  Efficient data processing and computation are essential for the industrial
Internet of things (IIoT) to empower various applications, which yet can be
significantly bottlenecked by the limited energy capacity and computation
capability of the IIoT nodes. In this paper, we employ an unmanned aerial
vehicle (UAV) as an edge server to assist IIoT data processing, while
considering the practical issue of UAV jittering. Specifically, we propose a
joint design on trajectory and offloading strategies to minimize energy
consumption due to local and edge computation, as well as data transmission. We
particularly address the UAV jittering that induces Gaussian-distributed
uncertainties associated with flying waypoints, resulting in probabilistic-form
flying speed and data offloading constraints. We exploit the Bernstein-type
inequality to reformulate the constraints in deterministic forms and decompose
the energy minimization to solve for trajectory and offloading separately
within an alternating optimization framework. The subproblems are then tackled
with the successive convex approximation technique. Simulation results show
that our proposal strictly guarantees robustness under uncertainties and
effectively reduces energy consumption as compared with the baselines.
