# Mobile Edge Computing in Unmanned Aerial Vehicle Networks

arXiv: https://arxiv.org/abs/1910.10523 · HTML: https://mlsanity.com/paper/1910.10523/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks](https://mlsanity.com/paper/2209.07367/) (2022)
- [AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing](https://mlsanity.com/paper/2510.23053/) (2025)
- [Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2508.06863/) (2025)
- [FM-EAC: Feature Model-based Enhanced Actor-Critic for Multi-Task Control in Dynamic Environments](https://mlsanity.com/paper/2512.15430/) (2025)

## Abstract (arXiv)

Unmanned aerial vehicle (UAV)-enabled communication networks are promising in the fifth and beyond wireless communication systems. In this paper, we shed light on three UAV-enabled mobile edge computing (MEC) architectures. Those architectures have been receiving ever increasing research attention for improving computation performance and decreasing execution latency by integrating UAV into MEC networks. We present a comprehensive survey for the state-of-the-art research in this domain. Important implementation issues are clarified. Moreover, in order to provide an enlightening guidance for future research directions, key challenges and open issues are discussed.