Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
RFID Technology and Applications
IEEE Pervasive Computing
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Sketching probabilistic data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
An adaptive RFID middleware for supporting metaphysical data independence
The VLDB Journal — The International Journal on Very Large Data Bases
Cascadia: A System for Specifying, Detecting, and Managing RFID Events
Proceedings of the 6th international conference on Mobile systems, applications, and services
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Increasing Supply-Chain Visibility with Rule-Based RFID Data Analysis
IEEE Internet Computing
IEEE Internet Computing
RFID Infrastructure Design: A Case Study of Two Australian RFID Projects
IEEE Internet Computing
RFID enabled traceability networks: a survey
Distributed and Parallel Databases
P2P Object Tracking in the Internet of Things
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
Factored particles for scalable monitoring
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
SPIRE: Efficient Data Inference and Compression over RFID Streams
IEEE Transactions on Knowledge and Data Engineering
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The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remains many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.