@inproceedings{2d1e3067690e4d3282cfc7f05c51f7e2,
title = "Power Control in Internet of Drones by Deep Reinforcement Learning",
abstract = "Internet of Drones (IoD) employs drones as the internet of things (IoT) devices to provision applications such as traffic surveillance and object tracking. Data collection service is a typical application where multiple drones are deployed to collect information from the ground and send them to the IoT gateway for further processing. The performance of IoD networks is constrained by drones' battery capacities, and hence we utilize both energy harvesting technologies and power control to address this limitation. Specifically, we optimize drones' wireless transmission power at each time epoch in energy harvesting aided time-varying IoD networks for the data collection service with the objective to minimize the average system energy cost. We then formulate a Markov Decision Process (MDP) model to characterize the power control process in dynamic IoD networks, which is then solved by our proposed model-free deep actor-critic reinforcement learning algorithm. The performance of our algorithm is demonstrated via extensive simulations.",
keywords = "Power control, actor-critic, deep reinforcement learning, energy harvesting, internet of drones (IoD), quality of service (QoS)",
author = "Jingjing Yao and Nirwan Ansari",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Communications, ICC 2020 ; Conference date: 07-06-2020 Through 11-06-2020",
year = "2020",
month = jun,
doi = "10.1109/ICC40277.2020.9148749",
language = "English (US)",
series = "IEEE International Conference on Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Communications, ICC 2020 - Proceedings",
address = "United States",
}