Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays

Activity monitoring, a crucial task in many applications, is often conducted expensively using video cameras. Effectively monitoring a large field by analyzing images from multiple cameras remains a challenging issue. Other approaches generally require the tracking objects to attach special devices, which are infeasible in many scenarios. To address the issue, we propose to use RF tag arrays for activity monitoring, where data mining techniques play a critical role. The RFID technology provides an economically attractive solution due to the low cost of RF tags and readers. Another novelty of this design is that the tracking objects do not need to be equipped with any RF transmitters or receivers. By developing a practical fault-tolerant method, we offset the noise of RF tag data and mine frequent trajectory patterns as models of regular activities. Our empirical study using real RFID systems and data sets verifies the feasibility and the effectiveness of this design.

[1]  Valerie Guralnik,et al.  A scalable algorithm for clustering sequential data , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[2]  Ying Zhang,et al.  Localization from connectivity in sensor networks , 2004, IEEE Transactions on Parallel and Distributed Systems.

[3]  Philip S. Yu,et al.  Mining long sequential patterns in a noisy environment , 2002, SIGMOD '02.

[4]  Fabian Mörchen,et al.  Algorithms for time series knowledge mining , 2006, KDD '06.

[5]  Yunhao Liu,et al.  Quality of Trilateration: Confidence-Based Iterative Localization , 2008, IEEE Transactions on Parallel and Distributed Systems.

[6]  Christopher D. Carothers,et al.  VOGUE: A variable order hidden Markov model with duration based on frequent sequence mining , 2010, TKDD.

[7]  Philip S. Yu,et al.  Direct mining of discriminative and essential frequent patterns via model-based search tree , 2008, KDD.

[8]  Heng Tao Shen,et al.  Searching trajectories by locations: an efficiency study , 2010, SIGMOD Conference.

[9]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[10]  Ai Chen,et al.  Local Barrier Coverage in Wireless Sensor Networks , 2010, IEEE Transactions on Mobile Computing.

[11]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[12]  Johannes Gehrke,et al.  Sequential PAttern mining using a bitmap representation , 2002, KDD.

[13]  Philip S. Yu,et al.  Mining Cluster-Based Temporal Mobile Sequential Patterns in Location-Based Service Environments , 2011, IEEE Transactions on Knowledge and Data Engineering.

[14]  Yunhao Liu,et al.  Rendered Path: Range-Free Localization in Anisotropic Sensor Networks With Holes , 2007, IEEE/ACM Transactions on Networking.

[15]  Tian He,et al.  RSD: A Metric for Achieving Range-Free Localization beyond Connectivity , 2011, IEEE Transactions on Parallel and Distributed Systems.

[16]  Jiawei Han,et al.  IncSpan: incremental mining of sequential patterns in large database , 2004, KDD.

[17]  Qing Liu,et al.  A Hybrid Prediction Model for Moving Objects , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[18]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[19]  Yunhao Liu,et al.  Beyond Trilateration: On the Localizability of Wireless Ad Hoc Networks , 2009, IEEE/ACM Transactions on Networking.

[20]  Yunhao Liu,et al.  Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07).

[21]  Shaojie Tang,et al.  iLight: Indoor device-free passive tracking using wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[22]  Sneha Kumar Kasera,et al.  Advancing wireless link signatures for location distinction , 2008, MobiCom '08.

[23]  Xi Chen,et al.  Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[24]  Neal Patwari,et al.  A Fade-Level Skew-Laplace Signal Strength Model for Device-Free Localization with Wireless Networks , 2012, IEEE Transactions on Mobile Computing.

[25]  Heikki Mannila,et al.  Dense itemsets , 2004, KDD.

[26]  Padhraic Smyth,et al.  Pattern discovery in sequences under a Markov assumption , 2002, KDD.

[27]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[28]  Minyi Guo,et al.  TASA: Tag-Free Activity Sensing Using RFID Tag Arrays , 2011, IEEE Transactions on Parallel and Distributed Systems.

[29]  Ge Yu,et al.  Similarity Match Over High Speed Time-Series Streams , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[30]  Haixun Wang,et al.  Finding semantics in time series , 2011, SIGMOD '11.

[31]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[32]  Jae-Gil Lee,et al.  Mining Discriminative Patterns for Classifying Trajectories on Road Networks , 2011, IEEE Transactions on Knowledge and Data Engineering.

[33]  Pedro José Marrón,et al.  On Boundary Recognition without Location Information in Wireless Sensor Networks , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[34]  Dominik Fisch,et al.  SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis , 2011, IEEE Transactions on Knowledge and Data Engineering.

[35]  Lionel M. Ni,et al.  RASS: A real-time, accurate and scalable system for tracking transceiver-free objects , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).