  This paper introduces a testbed to study distributed sensing problems of
Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart
City applications, such as transport and disaster response, require efficient
collection of sensor data by a swarm of intelligent and cooperative UAVs. This
often proves to be too complex and costly to study systematically and
rigorously without compromising scale, realism and external validity. With the
proposed testbed, this paper sets a stepping stone to emulate, within small
laboratory spaces, large sensing areas of interest originated from empirical
data and simulation models. Over this sensing map, a swarm of low-cost drones
can fly allowing the study of a large spectrum of problems such as energy
consumption, charging control, navigation and collision avoidance. The
applicability of a decentralized multi-agent collective learning algorithm
(EPOS) for UAV swarm intelligence along with the assessment of power
consumption measurements provide a proof-of-concept and validate the accuracy
of the proposed testbed.
