Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks Articles uri icon

authors

  • PEREZ CRUZ, FERNANDO
  • KULKARNI, S. R.

publication date

  • April 2010

start page

  • 355

end page

  • 358

issue

  • 4

volume

  • 17

International Standard Serial Number (ISSN)

  • 1070-9908

Electronic International Standard Serial Number (EISSN)

  • 1558-2361

abstract

  • 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.