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Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks.

Ke-Han Yao1, Jehn-Ruey Jiang2, Chung-Hsien Tsai3

  • 1Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan. falcoyao@gmail.com.

Sensors (Basel, Switzerland)
|August 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces two evolutionary algorithms (EBO and EBO-R) for efficient wireless rechargeable sensor network (WRSN) charging using radio frequency (RF) beamforming. These methods optimize energy transmission for sustainable WRSN operation.

Keywords:
RF chargingantenna arraybeamformingevolution strategyevolutionary algorithm

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Wireless Communication

Background:

  • Wireless rechargeable sensor networks (WRSNs) require efficient energy management for sustainability.
  • Radio frequency (RF) chargers with uniform circular arrays (UCAs) can direct energy using beamforming technology.
  • Optimizing beamforming parameters is crucial for maximizing charging efficiency.

Purpose of the Study:

  • To propose novel evolutionary algorithms for efficient energy charging in WRSNs.
  • To optimize the power ratio between the main lobe (ML) and peak side lobe (PSL) in UCA beamforming.
  • To enhance the sustainability of WRSNs through improved RF energy transfer.

Main Methods:

  • Development of the Evolutionary Beamforming Optimization (EBO) algorithm.
  • Development of the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, based on evolution strategy (ES).
  • Simulation and comparative analysis against the Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore).

Main Results:

  • The proposed EBO and EBO-R algorithms demonstrate superior performance in optimizing UCA beamforming.
  • Effective steering of RF energy towards target WRSN nodes is achieved.
  • The algorithms show significant improvements in energy transmission efficiency compared to existing methods.

Conclusions:

  • EBO and EBO-R offer efficient solutions for charging sensor nodes in WRSNs.
  • The proposed algorithms contribute to the development of sustainable wireless sensor networks.
  • Optimized beamforming is key to maximizing energy transfer in RF-powered WRSNs.