Infrastructure-Assisted Cooperative Multi-UAV Deep Reinforcement Energy Trading Learning for Big-Data Processing - Jung, Soyi; Yun, Won Joon; Kim, Joongheon; Kim, Jae Hyun (International Conference on Information Networking, 2021)
This paper proposes a cooperative multi-agent deep reinforcement learning (MADRL) algorithm for energy trading among multiple unmanned aerial vehicles (UAVs) in order to perform big-data processing in a distributed manner. In order to realize UAV-based aerial surveillance or mobile cellular services, seamless and robust wireless charging mechanisms are required for delivering energy sources from charging infrastructure (i.e., charging towers) to UAVs for the consistent operations of the UAVs in the sky. For actively and intelligently managing the charging towers, MADRL-based energy management system (EMS) is proposed and designed for energy trading among the energy storage systems those are equipped with charging towers. If the required energy for charging UAVs is not enough, the purchasing energy from utility company is desired which takes high consts. The main purpose of MADRL-based EMS learning is for minimizing purchasing energy from outside utility company for minimizing operational costs. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance.
Keywords: Aerial surveillance; Cellular services; Charging infrastructures; Charging mechanism; Consistent operation; Data intensive; Energy storage systems; Utility companies; Big-data processing; Deep learning; Multi-agent deep reinforcement learning; Smart grid; Unmanned aerial vehicle (UAV).