ABSTRACT
Unmanned aerial vehicle (UAV) networks now become more popular than ever and are being used in many fields such as area monitoring, surveillance, outdoor entertainment and rescue operations. The prediction of network mobility has become an important problem in resource reservation/allocation. In this chapter we will identify the state-of-the-art implementations of mobility prediction models via machine learning. Particularly, we will focus on deep spatio-temporal neural networks that can handle large-scale UAV swarms. Ten studies are compared with the discussions of their methods and advantages.
