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Related Experiment Video

Updated: Mar 6, 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

Optimal Power Control in Wireless Powered Sensor Networks: A Dynamic Game-Based Approach.

Haitao Xu¹, Chao Guo², Long Zhang³

¹School of Computer and Communication Engineering, University of Science and Technology Beijing; Beijing 100083, China. alex_xuht@hotmail.com.

Sensors (Basel, Switzerland)

|March 12, 2017

Summary

This summary is machine-generated.

This study introduces a game-based algorithm for optimal uplink transmit power control in wireless powered sensor networks (WPSN). The method balances performance and energy, extending sensor operational hours and meeting Quality of Service requirements.

Keywords:

WPSN differential game power control

Related Experiment Videos

Last Updated: Mar 6, 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
Network Engineering
Game Theory

Background:

Wireless Powered Sensor Networks (WPSN) require efficient uplink transmit power control for balancing throughput and energy scheduling.
Optimizing sensor transmit power is crucial for revenue maximization and extended network lifetime.
Existing methods may not adequately address dynamic power control for Quality of Service (QoS) in WPSN.

Purpose of the Study:

To develop a dynamic game-based algorithm for optimal uplink transmit power control in WPSN.
To ensure sensors operate at optimal power levels for revenue maximization and extended functionality.
To meet Quality of Service (QoS) requirements while managing energy resources effectively.

Main Methods:

Utilizing a non-cooperative differential game framework to model uplink transmit power control.
Applying Bellman dynamic programming to derive Nash equilibrium solutions for optimal power allocation.
Proposing a distributed uplink power control algorithm for practical implementation.

Main Results:

The proposed algorithm achieves optimal uplink transmit power control in WPSN.
The dynamic game-based approach demonstrates convergence towards optimal solutions over an infinite horizon.
Numerical simulations validate the effectiveness of the algorithm in balancing performance and energy.

Conclusions:

The developed dynamic game-based algorithm provides an effective solution for optimal power control in WPSN.
The approach successfully extends sensor operational hours and ensures QoS requirements are met.
The distributed nature of the algorithm facilitates practical deployment in real-world wireless sensor networks.