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Updated: May 23, 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

An efficient and self-adapting localization in static wireless sensor networks.

Guodong Teng¹, Kougen Zheng, Wei Dong

¹College of Computer Science, Zhejiang University, Hangzhou, 310027, China; E-Mails: teng@zju.edu.cn (G. T.); dongw@zju.edu.cn (D. W.).

Sensors (Basel, Switzerland)

|March 29, 2012

Summary

This summary is machine-generated.

This study enhances wireless sensor network localization by optimizing particle filter prediction and update stages. A new Self-Adapting Mobile Beacon-assisted Localization (SA-MBL) method offers improved flexibility and accuracy.

Keywords:

Self-Adapting Mobile Beacon-assisted Localization (SA-MBL)Wireless Sensor Networks (WSNs)localization particle filter

Related Experiment Videos

Last Updated: May 23, 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:

Computer Science
Electrical Engineering
Network Engineering

Background:

Localization is critical in Wireless Sensor Networks (WSNs).
Existing mobile beacon-assisted localization (MBL) methods face challenges with dynamic models and neighbor data utilization.
Particle filter-based approaches like MBL and A-MBL require refinement for optimal performance.

Purpose of the Study:

To address key challenges in mobile beacon-assisted localization within WSNs.
To determine optimal probability distributions for dynamic models in the prediction stage.
To establish conditions for utilizing neighbor observations in the update stage for enhanced accuracy.

Main Methods:

Theoretical analysis and experimental evaluations of different probability distributions for dynamic models.
Investigation into the conditions under which nodes should incorporate neighbor observations.
Development and proposal of a Self-Adapting Mobile Beacon-assisted Localization (SA-MBL) approach.

Main Results:

Identification of suitable probability distributions to improve prediction efficiency in WSN localization.
Determination of criteria for leveraging neighbor observations to enhance localization accuracy.
The proposed SA-MBL approach demonstrates significant flexibility and performance comparable to existing advanced methods like A-MBL.

Conclusions:

Optimizing dynamic models and neighbor data integration are crucial for efficient WSN localization.
The SA-MBL approach provides a flexible and effective solution for mobile beacon-assisted localization.
Further research can build upon these findings to advance WSN localization technologies.