Advertisement

The Acquisition of Sand Vibration Information in Hinterland of Desert Based on Advanced Remote Sensing System and Network Technologies

  • Xin Ma (马 鑫)
  • Shunge Deng (邓顺戈)
  • Xinwan Li (李新碗)
Article
  • 14 Downloads

Abstract

The deep understanding on sand and sand dunes scale can be useful to reveal the formation mechanism of the sandstorm for early sandstorm forecast. The current sandstorm observation methods are mainly based on conventional meteorological station and satellites remote sensing, which are difficult to acquire sand scale information. A wireless sensing network is implemented in the hinterland of desert, which includes ad hoc network, sensor, global positioning system (GPS) and system integration technology. The wireless network is a three-layer architecture and daisy chain topology network, which consists of control station, master robots and slave robots. Every three robots including one master robot and its two slave robots forms an ad hoc network. Master robots directly communicate with radio base station. Information will be sent to remote information center. Data sensing system including different kinds of sensors and desert robots is developed. A desert robot is designed and implemented as unmanned probing movable nodes and sensors’ carrier. A new optical fiber sensor is exploited to measure vibration of sand in particular. The whole system, which is delivered to the testing field in hinterland of desert (25 km far from base station), has been proved efficient for data acquisition.

Key words

sandstorm sand-scale information optical fiber sensor desert robot wireless sensor network 

CLC number

TN 29 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

The authors express their thanks to Professor KODITSCHEK Daniel E. (Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania), Professor DONG Zhibao, Professor ZHANG Zhengcai (Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences), Professor WANG Hesheng, associate professor PONG Hongli, associate professor QIAN Liang (School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University), Professor YANG Zelin, Professor WANG Xuming (School of Physics and Electronic-Electrical Engineering, Ningxia University) for their contribution.

References

  1. [1]
    JIANG H, DUN H C, TONG D, et al. Sand transportation and reverse patterns over leeward face of sand dune [J]. Geomorphology, 2017, 283: 41–47.CrossRefGoogle Scholar
  2. [2]
    ZHANG J, TENG Z J, HUANG N, et al. Surface renewal as a significant mechanism for dust emission [J]. Atmospheric Chemistry and Physics, 2016, 16: 15517–15528.CrossRefGoogle Scholar
  3. [3]
    LYLES L, KRAUSS R K. Threshold velocities and initial particle motion as influenced by air turbulence [J]. Transactions of the Asae, 1971, 14(3): 563–566.CrossRefGoogle Scholar
  4. [4]
    CASTELLINI P, MARTARELLI M, TOMASINI E P. Laser doppler vibrometry: Development of advanced solutions answering to technology’s needs [J]. Mechanical Systems and Signal Processing, 2006, 20(6): 1265–1285.CrossRefGoogle Scholar
  5. [5]
    EL-DIN M G, SMITH D W. Maximizing the enhanced ozone oxidation of kraft pulp mill effluents in an impinging-jet bubble column [J]. Ozone: Science & Engineering, 2001, 23(6): 479–493.CrossRefGoogle Scholar
  6. [6]
    DONG Z B, WANG H T, LIU X P, et al. Velocity profile of a sand cloud blowing over a gravel surface [J]. Geomorphology, 2002, 45(3/4): 277–289.CrossRefGoogle Scholar
  7. [7]
    WILLERT C E, GHARIB M. Digital particle image velocimetry [J]. Experiments in Fluids, 1991, 10(4): 181–193.CrossRefGoogle Scholar
  8. [8]
    YANG P, DONG Z B, QIAN G Q. Height profile of the mean velocity of an aeolian saltating cloud: Wind tunnel measurements by Particle Image Velocimetry [J]. Geomorphology, 2007, 89(3): 320–334.CrossRefGoogle Scholar
  9. [9]
    GREELEY R, WILLIAMS S H, MARSHAL J R. Velocities of windblown particles in saltation: Preliminary laboratory and field measurements [J]. Developments in Sedimentology, 1983, 38: 133–148.CrossRefGoogle Scholar
  10. [10]
    ZOU X Y, WANG Z L, HAO Q Z, et al. The distribution of velocity and energy of saltating sand grains in a wind tunnel [J]. Geomorphology, 2001, 36(3/4): 155–165.CrossRefGoogle Scholar
  11. [11]
    GOLDMAN D E, KOMSUOGLU H, KODITSCHEK D E. March of the SandBots [J]. IEEE Spectrum, 2009, 46(4): 30–35.CrossRefGoogle Scholar
  12. [12]
    GALLOWAY K C, CLARK J E, YIM M, et al. Experimental investigations into the role of passive variable compliant legs for dynamic robotic locomotion [C]//IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011: 1243–1249.Google Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xin Ma (马 鑫)
    • 1
  • Shunge Deng (邓顺戈)
    • 1
  • Xinwan Li (李新碗)
    • 1
    • 2
    • 3
  1. 1.State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.University of Michigan - Shanghai Joint InstituteShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Shanghai Institute for Advanced Communication and Data ScienceShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations