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Updated: Feb 24, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
Statistical-QoS Guaranteed Energy Efficiency Optimization for Energy Harvesting Wireless Sensor Networks
Ya Gao1,2, Wenchi Cheng3, Hailin Zhang4
1State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China. gaoya@stu.xidian.edu.cn.
This study introduces QoS-driven power control policies to maximize effective energy efficiency (EEE) in energy harvesting wireless sensor networks. The developed policies optimize performance for both infinite and finite battery scenarios, ensuring reliable service.
Area of Science:
- Wireless Communication Systems
- Energy Harvesting Technologies
- Network Performance Optimization
Background:
- Battery-powered wireless sensor networks (WSNs) face limitations in lifetime and cost.
- Energy harvesting offers a sustainable power source for WSNs.
- Improving energy efficiency while maintaining Quality of Service (QoS) in energy harvesting WSNs remains a challenge.
Purpose of the Study:
- To develop statistical delay-bounded QoS-driven power control policies for energy harvesting WSNs.
- To maximize effective energy efficiency (EEE), defined as spectrum efficiency per unit harvested energy, under QoS constraints.
- To analyze and validate the performance of the proposed policies.
Main Methods:
- Development of QoS-driven power control policies considering harvested energy.
- Analysis of policy convergence for battery-infinite WSNs (to E-WF and E-CI schemes).
- Analysis of policy behavior for battery-finite WSNs (to T-WF and T-CI schemes).
- Evaluation of outage probabilities for theoretical performance analysis.
Main Results:
- The proposed policies optimize EEE in energy harvesting WSNs.
- For battery-infinite WSNs, policies converge to Energy harvesting Water Filling (E-WF) and Energy harvesting Channel Inversion (E-CI) under different QoS constraints.
- For battery-finite WSNs, policies become Truncated energy harvesting Water Filling (T-WF) and Truncated energy harvesting Channel Inversion (T-CI) under different QoS constraints.
Conclusions:
- The developed QoS-driven power control policies effectively maximize EEE in energy harvesting WSNs.
- The policies provide optimal solutions for various battery capacities and QoS requirements.
- Numerical results confirm the theoretical analysis and the effectiveness of the proposed power control strategies.
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