A Cloud-Friendly RFID Trajectory Clustering Algorithm in Uncertain Environments

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 number of transactions and amount of data that require novel approaches in mining useful information from RFID trajectories. RFID data usually contain a considerable degree of uncertainty caused by various factors such as hardware flaws, transmission faults and environment instability. In this paper, we propose an efficient clustering algorithm that is much less sensitive to noise and outliers than the existing methods. To better facilitate the emerging cloud computing resources, our algorithm is designed cloud-friendly so that it can be easily adopted in a cloud environment. The scalability and efficiency of the proposed algorithm are demonstrated through an extensive set of experimental studies.

[1]  Wilfred Ng,et al.  Mining probabilistically frequent sequential patterns in uncertain databases , 2012, EDBT '12.

[2]  Sherali Zeadally,et al.  Enabling Next-Generation RFID Applications: Solutions and Challenges , 2008, Computer.

[3]  Rebecca Angeles,et al.  Rfid Technologies: Supply-Chain Applications and Implementation Issues , 2004, Inf. Syst. Manag..

[4]  Haixun Wang,et al.  A Bayesian Inference-Based Framework for RFID Data Cleansing , 2013, IEEE Transactions on Knowledge and Data Engineering.

[5]  Gian Luca Foresti,et al.  On-line trajectory clustering for anomalous events detection , 2006, Pattern Recognit. Lett..

[6]  Andrew McGregor,et al.  Conditioning and aggregating uncertain data streams , 2010, Proc. VLDB Endow..

[7]  Christian S. Jensen,et al.  Discovery of convoys in trajectory databases , 2008, Proc. VLDB Endow..

[8]  Avigdor Gal,et al.  Complex event processing over uncertain data , 2008, DEBS.

[9]  Minos N. Garofalakis,et al.  An adaptive RFID middleware for supporting metaphysical data independence , 2008, The VLDB Journal.

[10]  Jiawei Han,et al.  Cost-Conscious Cleaning of Massive RFID Data Sets , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[11]  Anna Liu,et al.  PODS: a new model and processing algorithms for uncertain data streams , 2010, SIGMOD Conference.

[12]  Fosca Giannotti,et al.  Synthetic generation of cellular network positioning data , 2005, GIS '05.

[13]  Sherali Zeadally,et al.  RFID enabled traceability networks: a survey , 2011, Distributed and Parallel Databases.

[14]  Avigdor Gal,et al.  Efficient Processing of Uncertain Events in Rule-Based Systems , 2012, IEEE Transactions on Knowledge and Data Engineering.

[15]  Prashant J. Shenoy,et al.  Probabilistic Inference over RFID Streams in Mobile Environments , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[16]  Andrew McGregor,et al.  CLARO: modeling and processing uncertain data streams , 2012, The VLDB Journal.

[17]  Nikos Pelekis,et al.  Clustering and Representing Movements in an Uncertain World , 2009 .

[18]  Dino Pedreschi,et al.  Time-focused clustering of trajectories of moving objects , 2006, Journal of Intelligent Information Systems.

[19]  Nikos Pelekis,et al.  Clustering Trajectories of Moving Objects in an Uncertain World , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[20]  Jae-Gil Lee,et al.  TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering , 2008, Proc. VLDB Endow..

[21]  Reynold Cheng,et al.  Efficient Mining of Frequent Item Sets on Large Uncertain Databases , 2012, IEEE Transactions on Knowledge and Data Engineering.

[22]  Gustavo Alonso,et al.  Declarative Support for Sensor Data Cleaning , 2006, Pervasive.

[23]  Yang Li,et al.  Cascadia: A System for Specifying, Detecting, and Managing RFID Events , 2008, MobiSys '08.

[24]  Padhraic Smyth,et al.  Trajectory clustering with mixtures of regression models , 1999, KDD '99.

[25]  Christopher Ré,et al.  Access Methods for Markovian Streams , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[26]  Lei Chen,et al.  Similarity Join Processing on Uncertain Data Streams , 2011, IEEE Transactions on Knowledge and Data Engineering.

[27]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[28]  Wei Hu,et al.  A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[29]  Elke A. Rundensteiner,et al.  Sequence Pattern Query Processing over Out-of-Order Event Streams , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[30]  Holger Ziekow,et al.  A probabilistic approach for cleaning RFID data , 2008, 2008 IEEE 24th International Conference on Data Engineering Workshop.

[31]  Zhanhuai Li,et al.  Complex Event Processing over Unreliable RFID Data Streams , 2011, APWeb.

[32]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[33]  J. D. M. Kinyua,et al.  An adaptive data cleaning scheme for reducing false negative reads in RFID data streams , 2012, 2012 IEEE International Conference on RFID (RFID).

[34]  Jing Li,et al.  KLEAP: an efficient cleaning method to remove cross-reads in RFID streams , 2011, CIKM '11.

[35]  Jun Rao,et al.  A deferred cleansing method for RFID data analytics , 2006, VLDB.

[36]  Andrew S. Tanenbaum,et al.  The evolution of RFID security , 2006, IEEE Pervasive Computing.

[37]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[38]  J. Landt,et al.  The history of RFID , 2005, IEEE Potentials.

[39]  Minos N. Garofalakis,et al.  Adaptive cleaning for RFID data streams , 2006, VLDB.

[40]  Dan Suciu,et al.  Probabilistic Event Extraction from RFID Data , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[41]  Neil Immerman,et al.  Efficient pattern matching over event streams , 2008, SIGMOD Conference.

[42]  Christopher Ré,et al.  Event queries on correlated probabilistic streams , 2008, SIGMOD Conference.