research-article

A model-based approach for RFID data stream cleansing

Authors:

Wilfred NgAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 862 - 871

https://doi.org/10.1145/2396761.2396871

Published: 29 October 2012 Publication History

Abstract

In recent years, RFID technologies have been used in many applications, such as inventory checking and object tracking. However, raw RFID data are inherently unreliable due to physical device limitations and different kinds of environmental noise. Currently, existing work mainly focuses on RFID data cleansing in a static environment (e.g. inventory checking). It is therefore difficult to cleanse RFID data streams in a mobile environment (e.g. object tracking) using the existing solutions, which do not address the data missing issue effectively.

In this paper, we study how to cleanse RFID data streams for object tracking, which is a challenging problem, since a significant percentage of readings are routinely dropped. We propose a probabilistic model for object tracking in a mobile environment. We develop a Bayesian inference based approach for cleansing RFID data using the model. In order to sample data from the movement distribution, we devise a sequential sampler that cleans RFID data with high accuracy and efficiency. We validate the effectiveness and robustness of our solution through extensive simulations and demonstrate its performance by using two real RFID applications of human tracking and conveyor belt monitoring.

References

[1]

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Signal Processing, IEEE Transactions on, 50(2):174--188, 2002.

Digital Library

[2]

C. Bishop and S. S. en ligne. Pattern recognition and machine learning, volume 4. springer New York, 2006.

Digital Library

[3]

C. Bornhövd, T. Lin, S. Haller, and J. Schaper. Integrating automatic data acquisition with business processes experiences with sap's auto-id infrastructure. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, pages 1182--1188. VLDB Endowment, 2004.

Digital Library

[4]

Z. Cao, C. Sutton, Y. Diao, and P. Shenoy. Distributed inference and query processing for rfid tracking and monitoring. Proceedings of the VLDB Endowment, 4(5):326--337, 2011.

Digital Library

[5]

H. Chen, W. Ku, H. Wang, and M. Sun. Leveraging spatio-temporal redundancy for rfid data cleansing. In Proceedings of the 2010 international conference on Management of data, pages 51--62. ACM, 2010.

Digital Library

[6]

D. Delen, B. Hardgrave, and R. Sharda. Rfid for better supply-chain management through enhanced information visibility. Production and Operations Management, 16(5):613--624, 2007.

[7]

L. Ferreira Chaves, E. Buchmann, and K. Böhm. Finding misplaced items in retail by clustering rfid data. In Proceedings of the 13th International Conference on Extending Database Technology, pages 501--512. ACM, 2010.

Digital Library

[8]

H. Gonzalez, J. Han, and X. Shen. Cost-conscious cleaning of massive rfid data sets. In Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, pages 1268--1272. IEEE, 2007.

[9]

S. Jeffery, G. Alonso, M. Franklin, W. Hong, and J. Widom. A pipelined framework for online cleaning of sensor data streams. In Data Engineering, 2006. ICDE'06. Proceedings of the 22nd International Conference on, pages 140--140. IEEE, 2006.

Digital Library

[10]

S. Jeffery, M. Garofalakis, and M. Franklin. Adaptive cleaning for rfid data streams. In Proceedings of the 32nd international conference on Very large data bases, pages 163--174. VLDB Endowment, 2006.

Digital Library

[11]

W. Ng. Developing rfid database models for analysing moving tags in supply chain management. Conceptual Modeling--ER 2011, pages 204--218, 2011.

Digital Library

[12]

J. Rao, S. Doraiswamy, H. Thakkar, and L. Colby. A deferred cleansing method for rfid data analytics. In Proceedings of the 32nd international conference on Very large data bases, pages 175--186. VLDB Endowment, 2006.

Digital Library

[13]

C. Ré, J. Letchner, M. Balazinksa, and D. Suciu. Event queries on correlated probabilistic streams. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 715--728. ACM, 2008.

Digital Library

[14]

C. Ré, J. Letchner, M. Balazinksa, and D. Suciu. Event queries on correlated probabilistic streams. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 715--728. ACM, 2008.

Digital Library

[15]

L. Sullivan. Rfid implementation challenges persist, all this time later. Information Week, 2005.

[16]

F. Thiesse and F. Michahelles. An overview of epc technology. Sensor Review, 26(2):101--105, 2006.

[17]

T. Tran, C. Sutton, R. Cocci, Y. Nie, Y. Diao, and P. Shenoy. Probabilistic inference over rfid streams in mobile environments. In Data Engineering, 2009. ICDE'09. IEEE 25th International Conference on, pages 1096--1107. IEEE, 2009.

Digital Library

[18]

F. Wang and P. Liu. Temporal management of rfid data. In Proceedings of the 31st international conference on Very large data bases, pages 1128--1139. VLDB Endowment, 2005.

Digital Library

Cited By

Mirzaie MBehkamal BAllahbakhsh MPaydar SBertino E(2023)State of the art on quality control for data streamsComputer Science Review10.1016/j.cosrev.2023.10055448:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100554
Cao ZWang SSun DZhang YFeng YJiang S(2023)ITAR: A Method for Indoor RFID Trajectory Automatic RecoveryCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-24386-8_22(400-418)Online publication date: 25-Jan-2023
https://doi.org/10.1007/978-3-031-24386-8_22
Wang PYang JZhang J(2022)Indoor Trajectory Prediction for Shopping Mall via Sequential SimilarityInformation10.3390/info1303015813:3(158)Online publication date: 19-Mar-2022
https://doi.org/10.3390/info13030158
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Index Terms

A model-based approach for RFID data stream cleansing
1. Information systems
  1. Data management systems

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

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Published: 29 October 2012

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October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Mirzaie MBehkamal BAllahbakhsh MPaydar SBertino E(2023)State of the art on quality control for data streamsComputer Science Review10.1016/j.cosrev.2023.10055448:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100554
Cao ZWang SSun DZhang YFeng YJiang S(2023)ITAR: A Method for Indoor RFID Trajectory Automatic RecoveryCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-24386-8_22(400-418)Online publication date: 25-Jan-2023
https://doi.org/10.1007/978-3-031-24386-8_22
Wang PYang JZhang J(2022)Indoor Trajectory Prediction for Shopping Mall via Sequential SimilarityInformation10.3390/info1303015813:3(158)Online publication date: 19-Mar-2022
https://doi.org/10.3390/info13030158
Sun YShinar TSchroeder CLevin DKry P(2020)Effective time step restrictions for explicit MPM simulationProceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation10.1111/cgf.14101(1-13)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1111/cgf.14101
Mirsky RGal KStern RKalech M(2019)Goal and Plan Recognition Design for Plan LibrariesACM Transactions on Intelligent Systems and Technology10.1145/323446410:2(1-23)Online publication date: 23-Jan-2019
https://dl.acm.org/doi/10.1145/3234464
Corizzo RCeci MMalerba D(2019)Big Data Analytics and Predictive Modeling Approaches for the Energy Sector2019 IEEE International Congress on Big Data (BigDataCongress)10.1109/BigDataCongress.2019.00020(55-63)Online publication date: Jul-2019
https://doi.org/10.1109/BigDataCongress.2019.00020
Ramzan SBajwa IRamzan BAnwar W(2019)Intelligent Data Engineering for Migration to NoSQL Based Secure EnvironmentsIEEE Access10.1109/ACCESS.2019.29169127(69042-69057)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2916912
Dice DHerlihy MKogan A(2018)Improving Parallelism in Hardware Transactional MemoryACM Transactions on Architecture and Code Optimization10.1145/317796215:1(1-24)Online publication date: 22-Mar-2018
https://dl.acm.org/doi/10.1145/3177962
Zinenko OHuot SBastoul C(2018)Visual Program Manipulation in the Polyhedral ModelACM Transactions on Architecture and Code Optimization10.1145/317796115:1(1-25)Online publication date: 22-Mar-2018
https://dl.acm.org/doi/10.1145/3177961
Fazzinga BFlesca SFurfaro FParisi F(2017)Using integrity constraints to guide the interpretation of RFID-trajectory dataSIGSPATIAL Special10.1145/3151123.31511289:2(28-35)Online publication date: 10-Oct-2017
https://dl.acm.org/doi/10.1145/3151123.3151128
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