  The use of Unmanned Aerial Vehicles (UAVs) in Data transport has attracted a
lot of attention and applications, as a modern traffic engineering technique
used in data sensing, transport, and delivery to where infrastructure is
available for its interpretation. Due to UAVs constraints such as limited power
lifetime, it has been necessary to assist them with ground sensors to gather
local data which has to be transferred to UAVs upon visiting the sensors. The
management of such ground sensor communication together with a team of flying
UAVs constitutes an interesting data muling problem which still deserves to be
addressed and investigated. This paper revisits the issue of traffic
engineering in Internet-of- Things (IoT) settings, to assess the relevance of
using UAVs for the persistent collection of sensor readings from the sensor
nodes located into an environment and their delivery to base stations where
further processing is performed. We propose a persistent path planning and UAV
allocation model, where a team of heterogeneous UAVs coming from various base
stations are used to collect data from ground sensors and deliver the collected
information to their closest base stations. This problem is mathematically
formalised as a real-time constrained optimisation model, and proven to be
NP-hard. The paper proposes a heuristic solution to the problem and evaluates
its relative efficiency through performing experiments on both artificial and
real sensors networks, using various scenarios of UAVs settings.
