Erasure Coding-Based Cost-Optimized and Latency-Aware Data Storage in UAV-Enabled Edge Systems

Uncrewed aerial vehicle (UAV)-enabled edge storage systems provide data storage services to users by deploying UAVs in areas lacking infrastructure coverage, overcoming delay limitations and improving Quality of Service (QoS). Most existing studies focus on storing replicas on UAVs to ensure low-latency data access. Nonetheless, replica-based strategies incur high storage cost, posing significant challenges for UAVs with limited storage resources. In this paper, we introduce erasure coding into the UAV-enabled edge storage system, aiming to reduce user data access latency while minimizing storage cost. However, the mobility of users and the non-fully-connected nature of the UAV network pose new challenges for the coupled decisions of data encoding, block placement, and access. In this paper, we propose a Mobility-Enhanced Hierarchical Deep Reinforcement Learning algorithm (ME-HDRL). Specifically, we design a trajectory prediction algorithm combining CNN and ConvLSTM to account for user mobility in decision-making. We further decompose the original problem into two subproblems: data encoding and placement, as well as block access. A hierarchical deep reinforcement learning algorithm involving multiple UAV agents and an edge agent is proposed to collaboratively learn optimal decisions. To improve the convergence of the algorithm, we design an impractical action filter to reduce the action space. Experimental results show that our approach outperforms existing rule-based and reinforcement learning-based algorithms in various scenarios, exhibiting significant convergence improvements and a substantial reduction in both storage cost and user data access latency.