Edge storage systems aim to store data on edge nodes to provide users with reliable and low-latency data retrieval services. This is especially important for latency-sensitive applications, where users need to locate the target data and retrieve it quickly under multi-hop delay constraints. Although users can query neighboring nodes through serial or parallel retrieval methods, these approaches often lead to significant redundant communication and resource consumption across the edge network. Therefore, designing an efficient indexing mechanism is essential. However, current research on indexing mechanisms for local retrieval in edge storage systems remains limited. In this paper, we propose a tree-based indexing structure built upon the probabilistic data structure known as the Counting Bloom Filter (CBF). By incorporating the advantages of Delaunay Triangulation (DT) graph, the proposed indexing structure enables efficient data query and retrieval. Experimental results show that, compared with existing local retrieval indexing structures such as EDIndex-CBF and EDIndex-HCBF, our approach achieves lower maintenance and query overhead.