Cascadia: A System for Specifying, Detecting, and Managing RFID Events

Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia provides three important services. First, it allows application developers and even users to specify events using either a declarative query language or an intuitive visual language based on direct manipulation. Second, it provides an API that facilitates the development of applications which rely on RFID-based events. Third, it automatically detects the specified events, forwards them to registered applications and stores them for later use (e.g., for historical queries). We present the design and implementation of Cascadia along with an evaluation that includes both a user study and measurements on traces collected in a building-wide RFID deployment. To demonstrate how Cascadia facilitates application development, we built a simple digital diary application in the form of a calendar that populates itself with RFID-based events. Cascadia copes with ambiguous RFID data and limitations in an RFID deployment by transforming RFID readings into probabilistic events. We show that this approach outperforms deterministic event detection techniques while avoiding the need to specify and train sophisticated models.

[1]  Narain H. Gehani,et al.  Composite Event Specification in Active Databases: Model & Implementation , 1992, VLDB.

[2]  Hector Garcia-Molina,et al.  The Management of Probabilistic Data , 1992, IEEE Trans. Knowl. Data Eng..

[3]  Sharma Chakravarthy,et al.  Composite Events for Active Databases: Semantics, Contexts and Detection , 1994, VLDB.

[4]  Bill N. Schilit,et al.  An overview of the PARCTAB ubiquitous computing experiment , 1995, IEEE Wirel. Commun..

[5]  Jason Pascoe,et al.  The stick-e note architecture: extending the interface beyond the user , 1997, IUI '97.

[6]  Gregory D. Abowd,et al.  The context toolkit: aiding the development of context-enabled applications , 1999, CHI '99.

[7]  Jean Bacon,et al.  Event Storage and Federation Using ODMG , 2000, POS.

[8]  Joseph F. McCarthy,et al.  EVENTMANAGER: Support for the Peripheral Awareness of Events , 2000, HUC.

[9]  Robert B. Ross,et al.  Probabilistic temporal databases, I: algebra , 2001, TODS.

[10]  Andy Hopper,et al.  Implementing a Sentient Computing System , 2001, Computer.

[11]  Peter R. Pietzuch,et al.  Hermes: a distributed event-based middleware architecture , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

[12]  Klara Nahrstedt,et al.  Gaia: a middleware platform for active spaces , 2002, MOCO.

[13]  Guanling Chen,et al.  Context aggregation and dissemination in ubiquitous computing systems , 2002, Proceedings Fourth IEEE Workshop on Mobile Computing Systems and Applications.

[14]  Armando Fox,et al.  The Event Heap: a coordination infrastructure for interactive workspaces , 2002, Proceedings Fourth IEEE Workshop on Mobile Computing Systems and Applications.

[15]  Gregory D. Abowd,et al.  Distributed mediation of ambiguous context in aware environments , 2002, UIST '02.

[16]  David Garlan,et al.  Aura: an Architectural Framework for User Mobility in Ubiquitous Computing Environments , 2002, WICSA.

[17]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[18]  Jeffrey Heer,et al.  liquid: Context-Aware Distributed Queries , 2003, UbiComp.

[19]  Mik Lamming,et al.  SPECx: Another Approach to Human Context and Activity Sensing Research, Using Tiny Peer-to-Peer Wireless Computers , 2003, UbiComp.

[20]  Robert E. Strom,et al.  Relational subscription middleware for Internet-scale publish-subscribe , 2003, DEBS '03.

[21]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[22]  Anind K. Dey,et al.  a CAPpella: programming by demonstration of context-aware applications , 2004, CHI.

[23]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[24]  James A. Landay,et al.  An architecture for privacy-sensitive ubiquitous computing , 2004, MobiSys '04.

[25]  Christian Floerkemeier,et al.  Issues with RFID Usage in Ubiquitous Computing Applications , 2004, Pervasive.

[26]  Michael J. Franklin,et al.  Events on the edge , 2005, SIGMOD '05.

[27]  Ying Hu,et al.  Supporting RFID-based Item Tracking Applications in Oracle DBMS Using a Bitmap Datatype , 2005, VLDB.

[28]  Jennifer Widom,et al.  Trio: A System for Integrated Management of Data, Accuracy, and Lineage , 2004, CIDR.

[29]  Frederick Reiss,et al.  Design Considerations for High Fan-In Systems: The HiFi Approach , 2005, CIDR.

[30]  Fusheng Wang,et al.  Temporal Management of RFID Data , 2005, VLDB.

[31]  Jakob E. Bardram The Java Context Awareness Framework (JCAF) - A Service Infrastructure and Programming Framework for Context-Aware Applications , 2005, Pervasive.

[32]  Sunny Consolvo,et al.  Learning and Recognizing the Places We Go , 2005, UbiComp.

[33]  Diego Klabjan,et al.  Warehousing and Analyzing Massive RFID Data Sets , 2006, 22nd International Conference on Data Engineering (ICDE'06).

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

[35]  Dan Suciu,et al.  Towards correcting input data errors probabilistically using integrity constraints , 2006, MobiDE '06.

[36]  Christopher Ré,et al.  Query Evaluation on Probabilistic Databases , 2006, IEEE Data Eng. Bull..

[37]  Sharma Chakravarthy,et al.  SnoopIB: Interval-based event specification and detection for active databases , 2003, Data Knowl. Eng..

[38]  Daisy Zhe Wang,et al.  Probabilistic Data Management for Pervasive Computing: The Data Furnace Project , 2006, IEEE Data Eng. Bull..

[39]  Johannes Gehrke,et al.  Towards Expressive Publish/Subscribe Systems , 2006, EDBT.

[40]  Samuel Madden,et al.  MauveDB: supporting model-based user views in database systems , 2006, SIGMOD Conference.

[41]  Yanlei Diao,et al.  High-performance complex event processing over streams , 2006, SIGMOD Conference.

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

[43]  Dan Suciu,et al.  Efficient query evaluation on probabilistic databases , 2004, The VLDB Journal.

[44]  Val Tannen,et al.  Models for Incomplete and Probabilistic Information , 2006, IEEE Data Eng. Bull..

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

[46]  T. S. Jayram,et al.  Efficient aggregation algorithms for probabilistic data , 2007, SODA '07.

[47]  Magdalena Balazinska,et al.  Challenges for Pervasive RFID-Based Infrastructures , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).

[48]  Dan Suciu,et al.  Physical Access Control for Captured RFID Data , 2007, IEEE Pervasive Computing.

[49]  Graham Cormode,et al.  Sketching probabilistic data streams , 2007, SIGMOD '07.

[50]  Scott R. Klemmer,et al.  Authoring sensor-based interactions by demonstration with direct manipulation and pattern recognition , 2007, CHI.

[51]  M. Balazinska,et al.  PEEX : Extracting Probabilistic Events from RFID Data , 2007 .

[52]  Andrew McGregor,et al.  Estimating statistical aggregates on probabilistic data streams , 2008, TODS.

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

[54]  Amol Deshpande,et al.  Online Filtering, Smoothing and Probabilistic Modeling of Streaming data , 2008, 2008 IEEE 24th International Conference on Data Engineering.