220401

Internet-of-Things (IoT) devices equipped with temperature and humidity sensors, and cameras are increasingly deployed to monitor remote and human-unfriendly areas, e.g., farmlands, forests, rural highways or electricity infrastructures. Aerial data aggregators, e.g., autonomous drones, provide a promising solution for collecting sensory data of the IoT devices in human-unfriendly environments, enhancing network scalability and connectivity. The flexibility of a drone and favourable line-of-sight connection between the drone and IoT devices can be exploited to improve data reception at the drone. This article first discusses challenges of the drone-assisted data aggregation in IoT networks, such as incomplete network knowledge at the drone, limited buffers of the IoT devices, and lossy wireless channels. Next, we investigate the feasibility of onboard deep reinforcement learning-based solutions to allow a drone to learn its cruise control and data collection schedule online. For deep reinforcement learning in a continuous operation domain, deep deterministic policy gradient (DDPG) is suitable to deliver effective joint cruise control and communication decision, using its outdated knowledge of the IoT devices and network states. A case study shows that the DDPG-based framework can take advantage of the continuous actions to substantially outperform existing non-learning-based alternatives.This work was supported in part by the National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit under Grant UIDP/UIDB/04234/2020, and in part by the National Funds through FCT, under CMU Portugal Partnership under Project CMU/TIC/0022/2019 (CRUAV).