Underground wireless charging device deployment algorithm based on grid divisio

GUO Yu; LIU Xiaoming; FENG Kai; DING Enjie; ZHAO Duan

doi:10.13272/j.issn.1671-251x.17366

Volume 44 Issue 12

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Article Navigation > Journal of Mine Automation > 2018 > 44(12): 78-82

GUO Yu, LIU Xiaoming, FENG Kai, et al. Underground wireless charging device deployment algorithm based on grid divisio[J]. Industry and Mine Automation, 2018, 44(12): 78-82. doi: 10.13272/j.issn.1671-251x.17366

Citation:

GUO Yu, LIU Xiaoming, FENG Kai, et al. Underground wireless charging device deployment algorithm based on grid divisio[J]. Industry and Mine Automation, 2018, 44(12): 78-82. doi: 10.13272/j.issn.1671-251x.17366

Citation:

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Underground wireless charging device deployment algorithm based on grid divisio

doi: 10.13272/j.issn.1671-251x.17366

GUO Yu^{1, 3, 4},
LIU Xiaoming^{1, 2},
FENG Kai^{1, 3, 4},
DING Enjie^{1, 3, 4},
ZHAO Duan^{1, 3, 4}

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China
2.
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
3.
Internet of Things(Perception Mine) Research Center, China University of Mining and Technology, Xuzhou 221008, China
4.
The National and Local Joint Engineering Laboratory of Internet Technology on Mine, Xuzhou 221008, China

Publish Date: 2018-12-10

Abstract

Abstract

Aiming at scenario of using wireless charging device to carry out energy transmission for underground wireless rechargeable sensor networks, deployment of charging device was transformed into charging coverage problem, and the optimal charging coverage model was established based on charging model. In order to obtain the approximate optimal solution of the optimal charging coverage model, a wireless charging device deployment algorithm based on grid division was proposed. The optimal position of charging device is determined through grid division and scanning, and the number of charging devices is minimized while charging coverage of all sensor nodes is satisfied. The simulation results show that when the number of sensor nodes is small, grid size has little influence on charging coverage, while charging radius has a great influence on the number of charging devices. When the number of sensor nodes is large, grid size has a certain influence on charging coverage, while charging radius has little influence on the number of charging devices. When charging radius exceeds a certain range, the number of charging devices required is almost constant.