Abstract
With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energy-inference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy.
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References
Baccour, N., Koubaa, A., Mottola, L., Zuniga, M.A., Youssef, H., Boano, C.A., Alves, M.: Radio link quality estimation in wireless sensor networks. ACM Transactions on Sensor Networks 8(4), 1–33 (2012)
Bengio, Y.: Learning deep architectures for ai. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: Data stream mining (May 2011)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag New York, Inc., Secaucus (2006)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Forster, A., Puccinelli, D., Giordano, S.: Sensor node lifetime: An experimental study. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 202–207. IEEE (March 2011)
Liotta, A.: The cognitive NET is coming. IEEE Spectrum 50(8), 26–31 (2013)
Mottola, L., Picco, G.P., Ceriotti, M., Guna, S., Murphy, A.L.: Not all wireless sensor networks are created equal. ACM Transactions on Sensor Networks 7(2), 1–33 (2010)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)
Srinivasan, K., Dutta, P., Tavakoli, A., Levis, P.: An empirical study of low-power wireless. ACM Transactions on Sensor Networks 6(2), 1–49 (2010)
Yano, I.H., Oliveira, V.C., Alberto, E., Fagotto, D.M., Mota, A.D.A., Toledo, L., Mota, M.: Predicting battery charge deplition in Wireless Sensor Networks using received signal strength indicator. Journal of Computer Science 9(7), 821–826 (2013)
Zhou, G., He, T., Krishnamurthy, S., Stankovic, J.A.: Models and solutions for radio irregularity in wireless sensor networks. ACM Transactions on Sensor Networks 2(2), 221–262 (2006)
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Kotian, R., Exarchakos, G., Mocanu, D.C., Liotta, A. (2013). Predicting Battery Depletion of Neighboring Wireless Sensor Nodes. In: Aversa, R., Kołodziej, J., Zhang, J., Amato, F., Fortino, G. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8286. Springer, Cham. https://doi.org/10.1007/978-3-319-03889-6_32
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DOI: https://doi.org/10.1007/978-3-319-03889-6_32
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