  The emerging drone swarms are capable of carrying out sophisticated tasks in
support of demanding Internet-of-Things (IoT) applications by synergistically
working together. However, the target area may be out of the coverage of the
ground station and it may be impractical to deploy a large number of drones in
the target area due to cost, electromagnetic interference and flight-safety
regulations. By exploiting the innate \emph{agility} and \emph{mobility} of
unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone
swarm network architecture, which is capable of extending the coverage of the
ground station and enhancing the effective end-to-end throughput. Explicitly, a
swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing
and collecting data with the aid of their mounted cameras and/or sensors, and a
powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the
DCDS and a ground station (GS). Given a time period, in order to maximize the
data delivered whilst minimizing the delay imposed, we harness an
$\epsilon$-multiple objective genetic algorithm ($\epsilon$-MOGA) assisted
Pareto-optimization scheme. Our simulation results demonstrate that the
proposed mobile relaying is capable of delivering more data. As specific
examples investigated in our simulations, our mobile relaying-assisted drone
swarm network is capable of delivering $45.38\%$ more data than the benchmark
solutions, when a stationary relay is available, and it is capable of
delivering $26.86\%$ more data than the benchmark solutions when no stationary
relay is available.
