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Three-dimensional Voronoi Diagram–based Self-deployment Algorithm in IoT Sensor Networks

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Abstract

With the rapid development of 4G/5G technology and the Internet of Things (IoT), data security and privacy problems are becoming more serious. Wireless sensor networks (WSNs), as the main data source of IoT, are an important stage to ensure data availability and data privacy protection. In this paper, a novel deployment algorithm for 3D WSNs based on the Voronoi diagram is proposed. The algorithm uses the characteristics of adjacency and fast partition of the Voronoi diagram to realize fast division of the 3D monitoring area, calculates the center of each Voronoi area as the latest position of node, repeatedly builds the Voronoi diagram to maximize the coverage of the monitoring area, and maximizes the availability and integrity of data. At the same time, the 4G/5G communication technology is used to realize communication between nodes, and data encryption is used to improve data security. An improved algorithm is also proposed to adapt to different deployment conditions. In this paper, data and privacy security are protected from data sources, and the effectiveness of the algorithm is tested by computer simulation.

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References

  1. Tan YA, Xu X, Liang C, Zhang X, Zhang Q, Li Y (2018) An end-to-end covert channel via packet dropout for mobile networks. Int J Distrib Sens Netw 14(5):155014771877956. https://doi.org/10.1177/1550147718779568

    Article  Google Scholar 

  2. Liang C, Wang X, Zhang X, Zhang Y, Sharif K, Tan YA (2018) A payload-dependent packet rearranging covert channel for mobile VoIP traffic. Inf Sci 465:162–173

    Article  Google Scholar 

  3. Zhang X, Liang C, Zhang Q, Li Y, Zheng J, Tan YA (2018) Building covert timing channels by packet rearrangement over mobile networks. Inf Sci 445-446:66–78

    Article  MathSciNet  Google Scholar 

  4. Tan YA, Xue Y, Liang C, Zheng J, Zhang Q, Zheng J, Li Y (2018) A root privilege management scheme with revocable authorization for android devices. J Netw Comput Appl 107:69–82

    Article  Google Scholar 

  5. Liang C, Tan YA, Zhang X, Wang X, Zheng J, Zhang Q (2018) Building packet length covert channel over mobile VoIP traffics. J Netw Comput Appl 118:144–153

    Article  Google Scholar 

  6. Xue Y, Tan YA, Liang C, Zhang CY, Zheng J (2018) An optimized data hiding scheme for deflate codes. Soft Comput 22(13):4445–4455

    Article  Google Scholar 

  7. Guan Z, Li J, Wu L, Zhang Y, Wu J, Du X (2017) Achieving efficient and secure data acquisition for cloud-supported internet of things in smart grid. IEEE Internet Things J 4(6):1934–1944

  8. Guan Z, Si G, Zhang X, Wu L, Guizani N, Du X, Ma Y (2018) Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities. IEEE Commun Mag 56(7):1–7

    Article  Google Scholar 

  9. Rawat P, Singh KD, Chaouchi H, Bonnin JM (2014) Wireless sensor networks: a survey on recent developments and potential synergies. J Supercomput 68(1):1–48

    Article  Google Scholar 

  10. Winkler M, Tuchs KD, Hughes K, Barclay G (2008) Theoretical and practical aspects of military wireless sensor networks. Journal of Telecommunications & Information Technology 12(4):263–264

    Google Scholar 

  11. Fan L, Lei X, Yang N, Duong TQ, Karagiannidis GK (2016) Secure multiple amplify-and-forward relaying with cochannel interference. IEEE J Sel Top Sign Proces 10(8):1494–1505

    Article  Google Scholar 

  12. Li Y, Wang G, Nie L, Wang Q, Tan W (2018) Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recogn 75:51–62

  13. Lin W, Wu Z, Lin L, Wen A, Li J (2017) An ensemble random forest algorithm for insurance big data analysis. Ieee Access 5:16568–16575

  14. Zhou Z, Dong M, Ota K, Wang G, Yang LT (2016) Energy-efficient resource allocation for d2d communications underlaying cloud-ran-based lte-a networks. IEEE Internet Things J 3(3):428–438

    Article  Google Scholar 

  15. Lin W, Xu SY, Li J, Xu L, Peng Z (2015) Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft Comput 27(7):1–14

    MATH  Google Scholar 

  16. He P, Deng Z, Wang H, Liu Z (2016) Model approach to grammatical evolution: theory and case study. Soft Comput 20(9):3537–3548

    Article  MATH  Google Scholar 

  17. Zheng J, Tan YA, Zhang Q, Zhang X, Zhu L, Zhang Q (2018) Cross-cluster asymmetric group key agreement for wireless sensor networks. Sci China Inf Sci 61(4):048103

    Article  MathSciNet  Google Scholar 

  18. Wang H, Wang W, Cui Z, Zhou X, Zhao J, Li Y (2018) A new dynamic firefly algorithm for demand estimation of water resources. Inf Sci 438:95–106

    Article  MathSciNet  Google Scholar 

  19. Lin TY, Santoso HA, Wu KR, Wang GL (2017) Enhanced deployment algorithms for heterogeneous directional mobile sensors in a bounded monitoring area. IEEE Trans Mob Comput 16(3):744–758

  20. Sun Z, Zhang Q, Li Y, & Tan YA (2016). Dppdl: a dynamic partial-parallel data layout for green video surveillance storage. IEEE Trans Circuits Syst Video Technol, pp(99):1–1

  21. Sung TW, Yang CS (2014) Voronoi-based coverage improvement approach for wireless directional sensor networks. J Netw Comput Appl 39(1):202–213

    Article  Google Scholar 

  22. Watfa MK (2006) Coverage issues in wireless sensor networks[D]. The University of Oklahoma, Oklahoma

  23. Yu X, Zhang C, Xue Y, Zhu H, Li Y, Tan YA (2017) An extra-parity energy saving data layout for video surveillance. Multimedia Tools Appl 77(C):1–21

    Google Scholar 

  24. Nauman A (2010) Optimizing coverage in 3D wireless sensor networks. In: Tan YK (ed) Smart wireless sensor networks. InTech, Available from: http://www.intechopen.com/books/smart-wireless-sensor-networks/optimizing-coverage-in-3d-wireless-sensornetworks. Accessed 2 Dec 2018

  25. Li F, Luo J, Wang W, He Y (2015) Autonomous deployment for load balancing, surface coverage in sensor networks. Wireless Commun IEEE Trans on Wireless 14(1):279–293

    Article  Google Scholar 

  26. Brown T, Wang Z, Shan T, Wang F, & Xue J (2016). On wireless video sensor network deployment for 3D indoor space coverage. Southeastcon. IEEE pp. 1–8

  27. Temel S, Unaldi N, Kaynak O (2013) 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 Hum 44(1):111–120

    Article  Google Scholar 

  28. Akbarzadeh V, Gagne C, Parizeau M, Argany M, Mostafavi MA (2013) Probabilistic sensing model for sensor placement optimization based on line-of-sight coverage. IEEE Trans Instrum Meas 62(2):293–303

    Article  Google Scholar 

  29. Topcuoglu HR, Ermis M, Sifyan M (2011) Positioning and utilizing sensors on a 3-d terrain part ii—solving with a hybrid evolutionary algorithm. IEEE Trans Syst Man Cybern Part C Appl Rev 41(4):470–480

    Article  Google Scholar 

  30. Li X, Ci L, Yang M, Tian C, Li X (2012) Deploying three-dimensional mobile sensor networks based on virtual forces algorithm. Commun Comput Inform Sci 334:204–216

    Article  Google Scholar 

  31. Boufares N, Khoufi I, Minet P, Saidane L, Ben Saied Y (2015) 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

  32. Boufares N, Minet P, Khoufi I, Saidane L (2017) 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

  33. Yang H, Xunbo LI, Wang Z, Wenjie YU, Huang B (2016) 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

    Article  Google Scholar 

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Acknowledgments

L.T. and X.T. designed the algorithms, simulation model, and experiments, and performed all tests and analyses for the research work. X.T. was also responsible for preparing the initial draft of the manuscript. A.H. and M.W. contributed to verifying the work and finalizing the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (61702020) and its special supporting fund (PXM2018_014213_000033), Beijing Natural Science Foundation (4172013), and Beijing Technology and Business University graduate research capacity enhancement program.

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Correspondence to Li Tan.

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Tang, X., Tan, L., Hussain, A. et al. Three-dimensional Voronoi Diagram–based Self-deployment Algorithm in IoT Sensor Networks. Ann. Telecommun. 74, 517–529 (2019). https://doi.org/10.1007/s12243-018-0686-8

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  • DOI: https://doi.org/10.1007/s12243-018-0686-8

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