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

## TL;DR

This paper proposes a UAV clustering architecture that optimizes multi-task offloading for vehicular VR/AR gaming, enhancing efficiency through AI-driven resource management and fusion of computation and communication.

## Contribution

It introduces a novel UAV clustering architecture that enables efficient multi-task offloading by AI-based optimization of resources, addressing real-time interaction challenges.

## Key findings

- Improved UAV cluster efficiency through AI-based resource optimization
- Enhanced multi-task offloading for vehicular VR/AR applications
- Insights into computation and communication fusion in UAV networks

## Abstract

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.