A Design Mode of Streaming Media Transmission and Access in Wireless Video Sensor Network

  • Mengxi Xu
  • Chenrong Huang
  • Shengnan Zheng
  • Jianqiang Shi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 131)


Wireless video sensor network, acquired by urban road traffic flow information, is composed of several sensor network nodes with traffic flow information video detection, process and wireless communication capacity. Wireless digital camera sensor network cooperatively senses the traffic flow information (traffic flow, vehicle length, vehicle type, etc.) in each location by the means of each node. Network nodes transfer data to information convergence nodes (base station, SINK) by wireless multi-hop relaying mode. Meanwhile, adopting 2.4 G wireless communication, the convergence nodes analyze traffic flow video detection information and converge into MAN/WAN, Gigabit Ethernet, and the control center can do the query, record and other operations by reverse IPTV (interactive network television). This paper focuses on a design mode of streaming transmission and access application in wireless video sensor network.


Multimedia sensor network Streaming media Image communication Intelligent traffic 


  1. 1.
    Luo W-S, Zhai Y-P, Lu Q (2008) Study on wireless multimedia sensor networks. J Electron Inf Technol V30(6):1511–1516Google Scholar
  2. 2.
    Bell MGH (1992) Future direction in traffic signal control. Transp Res A 19A(5–6):369–373Google Scholar
  3. 3.
    Zhong S, Zong C (2010) The application of streaming video monitoring in highway supervision. Transp Inf Ind 5(7):38–44Google Scholar
  4. 4.
    Ding X, Xu L (2011) Stereo depth estimation under different camera calibration and alignment errors. Appl Opt 50(10):1289–1301Google Scholar
  5. 5.
    Wang H (2010) Adaptive down-sampling stereo video coding based on background complexity. J Inf Comput Sci 7(10):2090–2100Google Scholar
  6. 6.
    Xiaofeng D, Lizhong X (2011) Robust visual object tracking using covariance features in Quasi-Monte Carlo filter. Intell Autom Soft Comput 17(5):571Google Scholar
  7. 7.
    Lizhong X et al. (2011) Trust region based sequential Quasi-Monte Carlo filter. Acta Electronica Sinica 39(3):24–30Google Scholar
  8. 8.
    Islam ABMAA, Hyder CS, Kabir H, Naznin M (2010) Finding the optimal percentage of cluster heads from a new and complete mathematical model on LEACH. Wirel Sens Netw 2(2):129–140CrossRefGoogle Scholar
  9. 9.
    Pirzada AA, Portmann M, Indulska J (2008) Performance analysis of multi-radio AODV in hybrid wireless mesh networks. Comput Commun 31(5):885–895CrossRefGoogle Scholar
  10. 10.
    Wang H, Lizhong X (2007) Route protocol of wireless sensor networks based on dynamic setting cluster. In: Proceedings of 2007 IEEE international symposium on industrial electronics, Vigo, Spain, June 4–7Google Scholar
  11. 11.
    Nazir B, Hasbullah H (2010) Energy balanced clustering in wireless sensor network. In: International symposium on information technology (ITSim), Kuala LumpurGoogle Scholar
  12. 12.
    Tan G (2011) Optimizing HARQ with rateless codes for wireless multicast under strict delay-bandwidth constraints. Adv Inf Sci Serv Sci 3(7):347–356Google Scholar
  13. 13.
    Li Y et al. (2011) A new method for improving the small size spacing four-element MIMO system channel capacity and its stability 1(6):1–6Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Mengxi Xu
    • 2
    • 1
  • Chenrong Huang
    • 2
  • Shengnan Zheng
    • 2
  • Jianqiang Shi
    • 2
  1. 1.School of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Computer EngineeringNanjing Institute of TechnologyNanjingChina

Personalised recommendations