# Ready Player One: UAV Clustering based Multi-Task Offloading for Vehicular VR/AR Gaming

arXiv: https://arxiv.org/abs/1904.03861 · HTML: https://mlsanity.com/paper/1904.03861/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles](https://mlsanity.com/paper/2101.06056/) (2021)
- [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control](https://mlsanity.com/paper/2212.13141/) (2022)

## Abstract (arXiv)

With rapid development of unmanned aerial vehicle (UAV) technology, application of the UAVs for task offloading has received increasing interest in the academia. However, real-time interaction between one UAV and the mobile edge computing (MEC) node is required for processing the tasks of mobile end users, which significantly increases the system overhead and is unable to meet the demands of large-scale artificial intelligence (AI) based applications. To tackle this problem, in this article, we propose a new architecture for UAV clustering to enable efficient multi-modal multi-task task offloading. By the proposed architecture, the computing, caching and communication resources are collaboratively optimized using AI based decision-making. This not only increases the efficiency of UAV clusters, but also provides insight into the fusion of computation and communication.