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Updated: Apr 28, 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

Distributed optimal power and rate control in wireless sensor networks.

Meiqin Tang¹, Jianyong Bai², Jing Li³

¹School of Mathematics and Statistics, Ludong University, Yantai 264025, China.

Thescientificworldjournal

|June 5, 2014

Summary

This study introduces a power and rate control model for wireless sensor networks to reduce energy consumption and extend node lifetime. The proposed method enhances network throughput by optimizing power and transmission rates.

Related Experiment Videos

Last Updated: Apr 28, 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:

Wireless communication networks
Optimization theory
Computer engineering

Background:

Reducing energy consumption is critical for extending the lifetime of nodes in wireless sensor networks.
Limited energy availability necessitates adjustments in node transmission power.
Interference between nodes can cause signal transmission failures and reduce network throughput.

Purpose of the Study:

To propose a power and rate control model for wireless sensor networks based on the network utility maximization (NUM) framework.
To address the trade-off between sending power and transmission rate using a weighting factor in the utility function.
To mitigate the negative impacts of node interference on network throughput.

Main Methods:

Utilized the network utility maximization (NUM) framework for power and rate control.
Employed dual decomposition techniques to break down the NUM problem into two distributed subproblems.
Applied the conjugate gradient method, including Hessian matrix calculation and inversion, for efficient optimization.
Provided a convergence proof for the proposed algorithm.

Main Results:

The proposed power and rate control model effectively manages energy consumption and transmission rates.
The dual decomposition and conjugate gradient methods ensure fast convergence of the optimization algorithm.
Numerical examples demonstrate significant throughput improvements compared to existing approaches.
The weighting factor successfully balances the influence of sending power and transmission rate on network utility.

Conclusions:

The developed power and rate control model offers an effective solution for enhancing wireless sensor network performance.
The optimization techniques employed guarantee efficient and convergent solutions for practical network scenarios.
The approach successfully improves network throughput while managing energy constraints.
This work contributes to the advancement of energy-efficient and high-throughput wireless sensor networks.