Personal and Ubiquitous Computing

, Volume 22, Issue 3, pp 597–613 | Cite as

Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks

  • Haodi Ping
  • Zhangbing Zhou
  • Zhensheng Shi
  • Taj Rahman
Original Article


With the development and wide adoption of industrial wireless sensor networks to support various domain applications, the boundary detection of continuous objects has become an important research challenge, where improving the accuracy of boundary area while reducing the energy consumption are the first-class citizens to be considered. To address this research challenge, this article proposes a two-stage boundary face detection mechanism, where sensor nodes are duty-cycled and to be deployed in a dense fashion. When the occurrence of potential events are recognized using the initially activated sensor nodes, the boundary faces of continuous objects are constructed through adopting planarization algorithms. Thereafter, sensor nodes contained in certain boundary faces are examined, where their sensory data are estimated using spatial interpolation methods. Certain sensor nodes are selected to be woken up, only when their sensory data suggest that they should be more appropriate candidates of boundary sensor nodes. Consequently, the size of boundary faces is reduced, and this coarse-to-fine refinement procedure iterates, until all sensor nodes contained in the boundary faces have been examined. Experimental evaluation result shows that the boundary area can be refined significantly and be more precise, where the half of the initial boundary face area should be reduced in most situations.


Continuous objects Boundary faces Boundary area localization Planarization algorithms Duty-cycled wireless sensor networks 


Funding information

This work was supported partially by the National Natural Science Foundation of China (No. 61379126, 61662021 and 61772479) and by the Fundamental Research Funds for the Central Universities (China University of Geosciences (Beijing), China).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Haodi Ping
    • 1
  • Zhangbing Zhou
    • 1
    • 2
  • Zhensheng Shi
    • 3
  • Taj Rahman
    • 4
  1. 1.School of Information EngineeringChina University of Geosciences (Beijing)BeijingChina
  2. 2.Computer Science DepartmentTELECOM SudParisEvryFrance
  3. 3.PetroChina Research Institute of Petroleum Exploration and DevelopmentLangfangChina
  4. 4.University of Science and Technology BeijingBeijingChina

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