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Sensors 2017, 17(8), 1918; doi:10.3390/s17081918

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

Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan
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Author to whom correspondence should be addressed.
Received: 14 July 2017 / Revised: 13 August 2017 / Accepted: 17 August 2017 / Published: 20 August 2017
(This article belongs to the Special Issue Wireless Rechargeable Sensor Networks)
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Abstract

This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ, where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority. View Full-Text
Keywords: RF charging; beamforming; antenna array; evolutionary algorithm; evolution strategy RF charging; beamforming; antenna array; evolutionary algorithm; evolution strategy
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MDPI and ACS Style

Yao, K.-H.; Jiang, J.-R.; Tsai, C.-H.; Wu, Z.-S. Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks. Sensors 2017, 17, 1918.

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