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Updated: May 24, 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

Dynamic hierarchical sleep scheduling for wireless ad-hoc sensor networks.

Chih-Yu Wen¹, Ying-Chih Chen

¹Department of Electrical Engineering, Graduate Institute of Communication Engineering, National Chung Hsing University, Taichung 402, Taiwan;

Sensors (Basel, Switzerland)

|March 14, 2012

Summary

This study introduces two wireless sensor network (WSN) scheduling schemes that improve resource allocation and power efficiency. These algorithms enhance network scalability and ensure connectivity and coverage through dynamic cluster-based sleep scheduling.

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless sensor networks (WSNs) face challenges in efficient resource allocation and power management.
Hierarchical network structures offer potential for improved WSN management.
Maintaining sensing coverage and network connectivity is crucial for WSN functionality.

Purpose of the Study:

To propose two novel scheduling management schemes for wireless sensor networks.
To efficiently allocate network resources and manage sensor nodes.
To achieve dynamic cluster-based sleep scheduling while ensuring coverage and connectivity.

Main Methods:

Utilizing a hierarchical network structure for sensor management.
Implementing a local criterion for simultaneous sensing coverage and connectivity establishment.

Keywords:

scheduling management sensing coverage wireless sensor networks

Related Experiment Videos

Last Updated: May 24, 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

Simulating and analyzing network behaviors under various settings.

Main Results:

The proposed schemes demonstrate efficient network power control.
The algorithms achieve high scalability in wireless sensor networks.
Dynamic cluster-based sleep scheduling was successfully implemented.

Conclusions:

The presented scheduling management schemes are effective for wireless sensor networks.
The methods provide efficient power management and enhance network scalability.
The approach ensures both network connectivity and sensing coverage.