An improved binary tree topology for ZigBee networks for high efficiency network addressing

Mong Fong Horng, Jung Yi Jiang, Kuan Rong Lee, Yan Cheng Lee, Chih Chiu Shieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wireless sensor network (WSN) is one of popular research issues, and ZigBee is an important protocol for WSN. Traditionally, the device addresses in WSN are given by a Distributed Address Allocation Mechanism (DAAM). Although DAAM has better extensibility, this network topology resulted by DAAM did not consider fast routing between nodes. Although binary tree topology benefits a faster packet routing via a simple Boolean computation, the binary tree is less extensible. In this paper, a new variation of binary tree topology is proposed to assign nodes under routers the addresses for fast routing. The proposed approach improves the low utility rate of the network address for DAAM.

Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
Pages1152-1156
Number of pages5
ISBN (Electronic)9781479902576
DOIs
Publication statusPublished - 2013 Jan 1
Event12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
Duration: 2013 Jul 142013 Jul 17

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume3
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Other

Other12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
CountryChina
CityTianjin
Period13-07-1413-07-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • Cite this

    Horng, M. F., Jiang, J. Y., Lee, K. R., Lee, Y. C., & Shieh, C. C. (2013). An improved binary tree topology for ZigBee networks for high efficiency network addressing. In Proceedings - International Conference on Machine Learning and Cybernetics (pp. 1152-1156). [6890764] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 3). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2013.6890764