Wireless Charger Placement for Directional Charging

Wireless power transfer technology has witnessed huge development because of its convenience and reliability. This paper concerns the fundamental issue of wireless charger PLacement with Optimized charging uTility (PLOT), i.e., given a fixed number of chargers and a set of points where rechargeable...

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Bibliographic Details
Published in:IEEE/ACM transactions on networking Vol. 26; no. 4; pp. 1865 - 1878
Main Authors: Haipeng Dai, Xiaoyu Wang, Liu, Alex X., Huizhen Ma, Guihai Chen, Wanchun Dou
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
approximation algorithm
Approximation algorithms
Charging
Computer simulation
Directional antennas
directional charging
Electronic devices
Electronic equipment
Greedy algorithms
Performance evaluation
Placement
Software
Software algorithms
Wireless communication
wireless power transfer
Wireless power transmission
Wireless sensor networks
ISSN:1063-6692, 1558-2566
Online Access:Get full text
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Summary:Wireless power transfer technology has witnessed huge development because of its convenience and reliability. This paper concerns the fundamental issue of wireless charger PLacement with Optimized charging uTility (PLOT), i.e., given a fixed number of chargers and a set of points where rechargeable devices can be placed with orientations uniformly distributed in the range of [0, 2π) positions and orientations of chargers such that the overall expected charging utility for all points is maximized. To address PLOT, we propose a 1 - 1/ε - ε approximation algorithm. First, we present techniques to approximate the nonlinear charging power and the expected charging utility to make the problem almost linear. Second, we develop a dominating coverage set extraction method to reduce the continuous search space of PLOT to a limited and discrete one without a performance loss. Third, we prove that the reformulated problem is essentially maximizing a monotone submodular function subject to a matroid constraint, and propose a greedy algorithm to address this problem. We conduct both simulation and field experiments to validate our theoretical results, and the results show that our algorithm can outperform comparison algorithms by at least 32.9%.
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ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2018.2855398