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Related Experiment Video

Updated: May 26, 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

Optimization of the sampling periods and the quantization bit lengths for networked estimation.

Young Soo Suh¹, Young Sik Ro, Hee Jun Kang

¹Department of Electrical Engineering, University of Ulsan, Namgu, Ulsan 680-749, Korea. yssuh@ulsan.ac.kr

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

Networked estimation balances data transmission rates with performance. This study optimizes sampling periods and quantization bit lengths for efficient sensor data processing under network constraints.

Keywords:

Kalman filter networked estimation quantization sampling periods

Related Experiment Videos

Last Updated: May 26, 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:

Control Systems Engineering
Signal Processing
Networked Systems

Background:

Networked estimation involves transmitting sensor data over networks with limited bandwidth.
Transmission rates are influenced by sampling periods and quantization bit lengths.
Optimizing these parameters is crucial for maintaining estimation performance.

Purpose of the Study:

To investigate the impact of sampling periods and quantization bit lengths on networked estimation performance.
To develop an algorithm for selecting optimal sampling periods and quantization bit lengths.
To ensure estimation performance while adhering to transmission rate constraints.

Main Methods:

Derivation of an equation to compute estimation performance based on sampling periods and quantization bit lengths.
Development of an optimization algorithm to find the best combination of sampling periods and quantization bit lengths.
Validation through numerical examples.

Main Results:

A method to quantify the relationship between sampling periods, quantization bit lengths, and estimation performance.
An algorithm that successfully identifies parameter combinations meeting transmission rate constraints.
Demonstrated effectiveness of the proposed algorithm in achieving desired estimation performance.

Conclusions:

Sampling periods and quantization bit lengths are critical factors in networked estimation.
The proposed algorithm provides an effective approach to optimize these parameters.
This work contributes to efficient data transmission and reliable estimation in networked systems.