Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/121571
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Type: Conference paper
Title: Delay-tolerant on-demand mobile charging scheduling scheme for wireless rechargeable sensor networks
Author: Chen, Z.
Shen, H.
Zhao, X.
Citation: Proceedings of the 9th International Symposium on Parallel Architectures, Algorithms and Programming, 2019 / vol.2018-December, pp.29-35
Publisher: IEEE
Issue Date: 2019
Series/Report no.: International Symposium on Parallel Architectures Algorithms and Programming
ISBN: 9781538694039
ISSN: 2168-3034
2168-3042
Conference Name: International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (26 Dec 2018 - 28 Dec 2018 : Taipei, Taiwan)
Statement of
Responsibility: 
Zhansheng Chen, Hong Shen, Xiaofan Zhao
Abstract: Wireless sensor networks based on mobile charging vehicle can significantly alleviate the problem of network energy hole and extend network lifetime, but it is faced with how to improve the charging efficiency when the charger's power is constant. In this paper, a delay-tolerant on-demand mobile charging scheduling scheme (DMCSS) is proposed for maximizing the the charging efficiency and preventing nodes from premature failure under the condition of limited mobile charger capacity. The main challenges of our scheme are how to optimize the trajectory and maximize charging efficiency. In DMCSS, we design an adaptive and scalable energy charging strategy for realizing the rational allocation of power resource and devise an efficient next charging node selection algorithm function by considering the angle, the residual energy of RS and the current distance between request RS and MCV. With extensive simulations, we demonstrate that DMCSS scheme not only significantly increases charing efficiency, compared with EDF, NJNP, LADP, and RCSS schemes, but also prevents nodes from premature failure.
Keywords: Wireless rechargeable sensor networks (WRSNs); adaptive scheduling algorithm; delay-tolerant; mobile charging; on-demand energy replenishment
Rights: © 2018 IEEE
RMID: 0030117142
DOI: 10.1109/PAAP.2018.00013
Grant ID: http://purl.org/au-research/grants/arc/DP150104871
Appears in Collections:Computer Science publications

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