Offloading Optimization in Edge Computing for Deep-Learning-Enabled Target Tracking by Internet of UAVs

The empowering unmanned aerial vehicles (UAVs) have been extensively used in providing intelligence such as target tracking. In our field experiments, a pretrained convolutional neural network (CNN) is deployed at 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 the 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 pretrained CNN model while the MEC server (MES) 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 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.

Automated summary

The research proposes a hierarchical framework for offloading deep learning tasks to mobile-edge computing servers to improve inference accuracy. This method minimizes the weighted-sum cost considering computational resources and energy budgets, optimizing the performance of target tracking for UAVs.