Improving data ferrying by iteratively learning the radio frequency environment

A data ferry enables communication in sparse sensor networks by combining physical movement with wireless relaying of data. Optimizing an unmanned aircraft´s motion and communication link scheduling, however, requires knowledge of the communication environments through which it moves. Aspects of the radio frequency environment can be opportunistically learned through the process of communicating with sensor nodes while ferrying, allowing models of the radio environment to be improved. This work analyzes the integration of ferry optimization with using a Gaussian process to learn the radio environment. The unmanned aircraft´s trajectory is initially optimized with an a priori model. After flying one period of the trajectory, RF variations observed by the ferry are used to train a Gaussian process and improve the estimate of the environment. Through this iterative process, ferry performance improves rapidly, achieving 80% of optimal within 4 iterations, and 93% after 9 iterations, as the Gaussian process is able to converge rapidly to the true radio frequency environment.