Age-constrained dynamic content replacing and delivering for UAV-assisted context awareness

In this work, we employ the cache-enabled UAV to provide context information delivery to end devices that make timely and intelligent decisions. Different from the traditional network traffic, context information varies with time and brings in the age-constrained requirement. The cached content items should be refreshed timely based on the age status to guarantee the freshness of user-received contents, which however consumes additional transmission resources. The traditional cache methods separate the caching and the transmitting, which are not suitable for the dynamic context information. We jointly design the cache replacing and content delivery based on both the user requests and the content dynamics to maximize the offloaded traffic from the ground network. The problem is formulated based on the Markov Decision Process (MDP). A sufficient condition of cache replacing is found in closed form, whereby a dynamic cache replacing and content delivery scheme is proposed based on the Deep Q-Network (DQN). Extensive simulations have been conducted. Compared with the conventional popularity-based and the modified Least Frequently Used (i.e., LFU-dynamic) schemes, the UAV can offload around 30 % traffic from the ground network by utilizing the proposed scheme in the urban scenario, according to the simulation results.