Efficient distributed query processing in large RFID-enabled supply chains

Radio Frequency Identification (RFID) has dramatically streamlined supply chain management by automatically monitoring and tracking commodities. Considering the proliferation of RFID data volume, distributed storage is more applicable and scalable than centralized storage for distributed query processing. Traditional distributed RFID data storage requires each distribution center to locally store raw RFID data, leading to data redundancy, storage and query inefficiency. In this paper, we design an efficient distributed storage model by leveraging Bloom filters to save storage space and improve query efficiency. Meanwhile, we establish corresponding query processing schemes to locally support existence queries and path queries, which are two kinds of most popular queries in the supply chain management. A local query can be completed with constant time complexity regardless of data volume. Experiments demonstrate that our storage model outperforms the traditional one in terms of both space and time efficiency.