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

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.