Power Control in Internet of Drones by Deep Reinforcement Learning

Document Type

Conference Proceeding

Publication Date

6-1-2020

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.

Identifier

85089414305 (Scopus)

ISBN

[9781728150895]

Publication Title

IEEE International Conference on Communications

External Full Text Location

https://doi.org/10.1109/ICC40277.2020.9148749

ISSN

15503607

Volume

2020-June

Grant

1814748

Fund Ref

National Science Foundation

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