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

TL;DR

This paper proposes a hierarchical offloading framework for deep learning tasks in UAV-based target tracking, balancing delay, energy consumption, and inference accuracy by distributing CNN layers between UAVs and MEC servers.

Contribution

It introduces a novel hierarchical DL task distribution framework that optimally offloads CNN layers to MEC servers, improving tracking performance in UAV applications.

Findings

The framework reduces tracking delay and energy consumption.

It balances inference accuracy with resource constraints.

Analytical insights into the tradeoff between cost and error rate.

Abstract

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.