  Unmanned aerial vehicles (UAVs) are a relatively new technology. Their
application can often involve complex and unseen problems. For instance, they
can work in a cooperative-based environment under the supervision of a ground
station to speed up critical decision-making processes. However, the amount of
information exchanged among the aircraft and ground station is limited by high
distances, low bandwidth size, restricted processing capability, and energy
constraints. These drawbacks restrain large-scale operations such as large area
inspections. New distributed state-of-the-art processing architectures, such as
fog computing, can improve latency, scalability, and efficiency to meet time
constraints via data acquisition, processing, and storage at different levels.
Under these amendments, this research work proposes a mathematical model to
analyze distribution-based UAVs topologies and a fog-cloud computing framework
for large-scale mission and search operations. The tests have successfully
predicted latency and other operational constraints, allowing the analysis of
fog-computing advantages over traditional cloud-computing architectures.
