Chapter

Real-World Wireless Sensor Networks

Volume 281 of the series Lecture Notes in Electrical Engineering pp 97-101

Date:

Poster Abstract: Link Quality Estimation—A Case Study for On-line Supervised Learning in Wireless Sensor Networks

  • Eduardo Feo-FlushingAffiliated withDalle Molle Institute for Artificial Intelligence (IDSIA) Email author 
  • , Michal KudelskiAffiliated withDalle Molle Institute for Artificial Intelligence (IDSIA)
  • , Jawad NagiAffiliated withDalle Molle Institute for Artificial Intelligence (IDSIA)
  • , Luca M. GambardellaAffiliated withDalle Molle Institute for Artificial Intelligence (IDSIA)
  • , Gianni A. Di CaroAffiliated withDalle Molle Institute for Artificial Intelligence (IDSIA)

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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.