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

• Autor(a) principal: Li, Kai
• Data de Publicação: 2021
• Outros Autores: Ni, Wei, Tovar, Eduardo, Jamalipour, Abbas
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/17146
• topic: Unmanned aerial vehicle; Internet-of-Things; Velocity control; Data capture; Deep reinforcement learning

Applications 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.