  The popularity of drones is rapidly increasing across the different sectors
of the economy. Aerial capabilities and relatively low costs make drones the
perfect solution to improve the efficiency of those operations that are
typically carried out by humans (e.g., building inspection, photo collection).
The potential of drone applications can be pushed even further when they are
operated in fleets and in a fully autonomous manner, acting de facto as a drone
swarm. Besides automating field operations, a drone swarm can serve as an
ad-hoc cloud infrastructure built on top of computing and storage resources
available across the swarm members and other connected elements. Even in the
absence of Internet connectivity, this cloud can serve the workloads generated
by the swarm members themselves, as well as by the field agents operating
within the area of interest. By considering the practical example of a
swarm-powered 3D reconstruction application, we present a new optimization
problem for the efficient generation and execution, on top of swarm-powered
ad-hoc cloud infrastructure, of multi-node computing workloads subject to data
geolocation and clustering constraints. The objective is the minimization of
the overall computing times, including both networking delays caused by the
inter-drone data transmission and computation delays. We prove that the problem
is NP-hard and present two combinatorial formulations to model it.
Computational results on the solution of the formulations show that one of them
can be used to solve, within the configured time-limit, more than 50% of the
considered real-world instances involving up to two hundred images and six
drones.
