In this section
@article{Qiuchen:2021:10.1109/ACCESS.2021.3073076,
author = {Qiuchen, Q and Akshayaa, P and Boyle, D},
doi = {10.1109/ACCESS.2021.3073076},
journal = {IEEE Access},
pages = {59301--59312},
title = {Optimal recharge scheduler for drone-to-sensor wireless power transfer},
url = {http://dx.doi.org/10.1109/ACCESS.2021.3073076},
volume = {9},
year = {2021}
}
TY - JOUR
AB - Wireless recharging by autonomous power delivery vehicles is an attractive maintenance solution for Internet of Things devices. Improving the operating efficiency of power delivery vehicles is challenging due to complex dynamic environments and the need to solve difficult optimization problems to determine the best combination of routes, number of vehicles, and numerous safety thresholds prior to deployment. The optimal recharge scheduling problem considers minimizing discharged energy of drones while maximizing devices’ recharged energy. In this paper, a configurable optimal recharge scheduler is proposed that incorporates several evolutionary and clustering approaches. A modified version of the Black Hole algorithm is presented, which is shown to execute on average 35% faster than the state of the art genetic approach, while delivering comparable performance in simulation across 18 scenarios with varying area and density of sensor nodes deployed under different initialization scenarios.
AU - Qiuchen,Q
AU - Akshayaa,P
AU - Boyle,D
DO - 10.1109/ACCESS.2021.3073076
EP - 59312
PY - 2021///
SN - 2169-3536
SP - 59301
TI - Optimal recharge scheduler for drone-to-sensor wireless power transfer
T2 - IEEE Access
UR - http://dx.doi.org/10.1109/ACCESS.2021.3073076
VL - 9
ER -