  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.
