Abstract
We focus on the implementation issues of on-line, batch supervised learning in computationally limited devices. As a case study, we consider the use of such techniques for link quality estimation. We compare three strategies for the on-line selection of the data samples to be kept in memory and used for learning. Results suggest that strategies that keep balanced the set of training samples in terms of ranges of target values provide better accuracy and faster convergence.
This research has been partially funded by the Swiss National Science Foundation (SNSF) Sinergia project SWARMIX, project number CRSI22_133059.
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© 2014 Springer International Publishing Switzerland
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Feo-Flushing, E., Kudelski, M., Nagi, J., Gambardella, L.M., Di Caro, G.A. (2014). Poster Abstract: Link Quality Estimation—A Case Study for On-line Supervised Learning in Wireless Sensor Networks. In: Langendoen, K., Hu, W., Ferrari, F., Zimmerling, M., Mottola, L. (eds) Real-World Wireless Sensor Networks. Lecture Notes in Electrical Engineering, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-319-03071-5_12
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DOI: https://doi.org/10.1007/978-3-319-03071-5_12
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