JACIII Vol.13 No.2 pp. 128-134
doi: 10.20965/jaciii.2009.p0128


Optimal Location of Wireless LAN Access Points Using Fuzzy ID3

Isao Hayashi*, Takashi Kobayashi*, Yoshinori Arai**,
Toshiyuki Maeda***, and Atsushi Inoue****

*Faculty of Informatics, Kansai University, 2-1-1, Ryozenji, Takatsuki, Osaka 569-1095, Japan

**Faculty of Engineering, Tokyo Polytechnic University, 1583, Iiyama, Atsugi, Kanagawa 243-0297, Japan

*** Faculty of Management Information, Hannan University, 5-4-33, Amami-higashi, Matsubara, Osaka 580-8502, Japan

**** Department of Computer Science, Eastern Washington University, CEB319F EWU, Cheney, WA 99004-2493, USA

February 16, 2008
November 25, 2008
March 20, 2009
wireless LAN, security, fuzzy ID3, fuzzy rules
Although wireless LAN is useful in its small size and mobility, the connection region of transmitted radio wave is strongly affected by other electric devices, consumer products, and differences in size and type of the room. Besides, wireless LAN points (APs) must exclude a personal computers without permitting to connect to the Internet. Therefore, how optimally APs are located is important. In this paper, we propose the APs' optimal location method. The proposed algorithm integrates fuzzy rules acquired by fuzzy ID3 with knowledge of security experts, and estimates the connection region for AP. We discuss how to formulate the method for setting AP optimal location and show the effectiveness of this method by illustrating the examples of AP optimal locations.
Cite this article as:
I. Hayashi, T. Kobayashi, Y. Arai, T. Maeda, and A. Inoue, “Optimal Location of Wireless LAN Access Points Using Fuzzy ID3,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.2, pp. 128-134, 2009.
Data files:
  1. [1] S.Bradner, The Internet Standards Process,, 1996.
  2. [2] T. Tone, “How Networks Work,” Nikkei BP, 2002.
  3. [3] B. O'Hara and A. Petrick, “The IEEE 802.11 Handbook,” A Designer's Companion, IEEE, 1999.
  4. [4] J. R. Quinlan, “Discovering Rules by Induction from Large Collections of Examples, in Expert Systems in the Micro Electronics Age,” Edinburgh University Press, 1979.
  5. [5] R. Weber, “Fuzzy ID3: A Class of Methods for Automatic Knowledge Acquisition,” Proc. of the 2nd Int. Conf. on Fuzzy Logic and Neural Networks, pp. 265-268, 1992.
  6. [6] C. Z. Janikow, “Fuzzy Processing in Decision Trees,” Proc. of the Int. Sympo. on Artificial Intelligence, pp. 360-367, 1993.
  7. [7] M. Umano, H. Okamoto, I. Hatono, H. Tamura, F. Kawachi, S. Umezu, and J. Kinoshita, “Fuzzy Decision Trees by Fuzzy ID3 Algorithm and Its Applications to Diagnosis System,” Proc. of Third IEEE Int. Conf. on Fuzzy Systems, Vol.3, pp. 2113-2118, 1994.
  8. [8] H. Ichihashi, T. Shirai, K. Nagasaka, and T. Miyoshi, “Neuro-fuzzy ID3: A Method of Inducing Fuzzy Decision Trees with Linear Programming for Maximizing Entropy and an Algebraic Method for Incremental Learning,” Fuzzy Sets and Systems, Vol.81, No.1, pp. 157-167, 1996.
  9. [9] C. Olaru and L. Wehenkel, “A Complete Fuzzy Decision Tree Technique,” Fuzzy Sets and Systems, Vol.138, No.2, pp. 221-254, 2003.
  10. [10] I. Hayashi, “Acquisition of Fuzzy Rules Using Fuzzy ID3 with Ability of Learning for AND/OR Operators,” 1996 Australian New Zealand Conf. on Intelligent Inform. Systems, pp. 187-190, 1996.
  11. [11] B. Schweizer and A. Sklar, “Associative Functions and Abstract Semigroup,” Publ. Math. Debreccen, Vol.10, pp. 69-81, 1963.
  12. [12] D. Dubois and H. Prade, “Fuzzy Sets and Systems,” Academic Press, 1980.
  13. [13] T. Tani, M. Sakoda, and K. Tanaka, “Fuzzy Modeling by ID3 Algorithm and Its Application to Prediction of Heater Outlet Temperature,” Proc. of the First IEEE Conf. on Fuzzy Systems, pp. 923-930, 1992.
  14. [14] X. Boyen and L. Wehenkel, “Automatic Induction of Fuzzy Decision Trees and Its Application to Power System Security Assessment,” Fuzzy Sets and Systems, Vol.102, No.1, pp. 3-19, 1999.

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