Imperial College London
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DrStevenWright

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Teaching Fellow
 
 
 
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Contact

 

+44 (0)20 7594 6206s.wright02

 
 
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Location

 

1008BElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Pandiyan:2020:10.1109/tii.2020.3035645,
author = {Pandiyan, A and Boyle, D and Kiziroglou, M and Wright, S and Yeatman, E},
doi = {10.1109/tii.2020.3035645},
journal = {IEEE Transactions on Industrial Informatics},
pages = {5719--5729},
title = {Optimal dynamic recharge scheduling for two stage wireless power transfer},
url = {http://dx.doi.org/10.1109/tii.2020.3035645},
volume = {17},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Many Industrial Internet of Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is therefore to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. We describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbour search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve 90% throughput for large, dense networks.
AU  - Pandiyan,A
AU  - Boyle,D
AU  - Kiziroglou,M
AU  - Wright,S
AU  - Yeatman,E
DO  - 10.1109/tii.2020.3035645
EP  - 5729
PY  - 2020///
SN  - 1551-3203
SP  - 5719
TI  - Optimal dynamic recharge scheduling for two stage wireless power transfer
T2  - IEEE Transactions on Industrial Informatics
UR  - http://dx.doi.org/10.1109/tii.2020.3035645
UR  - https://ieeexplore.ieee.org/document/9247485
UR  - http://hdl.handle.net/10044/1/84644
VL  - 17
ER  -
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