Title :
Probabilistic Modeling of Streaming RFID Data by Using Correlated Variable-duration HMMs
Author :
Nie, Yanming ; Li, Zhanhuai ; Peng, Shanglian ; Chen, Qun
Author_Institution :
Sch. of Comput., Northwestern Polytech. Univ., Xi´´an, China
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.
Keywords :
data handling; hidden Markov models; inference mechanisms; learning (artificial intelligence); radiofrequency identification; statistical analysis; RFID stream processing; correlated variable-duration hidden Markov models; incomplete data; inference techniques; noisy data; object location inference; probabilistic modeling; product flow tracking; radio frequency identification; spatial correlations; streaming RFID data; tagged objects; temporal correlations; Application software; Cleaning; Computer aided manufacturing; Hidden Markov models; Monitoring; Radiofrequency identification; Smoothing methods; Stochastic processes; Supply chain management; Working environment noise; RFID data; correlated variable-duration HMMs; inference; probabilistic modeling;
Conference_Titel :
Software Engineering Research, Management and Applications, 2009. SERA '09. 7th ACIS International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-3903-4
DOI :
10.1109/SERA.2009.29