Flying in the dark: controlling autonomous data ferries with partial observations

We seek to support communications in highly-partitioned mobile wireless networks via controllable data ferries. While existing ferry control techniques assume either stationary nodes or complete ferry observation of node locations, we address the more challenging scenario of highly mobile nodes and partial ferry observations. Using the tool of Partially Observable Markov Decision Processes (POMDP), we develop a comprehensive framework where we expand the solution space from predetermined trajectories to policies that can map ferry observations to navigation actions dynamically. Under this framework, we present an optimal and several efficient heuristic policies. We compare the proposed policies with predetermined control through analysis and simulations with respect to multiple node mobility parameters including speed, locality, activeness, and range of movement. The comparisons show a significant performance gain of up to twice the contact rate in cases of high uncertainty. In cases of low uncertainty, we give a sufficient condition under which predetermined control is optimal.