Cited By
View all- Dai HWong RWang HZheng ZVasilakos A(2019)Big Data Analytics for Large-scale Wireless NetworksACM Computing Surveys10.1145/333706552:5(1-36)Online publication date: 13-Sep-2019
Automatic detection of false positive RFID readings using machine learning algorithms
Authors: 
Haishu Ma, Yi Wang, Kesheng WangAuthors Info & Claims
Abstract
This paper presents a classification method for detection of false positive RFID readings.We collect RFID data from a joint factory built by NTNU and SPU.We extract received signal strength (RSS) and phase rotations derived from the raw RFID data.Experimental results demonstrate our proposed method achieves satisfactory accuracy. Radio frequency identification (RFID) has been widely used for the automatic identification, tracking and tracing of goods throughout the supply chain from the manufacturer to the customer. However, one technological problem that impedes the productive and reliable use of RFID is the constraint of false positive readings, which refers to tags that are detected accidentally by the reader but not the ones of interest. This paper focuses on the use of machine learning algorithms to identify such RFID readings. A total of 11 statistical features are extracted from received signal strength (RSS) and phase rotations derived from the raw RFID data. Each of the features is highly statistically different to distinguish the false positive readings, but satisfactory classification cannot be achieved when these features are considered individually. Classifiers based on logistic regression (LR), support vector machine (SVM) and decision tree (DT) are constructed, which combine all of the extracted features to classify the RFID readings more effectively. The performance of the classifiers is evaluated in a real-world factory. Results show that SVM provides the highest accuracy of up to 95.3%. DT shows slightly better accuracy (92.85%) than LR (92.75%), while LR has the larger area under the curve (0.976) than DT (0.949). Overall, machine learning algorithms could achieve accuracy of 93% on average. The proposed methodology provides a much more reliable RFID application as false-positive readings are detected immediately without human intervention, which enables a significant potential of fully automatic identification and tracking of goods throughout the supply chain.
References
[1]
Y. Bai, F. Wang, P. Liu, Efficiently filtering RFID data streams, in: CleanDB, Citeseer, 2006.
[2]
A. Bekkali, H. Sanson, M. Matsumoto, RFID indoor positioning based on probabilistic RFID map and Kalman filtering, in: Third IEEE international conference on wireless and mobile computing, networking and communications (WiMob 2007), IEEE, 2007.
[3]
C. Bertoncini, K. Rudd, B. Nousain, M. Hinders, Wavelet fingerprinting of radio-frequency identification (RFID) tags, IEEE Transactions on Industrial Electronics, 59 (2012) 4843-4850.
[4]
C. Bian, Q. Peng, G. Zhang, Improvement of RFID accuracy for a product tracking system, in: ASME 2013 International design engineering technical conferences and computers and information in engineering conference, American Society of Mechanical Engineers, 2013.
[5]
G.H. Bong, Y.S. Chang, C.H. Oh, A practical algorithm for reliability-based RFID event management considering warehouse operational environment, International Journal of Advanced Logistics, 3 (2014) 100-108.
[6]
B. Chen, X. Chen, B. Li, Z. He, H. Cao, G. Cai, Reliability estimation for cutting tools based on logistic regression model using vibration signals, Mechanical Systems and Signal Processing, 25 (2011) 2526-2537.
[7]
S. Chernbumroong, S. Cang, A. Atkins, H. Yu, Elderly activities recognition and classification for applications in assisted living, Expert Systems with Applications, 40 (2013) 1662-1674.
[8]
J. Han, J. Pei, M. Kamber, Data mining: concepts and techniques, Elsevier, 2011.
[9]
C. Hekimian-Williams, B. Grant, X. Liu, Z. Zhang, P. Kumar, Accurate localization of RFID tags using phase difference, in: Proceedings of IEEE International Conference on RFID, IEEE, 2010, pp. 89-96.
[10]
A.M. Hossain, Y. Jin, W.-S. Soh, H.N. Van, SSD: A robust RF location fingerprint addressing mobile devices' heterogeneity, IEEE Transactions on Mobile Computing, 12 (2013) 65-77.
[11]
J. Huiting, H. Flisijn, A.B.J. Kokkeler, G.J.M. Smit, Exploiting phase measurements of EPC Gen2 RFID tags, in: RFID-technologies and applications (RFID-TA), 2013 IEEE international conference on, 45 Sept. 2013, 2013, pp. 1-6.
[12]
S.R. Jeffery, M. Garofalakis, M.J. Franklin, Adaptive cleaning for RFID data streams, in: Proceedings of the 32nd international conference on very large data bases, VLDB Endowment, 2006, pp. 163-174.
[13]
Y. Ju Tu, S. Piramuthu, Reducing false reads in RFID-embedded supply chains, Journal of Theoretical and Applied Electronic Commerce Research, 3 (2008) 60-70.
[14]
T. Keller, F. Thiesse, E. Fleisch, Classification models for RFID-based real-time detection of process events in the supply chain: An empirical study, ACM Transactions on Management Information Systems (TMIS), 5 (2015) 25.
[15]
T. Keller, F. Thiesse, A. Ilic, E. Fleisch, Decreasing false-positive rfid tag reads by improved portal antenna setups, in: Internet of things (IOT), 2012 3rd international conference on the, IEEE, 2012, pp. 99-106.
[16]
T. Keller, F. Thiesse, J. Kungl, E. Fleisch, Using low-level reader data to detect false-positive RFID tag reads, in: Internet of things (IOT), 2010, IEEE, 2010, pp. 1-8.
[17]
R. Krigslund, P. Popovski, G.F. Pedersen, K. Olesen, Interference helps to equalize the read range and reduce false positives of passive RFID tags, IEEE Transactions on Industrial Electronics, 59 (2012) 4821-4830.
[18]
C.K.M. Lee, W. Ho, G.T.S. Ho, H.C.W. Lau, Design and development of logistics workflow systems for demand management with RFID, Expert Systems with Applications, 38 (2011) 5428-5437.
[19]
H. Li, Y. Wang, P. Zhao, X. Zhang, P. Zhou, Cutting tool operational reliability prediction based on acoustic emission and logistic regression model, Journal of Intelligent Manufacturing, 26 (2015) 923-931.
[20]
X. Luo, W.J. O'Brien, C.L. Julien, Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites, Advanced Engineering Informatics, 25 (2011) 355-363.
[21]
L.M. Ni, Y. Liu, Y.C. Lau, A.P. Patil, LANDMARC: Indoor location sensing using active RFID, Wireless Networks, 10 (2004) 701-710.
[22]
S. Parlak, I. Marsic, Detecting object motion using passive RFID: A trauma resuscitation case study, IEEE Transactions on Instrumentation and Measurement, 62 (2013) 2430-2437.
[23]
B. Schlkopf, A.J. Smola, R.C. Williamson, P.L. Bartlett, New support vector algorithms, Neural Computation, 12 (2000) 1207-1245.
[24]
M. Stella, M. Russo, D. Begui, Fingerprinting based localization in heterogeneous wireless networks, Expert Systems with Applications, 41 (2014) 6738-6747.
[25]
Y.-J. Tu, S. Piramuthu, A decision-support model for filtering RFID read data in supply chains, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41 (2011) 268-273.
[26]
J. Wang, D. Katabi, Dude, where's my card?: RFID positioning that works with multipath and non-line of sight, ACM SIGCOMM Computer Communication Review, 43 (2013) 51-62.
[27]
K. Wang, Applying data mining to manufacturing: The nature and implications, Journal of Intelligent Manufacturing, 18 (2007) 487-495.
[28]
L. Yao, Q.Z. Sheng, W. Ruan, X. Li, S. Wang, Z. Yang, Unobtrusive posture recognition via online learning of multi-dimensional RFID received signal strength, in: Parallel and distributed systems (ICPADS), 2015 IEEE 21st international conference on, IEEE, 2015, pp. 116-123.
[29]
R.Y. Zhong, G.Q. Huang, Q. Dai, T. Zhang, Mining SOTs and dispatching rules from RFID-enabled real-time shopfloor production data, Journal of Intelligent Manufacturing, 25 (2014) 825-843.
[30]
C. Zhou, J.D. Griffin, Accurate phase-based ranging measurements for backscatter RFID tags, IEEE Antennas Wireless Propagation Letters, 11 (2012) 152-155.
- Automatic detection of false positive RFID readings using machine learning algorithms
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Published: 01 January 2018
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View all- Dai HWong RWang HZheng ZVasilakos A(2019)Big Data Analytics for Large-scale Wireless NetworksACM Computing Surveys10.1145/333706552:5(1-36)Online publication date: 13-Sep-2019
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