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Updated: Sep 18, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Age of Information Minimization in Multicarrier-Based Wireless Powered Sensor Networks.

Juan Sun1, Jingjie Xia1, Shubin Zhang2

  • 1School of Computer and Data Engineering, NingboTech University, Ningbo 315100, China.

Entropy (Basel, Switzerland)
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study minimizes information delay in wireless sensor networks using deep reinforcement learning. The novel approach reduces the weighted average Age of Information for better data delivery.

Keywords:
age of informationdeep reinforcement learninglyapunov optimizationwireless energy transfer

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Powered Sensor Networks (WPSNs) face challenges in timely information delivery.
  • Optimizing data transmission and energy transfer is crucial for network efficiency.

Purpose of the Study:

  • To minimize the long-term average weighted sum of the Age of Information (WAoI) in WPSNs.
  • To develop an efficient algorithm for balancing data transmission and energy transfer.

Main Methods:

  • Formulated the problem as a multi-stage stochastic optimization program.
  • Applied Lyapunov optimization to decompose the problem into per-time-block deterministic subproblems.
  • Utilized model-free deep reinforcement learning (DRL) to solve these subproblems.

Main Results:

  • The proposed DRL-based algorithm significantly reduced WAoI compared to existing methods (DQN, greedy algorithms).
  • The algorithm effectively mitigated excessive instantaneous Age of Information (AoI) for individual sensors.
  • Demonstrated superior performance in timely information delivery within WPSNs.

Conclusions:

  • The novel Lyapunov optimization and DRL approach provides an effective solution for minimizing WAoI in WPSNs.
  • This method offers a practical strategy for enhancing real-time monitoring in wireless sensor networks.
  • The findings contribute to advancements in efficient data management for resource-constrained networks.