Online Velocity Control and Data Capture of Drones for the Internet of Things: An Onboard Deep Reinforcement Learning Approach

Li, KaiNi, WeiTovar, EduardoJamalipour, Abbas2021-02-2520211556-6080http://hdl.handle.net/10400.22/17146Applications of unmanned aerial vehicles (UAVs) for data collection are a promising means to extend Internet of Things (IoT) networks to remote and hostile areas and to locations where there is no access to power supplies. The adequate design of UAV velocity control and communication decision making is critical to minimize the data packet losses at ground IoT nodes that result from overflowing buffers and transmission failures. However, online velocity control and communication decision making are challenging in UAV-enabled IoT networks, due to a UAV?s lack of up-to-date knowledge about the state of the nodes, e.g., the battery energy, buffer length, and channel conditions.engUnmanned aerial vehicleInternet-of-ThingsVelocity controlData captureDeep reinforcement learningOnline Velocity Control and Data Capture of Drones for the Internet of Things: An Onboard Deep Reinforcement Learning Approachjournal article10.1109/MVT.2020.3039199