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

arXiv: https://arxiv.org/abs/2008.08001 · HTML: https://mlsanity.com/paper/2008.08001/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 14 of 14)

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- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [LAMBO: Large AI Model Empowered Edge Intelligence](https://mlsanity.com/paper/2308.15078/) (2023)
- [Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks](https://mlsanity.com/paper/2107.04648/) (2021)
- [LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2305.15858/) (2023)
- [A2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems](https://mlsanity.com/paper/2301.06363/) (2023)
- [Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication](https://mlsanity.com/paper/2401.17880/) (2024)
- [AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty](https://mlsanity.com/paper/2305.00780/) (2023)
- [Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computing](https://mlsanity.com/paper/2507.05829/) (2025)

## Abstract (arXiv)

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.