  Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing
and estimation in a wide variety of applications including disaster response,
search and rescue, and security monitoring. These sensing UAVs have limited
battery and computational capabilities, and thus must offload their data so it
can be processed to provide actionable intelligence. We consider a compute
platform consisting of a limited number of highly-resourced UAVs that act as
mobile edge computing (MEC) servers to process the workload on premises. We
propose a novel distributed solution to the collaborative processing problem
that adaptively positions the MEC UAVs in response to the changing workload
that arises both from the sensing UAVs' mobility and the task generation. Our
solution consists of two key building blocks: (1) an efficient workload
estimation process by which the UAVs estimate the task field - a continuous
approximation of the number of tasks to be processed at each location in the
airspace, and (2) a distributed optimization method by which the UAVs partition
the task field so as to maximize the system throughput. We evaluate our
proposed solution using realistic models of surveillance UAV mobility and show
that our method achieves up to 28% improvement in throughput over a
non-adaptive baseline approach.
