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Updated: May 22, 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

Networked estimation for event-based sampling systems with packet dropouts.

Vinh Hao Nguyen¹, Young Soo Suh

¹Department of Electrical Engineering, University of Ulsan, Namgu, Ulsan 680-749, Korea; E-Mail: vinhhao@hcmut.edu.vn (V.H.N.).

Sensors (Basel, Switzerland)

|May 11, 2012

Summary

This summary is machine-generated.

A new modified send-on-delta (SOD) sampling method improves networked estimation by transmitting data when thresholds are met or time intervals elapse. This enhances performance, especially during packet dropouts, outperforming traditional SOD methods.

Keywords:

Networked estimation event-based sampling packet dropout send-on-delta

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Last Updated: May 22, 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
Networked Systems
Signal Processing

Background:

Event-based sampling, such as send-on-delta (SOD), offers bandwidth efficiency over time-triggered systems.
Traditional SOD lacks mechanisms to detect packet dropouts due to the absence of periodic timestamps.
Networked estimation relies on accurate data transmission, which is vulnerable to packet loss.

Purpose of the Study:

To address the packet dropout detection limitation in event-based sampling systems.
To propose a novel modified send-on-delta (SOD) sampling scheme for networked estimation.
To enhance the robustness and performance of estimation in the presence of network uncertainties.

Main Methods:

Introduced a modified SOD (mSOD) sampling scheme incorporating both data change thresholds and time-based triggers.
Implemented and simulated the mSOD scheme within a networked estimation framework.
Compared the estimation performance of mSOD against traditional SOD under simulated packet dropout conditions.

Main Results:

The modified SOD scheme effectively detects and mitigates the impact of packet dropouts.
Simulation results demonstrate significantly improved estimation accuracy with mSOD compared to standard SOD during packet loss.
The proposed mSOD balances data transmission efficiency with enhanced reliability.

Conclusions:

The modified SOD sampling scheme is a viable solution for improving networked estimation reliability under packet dropouts.
This approach offers a practical enhancement for event-based sensing systems operating in lossy networks.
Further research can explore adaptive thresholding and different network conditions for mSOD.