Probabilistic Modeling of Streaming RFID Data by Using Correlated Variable-duration HMMs

Yanming Nie, Zhanhuai Li, Shanglian Peng, Qun Chen

Abstract: Radio frequency identification (RFID) has been widely deployed to track product flow in such fields as automated manufacture, retail and supply chain management. The special characteristics of streaming RFID data, combined with the specific scenarios of RFID applications, present numerous challenges in RFID stream processing, including noisy and incomplete data, temporal and spatial correlations and very huge volumes. In this paper, we present a probabilistic model, specifically correlated variable-duration hidden Markov models (CVD-HMMs), to capture uncertainty and correlations of locations of tagged objects. Based on this model, we can infer object locations from raw RFID streams. And our model can be self-tuned by learning its key parameters from sample RFID readings. Experimental results show that our proposed model and the preliminary inference techniques are effective.