  The empowering unmanned aerial vehicles (UAVs) have been extensively used in
providing intelligence such as target tracking. In our field experiments, a
pre-trained convolutional neural network (CNN) is deployed at the UAV to
identify a target (a vehicle) from the captured video frames and enable the UAV
to keep tracking. However, this kind of visual target tracking demands a lot of
computational resources due to the desired high inference accuracy and
stringent delay requirement. This motivates us to consider offloading this type
of deep learning (DL) tasks to a mobile edge computing (MEC) server due to
limited computational resource and energy budget of the UAV, and further
improve the inference accuracy. Specifically, we propose a novel hierarchical
DL tasks distribution framework, where the UAV is embedded with lower layers of
the pre-trained CNN model, while the MEC server with rich computing resources
will handle the higher layers of the CNN model. An optimization problem is
formulated to minimize the weighted-sum cost including the tracking delay and
energy consumption introduced by communication and computing of the UAVs, while
taking into account the quality of data (e.g., video frames) input to the DL
model and the inference errors. Analytical results are obtained and insights
are provided to understand the tradeoff between the weighted-sum cost and
inference error rate in the proposed framework. Numerical results demonstrate
the effectiveness of the proposed offloading framework.
