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On Consensus-Based Distributed Blind Calibration of Sensor Networks.

Miloš S Stanković1,2,3, Srdjan S Stanković4,5, Karl Henrik Johansson6

  • 1Innovation Center, School of Electrical Engineering, University of Belgrade, 11120 Belgrade, Serbia. milstank@gmail.com.

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Summary
This summary is machine-generated.

Decentralized algorithms enable real-time, blind macro-calibration of sensor networks without a central fusion center. These algorithms achieve consensus on sensor gains and offsets, even with noise and communication issues.

Keywords:
blind calibrationconsensusdistributed estimationmacro calibrationsensor networksstochastic approximationsynchronization

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Area of Science:

  • Signal Processing
  • Control Systems
  • Networked Systems

Background:

  • Sensor networks require accurate calibration for reliable data.
  • Existing calibration methods often rely on centralized approaches, which can be inefficient and vulnerable.
  • Distributed, decentralized methods are needed for robust, scalable sensor network calibration.

Purpose of the Study:

  • To consolidate and unify existing results on distributed blind macro-calibration algorithms for sensor networks.
  • To provide theoretical and practical analysis of designing and applying these algorithms.
  • To address challenges like noise, communication dropouts, and asynchronous communication.

Main Methods:

  • Development of a basic algorithm enforcing asymptotic consensus on sensor gains and offsets.
  • Introduction of two modified algorithms to handle measurement noise, communication dropouts, and noise using compensation terms and instrumental variables.
  • Analysis of convergence rates and asynchronous communication scenarios.

Main Results:

  • The basic and modified algorithms achieve asymptotic consensus in the mean-square sense and with probability one (w.p.1).
  • Convergence rates are bounded by the mean-square error.
  • Algorithms are effective even with asynchronous communication and a subset of reference sensors.

Conclusions:

  • The presented algorithms offer a robust and efficient solution for real-time distributed blind macro-calibration of sensor networks.
  • The methodology is applicable in realistic scenarios with noise and communication imperfections.
  • Further research directions include exploring open questions in algorithm design and application.