Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

Design of Intelligent Public Transportation System based on ZigBee Technology

Volume 14, Number 3, March 2018, pp. 483-492
DOI: 10.23940/ijpe.18.03.p9.483492

Jing Bian, Xiuxia Yu, and Wei Du

College of Computer Science and Technology, Changchun University, Changchun, 130022, China

(Submitted on December 13, 2017; Revised on January 16, 2018; Accepted on February 9, 2018)


Abstract:

In this article, an intelligent public transportation system based on ZigBee 3.0 technology is proposed after researching conditions of recent public transportation systems that mostly adopt GPS or Beidou satellite positioning technology, 3G/4G communication technology and GIS technology. This system includes the principle and design of system architecture, design of intelligent public transportation system network, the solution of moving-bus positioning, the auto-announce function and design of intelligent public transportation stop board. Contrasted with recent bus systems, this system has the virtue of low construction cost, low running cost, low implementation difficulty and high intelligent level. ZigBee 3.0 protocol is compatible with Wi-Fi, too. Without additional ZigBee chip in smart phones, the intelligent public transportation system based on ZigBee 3.0 can interconnect with smart phones directly. It will break the barrier between citizens and intelligent public transportation systems, and the real intelligence can be realized.

 

References: 8

  1. H. F. Han, K. M. Du, Z. F. Sun, W. Zhao, R. Chen, and J. B. Liang, “Design and Application of ZigBee Based Telemonitoring System for Greenhouse Environment Data Acquisition,” Transactions of the Chinese Society of Agricultural Engineering, vol.25, no.7, pp.158-163, 2009
  2. S. B. Jiao, D. Song, Q. Zhang, and J. W. Tang, “Coal Mine Monitoring System Based on ZigBee Wireless Sensor Networks,” Journal of Electronic Measurement and Instrumentation, vol.27, no.5, pp.436-442, 2013
  3. J. B. Li, and Y. Z. Hu, “Design of ZigBee Network Based on CC2530,” Electronic Design Engineering, vol.19, no.16, pp.108-111, 2011
  4. M. Li, R. Wang, and L. Shi, “Wireless Sensors Network Node Based on ZigBee,” Techniques of Automation and Applications, vol.27, no.1, pp.91-94, 2008
  5. Z. H. Qian, S. Zhu, and X. Wang, “An Cluster-Based ZigBee Routing Algorithm for Network Energy Optimization,” Chinese Journal of Computers, vol.36, no.3, pp.485-493, 2013
  6. X. Shi, A. M. Yin, and X. Chen, “RSSI and Multidimensional Scaling Based Indoor Localization Algorithm,” Chinese Journal of Scientific Instrument, vol.35, no.2, pp.261-267, 2014
  7. J. Zhan, and W. Shi, “Design of Public Transport Communicate Wireless Network Based on ZigBee,” Modern Electronics Technique, vol.30, no.10, pp.118-120, 2007
  8. K. Y. Zhu, J. Y. Liu, Y. L. An, W. Y. Wang, and F. Y. Wang, “City Intelligence Public Transportation Network System Based on ZigBee,” Microcontrollers & Embedded Systems, pp.17-20, 2008

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-03-09.pdf)IJPE-2018-03-09.pdf[Design of Intelligent Public Transportation System based on ZigBee Technology]389 Kb
 

CURRENT ISSUE

Prev Next

Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

Read more

Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

Read more

Spark-based Ensemble Learning for Imbalanced Data Classification

Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

Read more

Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

Read more

An Improved Algorithm based on Time Domain Network Evolution

Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

Read more

Auto-Tuning for Solving Multi-Conditional MAD Model

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

Read more

Smart Mine Construction based on Knowledge Engineering and Internet of Things

Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

Read more

A Mining Model of Network Log Data based on Hadoop

Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com