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Optimization for DV-Hop type of localization scheme in wireless sensor networks

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Abstract

Distance Vector Hop (DV-Hop) is a range-free scheme used for node localization in wireless sensor networks (WSNs). The original DV-Hop scheme localizes the unknown nodes depending on a number of anchor nodes’ known position information and the multi-hop relationship among nodes. It is very popular and can meet most application requirements as the network is isotropous. However, it becomes powerless while the network is anisotropic due to the natural defects of its ranging strategy. In view of such problems of the traditional DV-Hop, we provide a scheme aiming to improve the original DV-Hop. In our scheme, an improved cosine similarity parameter is used to measure the similarity between path pairs, and the anchor–anchor path which is most like the path from the unknown node to the target anchor is selected to compute the average hop distance of the node-anchor path independently. Then, an improved particle swarm optimization and simulated annealing hybrid algorithm is adapted to improve the position accuracy of the initial position of an unknown node, which has been derived by the trilateration algorithm used in the original DV-Hop scheme. Based on the simulation result, in comparison with the original DV-Hop scheme and another two existed improved schemes, our proposed scheme can perform much better both on the distance estimation accuracy and on the final node localization accuracy. Thereby, our proposed scheme is a feasible and optimized choice for node localization in WSNs.

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References

  1. Ayadi A, Ghorbel O, Obeid AM (2017) Outlier detection approaches for wireless sensor networks—a survey. Comput Netw 129:319–333. https://doi.org/10.1016/j.comnet.2017.10.007

    Article  Google Scholar 

  2. Watt AJ, Phillips MR, Campbell CE-A, Wells I, Hole S (2019) Wireless sensor networks for monitoring underwater sediment transport. Sci Total Environ 167:160–165. https://doi.org/10.1016/j.scitotenv.2019.02.369

    Article  Google Scholar 

  3. Chowdhury TJS, Elkin C, Devabhaktuni V (2016) Advances on localization techniques for wireless sensor networks: a survey. Comput Netw 110:284–305. https://doi.org/10.1016/j.comnet.2016.10.006

    Article  Google Scholar 

  4. Kumar S, Lobiyal DK (2014) Power efficient range-free localization algorithm for wireless sensor networks. Wirel Netw 20(4):681–694. https://doi.org/10.1007/s11276-013-0630-9

    Article  Google Scholar 

  5. Canales J (2018) Navigating the history of GPS. Nat Electron 1:610–611

    Article  Google Scholar 

  6. Larios DF, Barbancho J, Molina FJ, Leon C (2012) LIS: localization based on an intelligent distributed fuzzy system applied to a WSN. Ad Hoc Netw 10(3):604–622. https://doi.org/10.1016/j.adhoc.2011.11.003

    Article  Google Scholar 

  7. Han G, Xu H, Duong TQ, Jiang J, Hara T (2013) Localization algorithms of wireless sensor networks: a survey. Telecommun Syst 52(4):2419–2436. https://doi.org/10.1007/s11235-011-9564-7

    Article  Google Scholar 

  8. Manickam M, Sudha S (2019) Range-based localisation of a wireless sensor network using Jaya algorithm. IET Sci Meas Technol 13(7):937–943. https://doi.org/10.1049/iet-smt.2018.5225

    Article  Google Scholar 

  9. Zaidi S, El Assaf A, Affes S, Kandil N (2016) Accurate range-free localization in multi-hop wireless sensor networks. IEEE Trans Commun 64(9):3886–3900. https://doi.org/10.1109/TCOMM.2016.2590436

    Article  Google Scholar 

  10. Niculescu D, Nath B (2001) Ad hoc positioning system (APS). In: 2001 IEEE Global Telecommunications Conference (GLOBECOM). IEEE, pp 2926–2931

  11. Niculescu D, Nath B (2003) DV based positioning in ad hoc networks. Telecommun Syst 22(1–4):267–280

    Article  Google Scholar 

  12. Mass-Sanchez J, Ruiz-Ibarra E, Espinoza-Ruiz A, Rizo-Dominguez L (2018) A comparative of range free localization algorithms and dv-hop using the particle swarm optimization algorithm. In: 2018 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). IEEE, pp 150–157

  13. Rekha Kumar G, Rai MK (2019) An advanced DV-Hop localization algorithm for random mobile nodes in wireless sensor networks. Arab J Sci Eng 44(11):9787–9803. https://doi.org/10.1007/s13369-019-04082-4

    Article  Google Scholar 

  14. Ren K, Wu M (2019) DV-Hop algorithm for adaptive hop-count improvement and average hop distance. J High-Speed Netw 25(2):127–137

    Article  Google Scholar 

  15. Xue D (2019) Research of localization algorithm for wireless sensor network based on DV-Hop. EURASIP J Wirel Commun Netw. https://doi.org/10.1186/s13638-019-1539-5

    Article  Google Scholar 

  16. Zhou C, Yang Y, Wang Y (2019) DV-Hop localization algorithm based on bacterial foraging optimization for wireless multimedia sensor networks. Multimed Tools Appl 78(4):4299–4309. https://doi.org/10.1007/s11042-018-5674-5

    Article  Google Scholar 

  17. Cai X, Geng S, Wang P, Wang L, Wu Q (2019) Fast triangle flip bat algorithm based on curve strategy and rank transformation to improve DV-Hop performance. KSII Trans Internet Inf Syst 13(12):5785–5804. https://doi.org/10.3837/tiis.2019.12.001

    Article  Google Scholar 

  18. Xue D (2019) Research on range-free location algorithm for wireless sensor network based on particle swarm optimization. EURASIP J Wirel Commun Netw. https://doi.org/10.1186/s13638-019-1540-z

    Article  Google Scholar 

  19. Mehrabi M, Taheri H, Taghdiri P (2017) An improved DV-Hop localization algorithm based on evolutionary algorithms. Telecommun Syst 64(4):639–647. https://doi.org/10.1007/s11235-016-0196-9

    Article  Google Scholar 

  20. Sharma G, Kumar A (2018) Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks. Telecommun Syst 67(2):163–178. https://doi.org/10.1007/s11235-017-0328-x

    Article  MathSciNet  Google Scholar 

  21. Cui L, Xu C, Li G, Ming Z, Feng Y, Lu N (2018) A high accurate localization algorithm with DV-Hop and differential evolution for wireless sensor network. Appl Soft Comput 68:39–52. https://doi.org/10.1016/j.asoc.2018.03.036

    Article  Google Scholar 

  22. Ge C, Susilo W, Liu Z, Xia J, Szalachowski P, Fang L (2020) Secure keyword search and data sharing mechanism for cloud computing. IEEE Trans Depend Secure Comput 5:5–9. https://doi.org/10.1109/TDSC.2020.2963978

    Article  Google Scholar 

  23. Ren Y, Leng Y, Qi J (2021) Multiple cloud storage mechanism based on block chain in smart homes. Futur Gener Comput Syst 115:304–313. https://doi.org/10.1016/j.future.2020.09.019

    Article  Google Scholar 

  24. Langendoen K, Reijers N (2003) Distributed localization in wireless sensor networks: a quantitative comparison. Comput Netw 43(4):499–518. https://doi.org/10.1016/S1389-1286(03)00356-6

    Article  MATH  Google Scholar 

  25. Venter G, Sobieszczanski-Sobieski J (2003) Particle swarm optimization. AIAA J 41(8):1583–1589

    Article  Google Scholar 

  26. Gumaida BF, Luo J (2019) A hybrid particle swarm optimization with a variable neighborhood search for the localization enhancement in wireless sensor networks. Appl Intell 49(10):3539–3557. https://doi.org/10.1007/s10489-019-01467-8

    Article  Google Scholar 

  27. Siddique N, Adeli H (2016) Simulated annealing, its variants and engineering applications. Int J Artif Intell Tools 25(6):1630001. https://doi.org/10.1142/S0218213016300015

    Article  Google Scholar 

  28. Avidrad F, Nazari M (2017) A new hybrid particle swarm and simulated annealing stochastic optimization method. Appl Soft Comput 60:634–654. https://doi.org/10.1016/j.asoc.2017.07.023

    Article  Google Scholar 

  29. Xia P, Zhang L, Li F (2015) Learning similarity with cosine similarity ensemble. Inf Sci 307:39–52. https://doi.org/10.1016/j.ins.2015.02.024

    Article  MathSciNet  MATH  Google Scholar 

  30. Okada S, Ohzeki M, Taguchi S (2019) Efficient partition of integer optimization problems with one-hot encoding. Sci Rep 5:5–9. https://doi.org/10.1038/s41598-019-49539-6

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by National Nature Science Foundation of China (No. 61103180), Collaborative Innovation Foundation of Shanghai Institute of Technology (No. XTCX2018-15) and Shanghai Alliance Project (LM201973).

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

    Qinqin Shi & Chengji Wu

  2. Suzhou Zhongke Advanced Technology Research Institute Co., Ltd., Suzhou, China

    Qiang Xu

  3. Sino Parking Tech Co. Ltd, Shanghai, China

    Jianping Zhang

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  1. Qinqin Shi
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  2. Chengji Wu
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Correspondence to Jianping Zhang.

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Shi, Q., Wu, C., Xu, Q. et al. Optimization for DV-Hop type of localization scheme in wireless sensor networks. J Supercomput 77, 13629–13652 (2021). https://doi.org/10.1007/s11227-021-03818-0

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