AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (13.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Smart Attendance System Based on Frequency Distribution Algorithm with Passive RFID Tags

Qianwen Miao, Fu Xiao( ), Haiping Huang, Lijuan Sun, Ruchuan Wang
School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.
Department of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210046, China.
Show Author Information

Abstract

Staff attendance information has always been an important part of corporate management. However, some opportunistic employees may consign others to punch their time cards, which hampers the authenticity of attendance and effectiveness of record keeping. Hence, it is necessary to develop an innovative anti-cheating system for office attendance. Radio-Frequency IDentification (RFID) offers new solutions to solve such problems because of its strong anti-interference capability and non-intrusiveness. In this paper, we present a smart attendance system that extracts distinguishable phase characteristics of individuals to enable recognition of various targets. A frequency distribution histogram is extracted as a fingerprint for recognition and the K-means clustering method is utilized for more fine-grained recognition of targets with similar features. Compared with traditional attendance mechanisms, RFID-based attendance systems are based on living biological characteristics, which greatly reduces the possibility of false records. To evaluate the performance of our system, we conducted extensive experiments. The results of which demonstrate the efficiency and accuracy of our system with an average accuracy of 92%. Moreover, the system evaluation shows that our design is robust against differences in the clothing worn and time of day, which further verifies the successful performance of our system.

Keywords

frequency distributionRadio-Frequency IDentification (RFID)office attendanceK-means clustering

References

[1]
K. Cao and A. K. Jain, Automated latent fingerprint recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence (Early Access), .
Crossref Google Scholar
[2]
Y. Zhang, R. Yu, W. Yao, S. Xie, Y. Xiao, and M. Guizani, Home M2M networks: Architectures, standards, and QoS improvement, IEEE Communications Magazine, vol. 49, no. 4, pp. 44–52, 2011.
Crossref Google Scholar
[3]
Z. Li, F. Xiao, S. Wang, T. Pei, and J. Li, Achievable rate maximization for cognitive hybrid satellite-terrestrial networks with AF-relays, IEEE J. on Selected Areas in Communications, vol. 36, no. 2, pp. 304–313, 2018.
Crossref Google Scholar
[4]
S. Thakre, A. K. Gupta, and S. Sharma, Secure reliable multimodel biometric fingerprint and face recognition, in Proc. 12th Int. Conf. ICCCI, Coimbatore, India, 2017, pp. 1–4.
Crossref
[5]
Z. Li, D. Gong, Q. Li, D. Tao, and X. Li, Mutual component analysis for heterogeneous face recognition, ACM Trans. on TIST, vol. 7, no. 3, pp. 1–23, 2016.
Crossref Google Scholar
[6]
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter, Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition, in Proc. ACM SIGSAC Conf., Vienna, Austria, 2016, pp. 1528–1540.
Crossref
[7]
J. Yang, Y. Ge, H. Xiong, Y. Chen, and H. Liu, Performing joint learning for passive intrusion detection in pervasive wireless environments, in Proc. IEEE INFOCOM, San Diego, CA, USA, 2010, pp. 767–775.
Crossref
[8]
Kosba A. E., Saeed A., and Youssef M., RASID:  A robust WLAN device-free passive motion detection system, in Proc. IEEE Int. Conf. on PerCom, Lugano, Switzerland, 2012, pp. 180–189.10.1109/PerCom.2012.6199865
Crossref
[9]
C. Alippi, M. Bocca, G. Boracchi, N. Patwari, and M. Roveri, RTI goes wild: Radio tomographic imaging for outdoor people detection and localization, IEEE Trans. on Mobile Computer, vol. 15, no. 10, pp. 2585–2598, 2014.
Crossref Google Scholar
[10]
D. Halperin, W. Hu, A. Sheth, and D. Wetherall, Tool release: Gathering 802.11N traces with channel state information, in ACM SIGCOMM Comput. Commun, vol. 41, no. 1, p. 53, 2011.
Crossref Google Scholar
[11]
K. Qian, C. Wu, Z. Yang, Y. Liu, and Z. Zhou, PADS: Passive detection of moving targets with dynamic speed using PHY layer information, in Proc. 20th IEEE Int. Conf. on ICPADS, Hsinchu, China, 2014, pp. 1–8.
Crossref
[12]
F. Adib and D. Katab, See through walls with WiFi, in Proc. ACM SIGCOMM, Hong Kong, China, 2013, pp. 75–86.
Crossref
[13]
Y. Wang, K. Wu, and L. M. Ni, WiFall: Device-free fall detection by wireless networks, IEEE Trans. on Mobile Computer, vol. 16, no. 2, pp. 581–594, 2017.
Crossref Google Scholar
[14]
L. M. Ni, Y. Liu, Y. Lau, and A. P. Patil, LANDMARC: Indoor location sensing using active RFID, Wireless Netw., vol. 10, no. 6, pp. 701–710, 2004.
Crossref Google Scholar
[15]
L. Yang, Q. Z. Lin, X. Li, T. Liu, and Y. H. Liu, See through walls with COTS RFID system, in Proc. 21st MobiCom, Paris, France, 2015, pp. 487–499.
Crossref
[16]
Z. Wang, F. Xiao, N. Ye, R. Wang, and P. Yang, A see-through-wall system for device-free human motion sensing based on battery-free RFID, ACM Trans. on TECS, vol. 17, no. 1, pp. 1–21, 2018.
Crossref Google Scholar
[17]
F. Xiao, Z. Wang, N. Ye, R. Wang, and X. Y. Li, One more tag enables fine-grained RFID localization and tracking, IEEE/ACM Trans. on Networking, vol. 26, no. 1, pp. 161–174, 2018.
Crossref Google Scholar
[18]
L. Yang, Y. Chen, X. Y. Li, C. Xiao, M. Li, and Y. H. Liu, Tagoram: Real-time tracking of mobile RFID tags to high precision using COTS devices, in Proc. 20th MobiCom, Maui, HI, USA, 2014, pp. 237–248.
Crossref
[19]
J. L. Zhang, P. Liu, F. Zhang, and Q. Q. Song, CloudNet: Ground-based cloud classification with deep convolutional neural network, Geophysical Research Letters, vol. 45, no. 16, pp. 8665–8672, 2018.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 25 Issue 2,
April 2020
Pages 217-226
DOI: 10.26599/TST.2018.9010141
Cite this article:
Miao Q, Xiao F, Huang H, et al. Smart Attendance System Based on Frequency Distribution Algorithm with Passive RFID Tags. Tsinghua Science and Technology, 2020, 25(2): 217-226. https://doi.org/10.26599/TST.2018.9010141

707

Views

39

Downloads

22

Crossref

N/A

Web of Science

31

Scopus

0

CSCD

Altmetrics
Received: 28 September 2018
Revised: 26 November 2018
Accepted: 27 November 2018
Published: 02 September 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return