Max Consensus in Sensor Networks: Non-Linear Bounded Transmission and Additive Noise

Sai Zhang, Cihan Tepedelenlioglu, Mahesh K. Banavar, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. 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 will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation together with a non-linear consensus algorithm is introduced herein. A design parameter controls the tradeoff between the soft-max error and convergence speed. An analysis of this tradeoff gives a guideline toward how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded transmit function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.

Original languageEnglish (US)
Pages (from-to)9089-9098
Number of pages10
JournalIEEE Sensors Journal
Issue number24
StatePublished - Dec 15 2016


  • Max consensus
  • adaptive step size
  • asymptotic covariance
  • bounded transmissions
  • soft-max

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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