Adaptive Edge Caching in UAV-assisted 5G Network

Abstract

Unmanned aerial vehicles (UAVs) with communication, computing, and storage capabilities have high mobility. Based on this advantage, it can push the service closer to the user. Our research group is concerned with implementing the Internet of Things (IoT) enabled massive crowd management platform that employs 5G to facilitate network connectivity among the UAV and sensory networks. In such a highly dynamic environment, IoT devices, users, and UAVs are the key factors to determine the caching strategies. Due to the limitations of drone batteries and changes in UAV cluster density, the environment is characterized as highly dynamic. However, the existing UAV caching strategy does not consider both the changes of the users and UAVs. Therefore, this paper proposes a three-layer UAV cache architecture in 5G network to achieve hierarchical adaptation to the dynamic changes of users and UAVs. Based on this architecture, we propose a dual dynamic adaptive caching(DDAC) algorithm. The DDAC algorithm is divided into two parts: user adaptation and UAV adaptation. For user adaptation, we designed a user-adaptive UAV trajectory model, which ensures the transmission efficiency of the UAV. For UAV adaptation, we designed and deployed a UAV-adaptive cache model based on a greedy algorithm in the cognitive center layer. The UAV can dynamically adjust the caching strategy according to the cluster density. Finally, the results of the experiment prove that our proposed UAV adaptive cache model has better performance in the cache hit ratio compared with the existing UAV cache model.