  Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is
a prominent concept, where a UAV equipped with a MEC server is deployed to
serve a number of terminal devices (TDs) of Internet of Things (IoT) in a
finite period. In this paper, each TD has a certain latency-critical
computation task in each time slot to complete. Three computation strategies
can be available to each TD. First, each TD can operate local computing by
itself. Second, each TD can partially offload task bits to the UAV for
computing. Third, each TD can choose to offload task bits to access point (AP)
via UAV relaying. We propose a new optimization problem formulation that aims
to minimize the total energy consumption including communication-related
energy, computation-related energy and UAV's flight energy by optimizing the
bits allocation, time slot scheduling and power allocation as well as UAV
trajectory design. As the formulated problem is non-convex and difficult to
find the optimal solution, we solve the problem by two parts, and obtain the
near optimal solution with within a dozen of iterations. Finally, numerical
results are given to validate the proposed algorithm, which is verified to be
efficient and superior to the other benchmark cases.
