Energy conservation in sensor network data ferrying: A reinforcement metalearning approach

Given multiple widespread stationary data sources such as ground-based sensors, an unmanned aircraft can fly over the sensors and gather the data via a wireless link. When sensors have limited energy resources, network lifetime can be extended by reducing the power that the sensors use for communication with the aircraft. Complex vehicle and communication dynamics and imperfect knowledge of the environment make accurate system models difficult to acquire and maintain, so we present a reinforcement learning approach that allows the data-ferrying aircraft to optimize data collection trajectories and sensor power use in situ, obviating the need for system identification. By allowing the ferry aircraft to fly longer trajectories, we learn energy-conserving radio transmission policies that are significantly better than hand-coded heuristics. Furthermore, we introduce a meta-level reinforcement learner that makes energy policy learning faster and more robust, and that transfers knowledge acquired in earlier tasks to new data-ferrying tasks.