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Applying estimation models to accelerate genetic algorithms for charging scheduling problems in wireless rechargeable sensor networks
Author(s): Jingjing Chen; Hong wei Chen; Wu Yu Chang
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Paper Abstract

In this paper, we designed a genetic algorithm based on a new charging path estimation model to obtain an efficient charging scheduling in a wireless rechargeable sensor network. Specifically, we first proposed a charging path estimation model, through which an expected cost of a scheduling charging path can be obtained. Based on this model, a genetic algorithm, which includes a traditional design of chromosome structure, selection, cross-over and mutation operation, supporting the charging scheduling for wireless charging vehicles is devised at the same time. We finally evaluate the performance of the proposed algorithm by extensive simulations. Simulation results show that the proposed algorithm is promising, can improve the performance of wireless rechargeable sensor network.

Paper Details

Date Published: 27 November 2019
PDF: 8 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211M (27 November 2019); doi: 10.1117/12.2549450
Show Author Affiliations
Jingjing Chen, Longyan Univ. (China)
Hong wei Chen, Longyan Univ. (China)
Wu Yu Chang, Chung Hua Univ. (Taiwan)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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