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Related Experiment Video

Updated: Feb 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

Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks.

Feng Xia¹, Wenhong Zhao², Youxian Sun³

¹Faculty of Information Technology, Queensland University of Technology, Brisbane QLD 4001, Australia. f.xia@ieee.org.

Sensors (Basel, Switzerland)

|September 15, 2017

Summary

This study introduces a fuzzy logic control-based Quality of Service management (FLC-QM) scheme for wireless sensor/actuator networks (WSANs). The FLC-QM scheme ensures reliable data transmission and maintains a desired deadline miss ratio in dynamic environments.

Keywords:

adaptive resource management deadline miss ratio.fuzzy logic control quality of service wireless sensor/actuator network

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Last Updated: Feb 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
Network Engineering

Background:

Wireless sensor/actuator networks (WSANs) are an emerging technology.
Quality of Service (QoS) management in WSANs is an under-researched area.
WSANs often operate in dynamic, unpredictable environments with constrained resources.

Purpose of the Study:

To develop a QoS management scheme for WSANs.
To address the challenges of dynamic traffic load and resource constraints in WSANs.
To maintain a desired level of QoS in WSANs.

Main Methods:

A fuzzy logic control-based QoS management (FLC-QM) scheme was developed.
The FLC-QM utilizes feedback control technology.
A fuzzy logic controller within each sensor node adapts the sampling period based on the deadline miss ratio.

Main Results:

The FLC-QM scheme effectively manages QoS in WSANs.
The scheme maintains the deadline miss ratio at a predetermined desired level.
Simulation results demonstrate the efficacy of the FLC-QM in providing QoS support.

Conclusions:

The FLC-QM scheme offers generality, scalability, and simplicity.
This approach provides robust QoS support for WSANs in challenging environments.
The FLC-QM is a viable solution for ensuring reliable data transmission in WSANs.