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A Weighted Voronoi Diagram Based Self-deployment Algorithm for Heterogeneous Mobile Sensor Network in Three-Dimensional Space

  • Li TanEmail author
  • Xiaojiang Tang
  • Minghua Yang
  • Haoyu Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1101)

Abstract

For the node deployment problem in three-dimensional heterogeneous sensor networks, the traditional virtual force method is prone to local optimization and the parameters required for calculation are uncertain. A spatial deployment algorithm for 3D mobile wireless sensor networks based on weighted Voronoi diagram is proposed (TDWVADA) to solve the problem. Based on the positions and weights of all nodes in the monitoring area, a three-dimensional weighted Voronoi diagram is constructed. Next, the central position of each node’s the Voronoi region is calculated and the position is regarded as target position of the node movement. Each node moves from the original position to the target position to complete one iteration. After multiple iterations, each node is moved to the optimal deployment location and network coverage is improved. In view of the initial centralized placement of sensor nodes, the addition of virtual force factors is added to the TWDVDA algorithm. An improved algorithm TDWVADA-I was proposed. The algorithm enables nodes that are centrally placed to spread quickly and speeds up deployment. The simulation results show that TDWVADA and TDWVADA-I effectively improve the network coverage of the monitored area compared to the virtual force algorithm and the unweighted Voronoi method. Compared with the virtual force method, the coverage of TDWVADA has increased from 90.53% to 96.70%, and the coverage of TDWVADA-I has increased from 81.12% to 96.56%. Compared with the Voronoi diagram method, the coverage of TDWVADA has increased from 85.01% to 96.70%, and the coverage of TDWVADA-I has increased from 80.82% to 96.56%. TDWVADA and TDWVADA-I also greatly reduce the energy consumption of the network. Experimental results demonstrate the effectiveness of the algorithms.

Keywords

Heterogeneous Wireless Sensor Networks (HWSNs) 3D coverage Area coverage Voronoi diagram Energy consumption 

Notes

Acknowledgement

This research was funded by the National Natural Science Foundation of China grant number (61702020), Beijing Natural Science Foundation grant number (4172013) and Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund grant number (L182007).

References

  1. 1.
    Aguirre, E., Lopez-Iturri, P., Azpilicueta, L., et al.: Design and implementation of context aware applications with wireless sensor network support in urban train transportation environments. IEEE Sens. J. 17(1), 169–178 (2017)CrossRefGoogle Scholar
  2. 2.
    Jia, G., Han, G., Rao, H., et al.: Edge computing-based intelligent manhole cover management system for smart cities. IEEE Internet Things J. PP(99), 1 (2017)Google Scholar
  3. 3.
    Manju, Chand, S., Kumar, B.: Maximising network lifetime for target coverage problem in wireless sensor networks. IET Wirel. Sens. Syst. 6(6), 192–197 (2016)CrossRefGoogle Scholar
  4. 4.
    Fosalau, C., Zet, C., Petrisor, D.: Implementation of a landslide monitoring system as a wireless sensor network. In: Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 1–6. IEEE (2016)Google Scholar
  5. 5.
    Radani, Z.M., Samavi, S., Fooladgar, F.: Multi plane volumetric coverage in wireless visual sensor network. In: Electrical Engineering, pp. 758–762. IEEE (2012)Google Scholar
  6. 6.
    Huang, C.-F., Tseng, Y.-C., Lo, L.-C.: The coverage problem in three-dimensional wireless sensor networks. J. Interconnection Netw. 8(3), 209–227 (2007)CrossRefGoogle Scholar
  7. 7.
    Nauman, A.: Optimizing coverage in 3D wireless sensor networks. In: Smart Wireless Sensor Networks, pp. 189–204 (2010)Google Scholar
  8. 8.
    Li, F., Luo, J., Wang, W., et al.: Autonomous deployment for load balancing, -surface coverage in sensor networks. IEEE Trans. Wirel. Commun. 14(1), 279–293 (2015)CrossRefGoogle Scholar
  9. 9.
    Brown, T., Wang, Z., Shan, T., et al.: On wireless video sensor network deployment for 3D indoor space coverage. In: Southeastcon, pp. 1–8. IEEE (2016)Google Scholar
  10. 10.
    Temel, S., Unaldi, N., Kaynak, O.: On deployment of wireless sensors on 3-D terrains to maximize sensing coverage by utilizing cat swarm optimization with wavelet transform. IEEE Trans. Syst. Man Cybern. Syst. 44(1), 111–120 (2013)CrossRefGoogle Scholar
  11. 11.
    Akbarzadeh, V., Gagne, C., Parizeau, M., et al.: Probabilistic sensing model for sensor placement optimization based on line-of-sight coverage. IEEE Trans. Instrum. Measur. 62(2), 293–303 (2013)CrossRefGoogle Scholar
  12. 12.
    Topcuoglu, H.R., Ermis, M., Sifyan, M.: Positioning and utilizing sensors on a 3-D terrain part ii—solving with a hybrid evolutionary algorithm. IEEE Trans. Syst. Man Cybern. Part C 41(4), 470–480 (2011)CrossRefGoogle Scholar
  13. 13.
    Li, X., Ci, L., Yang, M., Tian, C., Li, X.: Deploying three-dimensional mobile sensor networks based on virtual forces algorithm. In: Wang, R., Xiao, F. (eds.) CWSN 2012. CCIS, vol. 334, pp. 204–216. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-36252-1_19CrossRefGoogle Scholar
  14. 14.
    Boufares, N., Khoufi, I., Minet, P., et al.: Three dimensional mobile wireless sensor networks redeployment based on virtual forces. In: 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), Dubrovnik, pp. 563–568. IEEE (2015)Google Scholar
  15. 15.
    Boufares, N., Minet, P., Khoufi, I., et al.: Covering a 3D flat surface with autonomous and mobile wireless sensor nodes. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, pp. 1628–1633. IEEE (2017)Google Scholar
  16. 16.
    Yang, H., Li, X., Wang, Z., et al.: A novel sensor deployment method based on image processing and wavelet transform to optimize the surface coverage in WSNs. Chin. J. Electron. 25(3), 495–502 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Li Tan
    • 1
    Email author
  • Xiaojiang Tang
    • 1
  • Minghua Yang
    • 1
  • Haoyu Wang
    • 1
  1. 1.Beijing Technology and Business UniversityBeijingChina

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