Modeling Sovereign RFID Data Streams in Collaborative Traceable Networks

Abstract

In the emerging environment of the Internet of Things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented amount of transactions and data that requires novel approaches in RFID data stream processing and management. Unfortunately, it is difficult to maintain a distributed model without a shared directory or structured index. In this paper, we present a fully distributed model for sovereign RFID data streams. This model combines Tilted Time Frame and Histogram to represent the patterns of object flows. It is efficient in space and can be stored in main memory. The model is built on top of an unstructured P2P overlay. To reduce the overhead of distributed data acquisition, we further propose algorithms that use statistically optimistic number of network calls to maintain the model. The scalability and efficiency of the proposed model are demonstrated through an extensive set of experiments.