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Updated: Jun 14, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements.

Chethaka Jayasekaramudeli1, Alex S Leong2, Alexei T Skvortsov2

  • 1Faculty of Engineering and Information Technology, University of Melbourne, Parkville 3010, Australia.

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|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces two distributed algorithms, measurement diffusion and estimate diffusion, for estimating scalar fields using sensor networks with quantized data. Both methods achieve performance close to centralized estimation, even for time-varying fields.

Keywords:
distributed estimationfield estimationquantized measurementssensor networkstime-varying systems

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Area of Science:

  • Sensor Networks
  • Distributed Estimation
  • Signal Processing

Background:

  • Scalar field estimation is crucial for applications like environmental monitoring.
  • Existing methods often require centralized processing, which can be inefficient for large sensor networks.
  • Quantized sensor measurements introduce challenges in distributed estimation.

Purpose of the Study:

  • To develop and evaluate distributed estimation algorithms for scalar fields using sensor networks with quantized measurements.
  • To propose two novel schemes: measurement diffusion and estimate diffusion.
  • To assess the performance of these algorithms, including their ability to handle time-varying fields.

Main Methods:

  • Distributed estimation algorithms where sensors locally share information with neighbors.
  • Measurement diffusion: sensors broadcast received measurements.
  • Estimate diffusion: sensors broadcast local estimates and Hessians.
  • Iterative combination of information at each sensor.

Main Results:

  • Both measurement diffusion and estimate diffusion schemes effectively estimate scalar fields.
  • The algorithms can handle time-varying scalar fields.
  • Numerical studies show steady-state performance comparable to centralized estimation.

Conclusions:

  • Distributed estimation with quantized sensor data is feasible using the proposed diffusion schemes.
  • These methods offer a viable alternative to centralized estimation, particularly for large-scale sensor networks.
  • The algorithms demonstrate robustness and efficiency in estimating scalar fields.