18 Citations (Scopus)

Abstract

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 trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives a guideline towards 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)
Article number7574375
JournalIEEE Sensors Journal
VolumePP
Issue number99
DOIs
StatePublished - 2016

Fingerprint

Additive noise
Sensor networks
communication
sensors
Communication
estimates
Sensors
Sensor nodes
Parallel algorithms
iteration
estimating
approximation
simulation

Keywords

  • Adaptive Step Size
  • Asymptotic Covariance
  • Bounded Transmissions
  • Max Consensus
  • Soft-max

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Max Consensus in Sensor Networks : Non-linear Bounded Transmission and Additive Noise. / Zhang, Sai; Tepedelenlioglu, Cihan; Banavar, Mahesh K.; Spanias, Andreas.

In: IEEE Sensors Journal, Vol. PP, No. 99, 7574375, 2016.

Research output: Contribution to journalArticle

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AU - Zhang, Sai

AU - Tepedelenlioglu, Cihan

AU - Banavar, Mahesh K.

AU - Spanias, Andreas

PY - 2016

Y1 - 2016

N2 - 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 trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives a guideline towards 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.

AB - 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 trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives a guideline towards 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.

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KW - Bounded Transmissions

KW - Max Consensus

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