakaran, S., Perumal, B., and Devi, K. V. (2019). A cluster prediction model-based data collection for energy efcient wireless sensor network. The Journal of Supercomputing, 75(6):3302–3316.
Ghate, V. V. and Vijayakumar, V. (2018). Machine learning for data aggregation in wsn: A survey. International Journal of Pure and Applied Mathematics, 118(24):1–12.
Haykin, S. (2000). Redes neurais: princípios e prática. Bookman, 2th edition.
Hodge, V. J., O’Keefe, S., Weeks, M., and Moulds, A. (2015). Wireless sensor networks IEEE Transactions on for condition monitoring in the railway industry: A survey. Intelligent Transportation Systems, 3(16):1088–1106.
Jang, J. S. R., Sun, C. T., and Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing—A Computational Approach to Learning and Machine Intelligenc. Prentice-Hall.
Khan, J. A., Qureshi, H. K., and Iqbal, A. (2015). Energy management in wireless sensor networks: A survey. Computers & Electrical Engineering, 41:159–176.
Maia, J. E. B., Brayner, A., and Rodrigues, F. (2013). A framework for processing complex queries in wireless sensor networks. ACM SIGAPP Applied Computing Review, 13(2):30–41.
Matos, T. B., Brayner, A., and Maia, J. E. B. (2010). Towards in-network data prediction in wireless sensor networks. In Proceedings of the 2010 ACM Symposium on Applied Computing, pages 592–596. ACM.
Micchelli, C. A. (1984). Interpolation of scattered data: distance matrices and conditionally positive denite functions. In Approximation theory and spline functions, pages 143–145. Springer.
Narayanan, R. P., Sarath, T. V., and Vineeth, V. V. (2016). Survey on motes used in wireless sensor networks: Perf & parametric anal. Wireless Sensor Network, 8(04):51.
Rault, T., Bouabdallah, A., and Challal, Y. (2014). Energy efciency in wireless sensor networks: A top-down survey. Computer Networks, 67:104–122.
Rawat, P., Singh, K. D., Chaouchi, H., and Bonnin, J. M. (2014). Wireless sensor networks: a survey on recent developments and potential synergies. The Journal of supercomputing, 68(1):1–48.
Durisíc, M. P., Tafa, Z., Dimíc, G., and Milutinovíc, V. (2012). A survey of military In Embedded Computing (MECO), 2012 applications of wireless sensor networks. Mediterranean Conference on, pages 196–199. IEEE.
Vuran, M. C., Akan, ¨O. B., and Akyildiz, I. F. (2004). Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks, 45(3):245–259.
Xiao, H., Lu, C., and Ogai, H. (2017). A new low-power wireless sensor network for realtime bridge health diagnosis system. In Society of Instrument and Control Engineers of Japan (SICE), 2017 56th Annual Conference of the, pages 1565–1568. IEEE.
Yick, J., Mukherjee, B., and Ghosal, D. (2008). Wireless sensor network survey. Computer networks, 52(12):2292–2330.
Zhang, B., Liu, Y., He, J., and Zou, Z. (2013). An energy efcient sampling method through joint linear regression and compressive sensing. In 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), pages 447– 450. IEEE.
AlShawi, I. S., Yan, L., Pan, W., and Luo, B. (2012). Lifetime enhancement in wireless sensor networks using fuzzy approach and a-star algorithm. IEEE Sensors Journal, 12(10):3010–3018.
Bdiri, S. and Derbel, F. (2015). An ultra-low power wake-up receiver for realtime constrained wireless sensor networks. In Proceedings of the AMA Conferences, Nurenberg, Germany, pages 19–21.
Bouabdallah, N., Rivero-Angeles, M. E., and Sericola, B. (2009). Continuous monitoIEEE ring using event-driven reporting for cluster-based wireless sensor networks. Transactions on Vehicular Technology, 58(7):3460–3479.
de Carvalho, C. G. N., Gomes, D. G., de Souza, J. N., and Agoulmine, N. (2011). Multiple linear regression to improve prediction accuracy in wsn data reduction. In 2011 7th Latin American Network Operations and Management Symposium, pages 1–8. IEEE.
Deligiannakis, A. and Kotidis, Y. (2006). Exploiting spatio-temporal correlations for data processing in sensor networks. In International conference on GeoSensor Networks, pages 45–65. Springer.
Diwakaran, S., Perumal, B., and Devi, K. V. (2019). A cluster prediction model-based data collection for energy efcient wireless sensor network. The Journal of Supercomputing, 75(6):3302–3316.
Ghate, V. V. and Vijayakumar, V. (2018). Machine learning for data aggregation in wsn: A survey. International Journal of Pure and Applied Mathematics, 118(24):1–12.
Haykin, S. (2000). Redes neurais: princípios e prática. Bookman, 2th edition.
Hodge, V. J., O’Keefe, S., Weeks, M., and Moulds, A. (2015). Wireless sensor networks IEEE Transactions on for condition monitoring in the railway industry: A survey. Intelligent Transportation Systems, 3(16):1088–1106.
Jang, J. S. R., Sun, C. T., and Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing—A Computational Approach to Learning and Machine Intelligenc. Prentice-Hall.
Khan, J. A., Qureshi, H. K., and Iqbal, A. (2015). Energy management in wireless sensor networks: A survey. Computers & Electrical Engineering, 41:159–176.
Maia, J. E. B., Brayner, A., and Rodrigues, F. (2013). A framework for processing complex queries in wireless sensor networks. ACM SIGAPP Applied Computing Review, 13(2):30–41.
Matos, T. B., Brayner, A., and Maia, J. E. B. (2010). Towards in-network data prediction in wireless sensor networks. In Proceedings of the 2010 ACM Symposium on Applied Computing, pages 592–596. ACM.
Micchelli, C. A. (1984). Interpolation of scattered data: distance matrices and conditionally positive denite functions. In Approximation theory and spline functions, pages 143–145. Springer.
Narayanan, R. P., Sarath, T. V., and Vineeth, V. V. (2016). Survey on motes used in wireless sensor networks: Perf & parametric anal. Wireless Sensor Network, 8(04):51.
Rault, T., Bouabdallah, A., and Challal, Y. (2014). Energy efciency in wireless sensor networks: A top-down survey. Computer Networks, 67:104–122.
Rawat, P., Singh, K. D., Chaouchi, H., and Bonnin, J. M. (2014). Wireless sensor networks: a survey on recent developments and potential synergies. The Journal of supercomputing, 68(1):1–48.
Durisíc, M. P., Tafa, Z., Dimíc, G., and Milutinovíc, V. (2012). A survey of military In Embedded Computing (MECO), 2012 applications of wireless sensor networks. Mediterranean Conference on, pages 196–199. IEEE.
Vuran, M. C., Akan, ¨O. B., and Akyildiz, I. F. (2004). Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks, 45(3):245–259.
Xiao, H., Lu, C., and Ogai, H. (2017). A new low-power wireless sensor network for realtime bridge health diagnosis system. In Society of Instrument and Control Engineers of Japan (SICE), 2017 56th Annual Conference of the, pages 1565–1568. IEEE.
Yick, J., Mukherjee, B., and Ghosal, D. (2008). Wireless sensor network survey. Computer networks, 52(12):2292–2330.
Zhang, B., Liu, Y., He, J., and Zou, Z. (2013). An energy efcient sampling method through joint linear regression and compressive sensing. In 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), pages 447– 450. IEEE.
Publicado
15/10/2019
Como Citar
NUNES, Flávio; MAIA, José.
Continuous Monitoring in Wireless Sensor Networks: A Fuzzy-Probabilistic Approach. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 96-107.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9275.
