Robust and low complexity distributed kernel least squares learning in sensor networks

Fernando Pérez-Cruz, Sanjeev R. Kulkarni

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.

Original languageEnglish (US)
Article number5395679
Pages (from-to)355-358
Number of pages4
JournalIEEE Signal Processing Letters
Issue number4
StatePublished - 2010

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Consensus
  • Distributed learning
  • Kernel methods
  • Sensor networks


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