Skip to main content

Novel Binary Search Algorithm for Fast Tag Detection in Robust and Secure RFID Systems

  • Conference paper
Advanced Machine Learning Technologies and Applications (AMLTA 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 322))

Abstract

Novel binary search algorithm for fast tag detection (BSF1) and (BSF2) in robust and secure RFID systems is presented in this paper. These algorithms introduce fast tag detection with the new method of inquiry. Tags were grouped in two groups and tag collisions of each group were solved by implementing dynamic searching and backtracking procedure. By grouping the tags, time for solving collision was reduced. It performed fast detection in a robust situation, a group of tags with all possibilities of ID arrangements. Tags attached to the products of different manufacturers may considerably have robust ID. For the security of RFID system, the number of bit (n) will be increased to provide allocation of 2n unique ID. The increasing number of bit and the uniqueness of ID will increase the security of the system from counterfeiting. However it will also increase time identification, but our algorithms will provide fast detection in the situation of high security.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cheng-Sen, B., Jiang, Z.: Research on an RFID Anti-Collision Improved Algorithm Based on Binary Search. In: IEEE International Conference on Computer Application and System Modeling, pp. 430–432 (2010)

    Google Scholar 

  2. Zheng, J., Qin, T.: A Novel Collision Arbitration Protocol for RFID Tag Identification. In: 2nd IEEE International Conference on Software Engineering and Service Science, pp. 100–103 (2011)

    Google Scholar 

  3. Liu, L., Xie, Z., Xi, J., Lai, S.: An Improved Anti-Collision Algorithm in RFID System. In: IEEE Conference on Mobile Technology, Applications and Systems, pp. 1–5 (2005)

    Google Scholar 

  4. Chen, Z., Liao, M.: An Enhanced Dynamic Binary Anti-Collision Algorithm. In: 5th IEEE International Conference on Computer Science and Education, pp. 961–964 (2010)

    Google Scholar 

  5. Yu, Z., Liu, X.: Improvement of Dynamic Binary Search Algorithm Used in RFID System. In: Conference on Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, pp. 1046–1049 (2011)

    Google Scholar 

  6. Xie, X.M., Xie, Z.H., Lai, S.L., Chen, P.: Dynamic Adjustment Algorithm for Tag Anti-Collision. In: IEEE International Conference on Machine Learning and Cybernetics, pp. 443–446 (2011)

    Google Scholar 

  7. Ryu, J., Seok, Y., Kwon, T., Choi, Y.: A Hybrid Query Tree Protocol for Tag Collision Arbitration in RFID Systems. In: IEEE International Conference on Communications, pp. 5981–5986 (2007)

    Google Scholar 

  8. Finkenzeller, K.: RFID Handbook: Radio-Frequency Identification Fundamentals and application. John Wiley & Sons Ltd. (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yaacob, M., Mohd Daud, S. (2012). Novel Binary Search Algorithm for Fast Tag Detection in Robust and Secure RFID Systems. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35326-0_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35325-3

  • Online ISBN: 978-3-642-35326-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics