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Related Experiment Video

Updated: Mar 31, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms.

Huiwei Wang¹, Xiaofeng Liao², Zidong Wang³

¹College of Electronics and Information Engineering, Southwest University, Chongqing 400715, PR China; Texas A & M University at Qatar, Doha 5825, Qatar.

Neural Networks : the Official Journal of the International Neural Network Society

|October 23, 2015

Summary

This study introduces a robust asynchronous algorithm for parameter estimation in unreliable networks, ensuring reliable data collection even with changing sensors and network issues. The algorithm demonstrates convergence, offering an efficient solution for distributed sensing systems.

Keywords:

Broadcast gossip algorithm Distributed parameter estimation Quantized communication Unreliable sensor networks

Related Experiment Videos

Last Updated: Mar 31, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Distributed Systems
Signal Processing
Networked Sensing

Background:

Traditional parameter estimation algorithms struggle in dynamic and unreliable networks.
Existing methods often face challenges with sensor mobility, link failures, and message interference.
Accurate parameter estimation is crucial for various applications, including environmental monitoring and IoT.

Purpose of the Study:

To develop a novel asynchronous algorithm for unknown parameter estimation.
To ensure robustness against network unreliability, sensor dynamics (joining/leaving), and link failures.
To minimize measurement and quantization errors while avoiding message interference.

Main Methods:

An asynchronous algorithm leveraging stochastic approximation theory.
Decentralized data acquisition with partially informative measurements at each sensor.
Error mitigation techniques for accumulated measurement and quantization errors.

Main Results:

The proposed algorithm demonstrates almost sure convergence to the unknown parameter.
The algorithm effectively tolerates dynamic network conditions and sensor changes.
Numerical examples validate the algorithm's performance and communication efficiency.

Conclusions:

The developed asynchronous algorithm provides a reliable method for parameter estimation in challenging network environments.
It offers a significant improvement over existing methods by handling network unreliability and sensor dynamics.
The algorithm is suitable for large-scale, distributed sensing applications requiring robust estimation.