Projection-Based Coverage Algorithms in 3D Camera Sensor Networks for Indoor Objective Tracking

  • Yi Hong
  • Yongcai WangEmail author
  • Yuqing Zhu
  • Deying Li
  • Mengjie Chang
  • Zhibo Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)


For persistent objective tracking based on camera sensor networks, sensor deployment and scheduling are challenging problems especially in indoor environments. In this paper, we focus on the indoor objective tracking in three-dimensional (3D) camera sensor networks (CSNs) and aim to design the scheduling strategies to guarantee the coverage quality of the objective trajectory. We introduce the active-period-minimizing scheduling problem in CSNs for indoor objective tracking, with the goal of minimizing the total active durations of all the sensors. To solve the problem, we propose a pair of projection scheduling algorithms from the perspectives of the sensors and the trajectory correspondingly. To evaluate the algorithm performance on the time efficiency, we conduct extensive simulation experiments and their results indicate the proposed algorithms’ advantages on time efficiency.


Objective tracking Camera sensor networks Coverage quality Projection scheduling 



This research was partly supported by National Natural Science Foundation of China under Grant 11671400 and 61672524, General Project of Science and Technology Plan of Beijing Municipal Education Commission (KM201910017006), Program of Beijing Excellent Talents Training for Young Scholar (2016000020124G056).


  1. 1.
    Han, K., Xiang, L., Luo, J., Liu, Y.: Minimum-energy connected coverage in wireless sensor networks with omni-directional and directional features. In: Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 85–94. ACM (2012)Google Scholar
  2. 2.
    Deif, D.S., Gadallah, Y.: Classification of wireless sensor networks deployment techniques. IEEE Commun. Surv. Tutorials 16(2), 834–855 (2014)CrossRefGoogle Scholar
  3. 3.
    Costa, D.G., Guedes, L.A.: The coverage problem in video-based wireless sensor networks: a survey. Sensors 10, 8215–8247 (2010)CrossRefGoogle Scholar
  4. 4.
    Brown, T., Wang, Z.H., Shan, T., Wang, F., Xue, J.X.: Obstacle-aware wireless video sensor network deployment for 3D indoor monitoring. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2017)Google Scholar
  5. 5.
    Cardei, M., Thai, M.T., Li, Y.S., Wu, J.: Energy-efficient target coverage in wireless sensor networks. Proceedings of IEEE International Conference on Computer Communications (INFOCOM), pp. 1976–1982 (2005)Google Scholar
  6. 6.
    Guo, L., Li, D., Wang, Y., et al.: Maximisation of the number of \(\beta \)-view covered targets in visual sensor networks. IJSNet 29(4), 226–241 (2019)CrossRefGoogle Scholar
  7. 7.
    Wang, Y., Song, L., et al.: IntenCT: efficient multi-target counting and tracking by binary proximity sensors. In: SECON, pp. 1–9 (2016)Google Scholar
  8. 8.
    Cardei, M., Wu, J.: Energy-efficient coverage problems in wireless ad-hoc sensor networks. Comput. Commun. 29(4), 413–420 (2006)CrossRefGoogle Scholar
  9. 9.
    Cardei, M., Du, D.Z.: Improving wireless sensor network lifetime through power aware organization. Wireless Netw. 11(3), 333–340 (2005)CrossRefGoogle Scholar
  10. 10.
    Xu, S., Lyu, W., Li, H.: Optimizing coverage of 3D wireless multimedia sensor networks by means of deploying redundant sensors. Int. J. Adv. Stud. Comput. Sci. Eng. 4(9), 28 (2015)Google Scholar
  11. 11.
    Wang, Y., Cao, G.H.: On full-view coverage in camera sensor networks. In: Proceedings of IEEE International Conference on Computer Communications (INFOCOM), pp. 1781–1789 (2011)Google Scholar
  12. 12.
    Garey, M.R., Johnson, D.S.: Strong NP-completeness results: motivation, examples, and implications. J. ACM 25, 499–508 (1978)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yi Hong
    • 1
  • Yongcai Wang
    • 2
    Email author
  • Yuqing Zhu
    • 3
  • Deying Li
    • 2
  • Mengjie Chang
    • 4
  • Zhibo Chen
    • 1
  1. 1.School of Information Science and TechnologyBeijing Forestry UniversityBeijingPeople’s Republic of China
  2. 2.School of Information, Renmin University of ChinaBeijingPeople’s Republic of China
  3. 3.Department of Computer ScienceCalifornia State University Los AngelesLos AngelesUSA
  4. 4.Information Engineering College, Beijing Institute of Petrochemical TechnologyBeijingPeople’s Republic of China

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