Adaptively Directional Wireless Power Transfer for Large-scale Sensor Networks
Zhe Wang, Lingjie Duan, and Rui Zhang

TL;DR
This paper introduces an adaptive directional wireless power transfer scheme for large sensor networks, optimizing energy delivery by focusing beams based on sensor locations to improve efficiency and network lifetime.
Contribution
It develops a stochastic geometry-based model for adaptive beamforming in WPT, deriving closed-form power distribution metrics and optimizing charging radius for enhanced energy transfer.
Findings
Maximized average received power increases with deployment density and PB transmit power.
Sensor active probability improves with increased PB power and density.
Adaptive directional WPT outperforms traditional omnidirectional methods in efficiency.
Abstract
Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs' locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the closed-form expressions of the distribution metrics of the aggregate received power at a typical SN and further approximate the complementary cumulative distribution function using Gamma distribution with second-order moment matching. To design the charging radius for the optimal AD-WPT operation, we exploit the tradeoff between the power intensity of…
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