A distributed consensus algorithm for estimating the maximum and the minimum of the initial measurements in a sensor network is proposed. Estimating extrema is useful in many applications such as temperature control. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate may incorrectly drift to a larger value at each iteration. As a result, a soft-max approach together with a consensus algorithm is introduced herein. Soft-min based algorithm is also described using the same approach. It is shown that for some distributions of the initial measurements, a modified soft-min consensus can also be used to calculate the max. A shifted non-linear bounded transmit function is also introduced to improve the convergence speed. A trade-off between power of the transmitted signal and the error in the estimate is described and simulation results are provided.

Original languageEnglish (US)
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Number of pages5
ISBN (Print)9781479923908
StatePublished - 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other2013 47th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove, CA

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


Dive into the research topics of 'Max-consensus using the soft maximum'. Together they form a unique fingerprint.

Cite this