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Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks. / Li, Kai; Ni, Wei; Wei, Bo et al.
In: IEEE Networking Letters, Vol. 2, No. 2, 30.06.2020, p. 71-75.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Li, K, Ni, W, Wei, B & Tovar, E 2020, 'Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks', IEEE Networking Letters, vol. 2, no. 2, pp. 71-75. https://doi.org/10.1109/LNET.2020.2989130

APA

Li, K., Ni, W., Wei, B., & Tovar, E. (2020). Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks. IEEE Networking Letters, 2(2), 71-75. https://doi.org/10.1109/LNET.2020.2989130

Vancouver

Li K, Ni W, Wei B, Tovar E. Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks. IEEE Networking Letters. 2020 Jun 30;2(2):71-75. Epub 2020 May 22. doi: 10.1109/LNET.2020.2989130

Author

Li, Kai ; Ni, Wei ; Wei, Bo et al. / Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks. In: IEEE Networking Letters. 2020 ; Vol. 2, No. 2. pp. 71-75.

Bibtex

@article{d471e53a10eb46479355fa2396c32f04,
title = "Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks",
abstract = "This letter studies the use of Unmanned Aerial Vehicles (UAVs) in Internet-of-Things (IoT) networks, where the UAV with microwave power transfer (MPT) capability is employed to hover over the area of interest, charging IoT nodes remotely and collecting their data. Scheduling MPT and data transmission is critical to reduce the data packet loss resulting from buffer overflows and channel fading. In practice, the prior knowledge of the battery level and data queue length of the IoT nodes is not available at the UAV. A new onboard double Q-learning scheduling algorithm is proposed to optimally select the IoT node to be interrogated for data collection and MPT along the flight trajectory of the UAV, thereby minimizing asymptotically the packet loss of the IoT networks. Simulations confirm the superiority of our algorithm to Q-learning based alternatives in terms of packet loss and learning efficiency/speed.",
keywords = "Intelligent transportation systems, unmanned vehicles, unmanned aerial vehicles",
author = "Kai Li and Wei Ni and Bo Wei and Eduardo Tovar",
year = "2020",
month = jun,
day = "30",
doi = "10.1109/LNET.2020.2989130",
language = "Undefined/Unknown",
volume = "2",
pages = "71--75",
journal = "IEEE Networking Letters",
issn = "2576-3156",
publisher = "IEEE",
number = "2",

}

RIS

TY - JOUR

T1 - Onboard Double Q-Learning for Airborne Data Capture in Wireless Powered IoT Networks

AU - Li, Kai

AU - Ni, Wei

AU - Wei, Bo

AU - Tovar, Eduardo

PY - 2020/6/30

Y1 - 2020/6/30

N2 - This letter studies the use of Unmanned Aerial Vehicles (UAVs) in Internet-of-Things (IoT) networks, where the UAV with microwave power transfer (MPT) capability is employed to hover over the area of interest, charging IoT nodes remotely and collecting their data. Scheduling MPT and data transmission is critical to reduce the data packet loss resulting from buffer overflows and channel fading. In practice, the prior knowledge of the battery level and data queue length of the IoT nodes is not available at the UAV. A new onboard double Q-learning scheduling algorithm is proposed to optimally select the IoT node to be interrogated for data collection and MPT along the flight trajectory of the UAV, thereby minimizing asymptotically the packet loss of the IoT networks. Simulations confirm the superiority of our algorithm to Q-learning based alternatives in terms of packet loss and learning efficiency/speed.

AB - This letter studies the use of Unmanned Aerial Vehicles (UAVs) in Internet-of-Things (IoT) networks, where the UAV with microwave power transfer (MPT) capability is employed to hover over the area of interest, charging IoT nodes remotely and collecting their data. Scheduling MPT and data transmission is critical to reduce the data packet loss resulting from buffer overflows and channel fading. In practice, the prior knowledge of the battery level and data queue length of the IoT nodes is not available at the UAV. A new onboard double Q-learning scheduling algorithm is proposed to optimally select the IoT node to be interrogated for data collection and MPT along the flight trajectory of the UAV, thereby minimizing asymptotically the packet loss of the IoT networks. Simulations confirm the superiority of our algorithm to Q-learning based alternatives in terms of packet loss and learning efficiency/speed.

KW - Intelligent transportation systems

KW - unmanned vehicles

KW - unmanned aerial vehicles

U2 - 10.1109/LNET.2020.2989130

DO - 10.1109/LNET.2020.2989130

M3 - Journal article

VL - 2

SP - 71

EP - 75

JO - IEEE Networking Letters

JF - IEEE Networking Letters

SN - 2576-3156

IS - 2

ER -