Advertisement

An Improved DV-Hop Scheme Based on Path Matching and Particle Swarm Optimization Algorithm

  • Qinqin Shi
  • Qiang Xu
  • Jianping Zhang
Article
  • 45 Downloads

Abstract

Distance vector hop (DV-Hop) is a frequently-used localization technology for wireless sensor networks. The traditional DV-Hop scheme estimates the node–anchor distance depending on the hop-count between the network nodes. It is the advantage of the scheme because no costive direct range finding is needed, but it still is the disadvantage of the scheme because the heterogeneity of network topology will make the node–anchor distance estimation precision poor and the localization precision unstable. Since the heterogeneity of network topology is very common due to random node deployment in real applications, the effectiveness of DV-Hop scheme in these applications becomes difficult to confirm and the algorithm needs applicability improvement. Focusing on above problem of the traditional DV-Hop, improved strategies are provided. A path matching algorithm is presented to find out the optimal anchor-anchor shortest path, which is used to determine the average hop distance between an unknown node and its target anchor independently, aiming at making the estimated node-anchor distance as close as possible to the real distance; furtherly, a modified particle swarm optimization algorithm is presented to optimize the initial position of each unknown node, aiming at improving the whole node localization accuracy of the network. Simulations are carried out on different network topologies both in square area and in C-shaped area, and comparisons are made for our scheme with the traditional DV-Hop and the other three existed representative improved schemes. Results show that our scheme has better performance both on distance estimation accuracy and on average node localization accuracy.

Keywords

DV-Hop Node localization Path matching Trilateration Particle swarm optimization algorithm 

Notes

Acknowledgements

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

References

  1. 1.
    Belli, L., Cirani, S., Davoli, L., Ferrari, G., Melegari, L., & Picone, M. (2016). Applying security to a big stream cloud architecture for the internet of things. International Journal of Distributed Systems and Technologies, 7(1), 37–58.CrossRefGoogle Scholar
  2. 2.
    Shahra, E. Q., Sheltami, T. R., & Shakshuki, E. M. (2017). A comparative study of range-free and range-based localization protocols for wireless sensor network: Using COOJA simulator. International Journal of Distributed Systems and Technologies, 8(1), 1–16.CrossRefGoogle Scholar
  3. 3.
    Stanoev, A., Filiposka, S., In, V., & Kocarev, L. (2016). Cooperative method for wireless sensor network localization. Ad Hoc Networks, 40, 61–72.CrossRefGoogle Scholar
  4. 4.
    Singh, S. P., & Sharma, S. (2015). Range free localization techniques in wireless sensor networks: A review. Procedia Computer Science, 57, 7–16.CrossRefGoogle Scholar
  5. 5.
    Mao, G., Fidan, B., & Anderson, B. D. O. (2007). Wireless sensor network localization techniques. Computer Networks, 51(10), 2529–2553.CrossRefGoogle Scholar
  6. 6.
    Almuzaini, K. K., & Gulliver, A. (2010). Range-based localization in wireless networks using density-based outlier detection. Wireless Sensor Network, 2(11), 807–814.CrossRefGoogle Scholar
  7. 7.
    Li, M., & Liu, Y. H. (2010). Rendered path: Range-free localization in anisotropic sensor networks with holes. IEEE/ACM Transaction Networks., 18(1), 320–332.CrossRefGoogle Scholar
  8. 8.
    Bulusu, N., Heidemann, J., & Estrin, D. (2008). GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications Magazine, 7(5), 28–34.CrossRefGoogle Scholar
  9. 9.
    Niculescu, D., & Nath, B. (2003). DV based positioning in ad hoc networks. Telecommunication Systems, 22(1–4), 267–280.CrossRefGoogle Scholar
  10. 10.
    Nagpal, R., Shrobe, H., & J. Bachrach. (2003). Organizing a global coordinate system from local information on an ad hoc sensor network. In Proceedings of the 2nd international workshop on information processing in sensor networks (IPSN ‘03). Palo Alto, CA, United States, April 2003 (pp. 1–16).Google Scholar
  11. 11.
    Ma, Z., Liu, Y., & Shen, B. (2008). Distributed locating algorithm for wireless sensor networks-MDS-MAP(D). Journal on Communications, 29(6), 58–62.Google Scholar
  12. 12.
    Kumar, S., & Lobiyal, D. K. (2014). Power efficient range-free localization algorithm for wireless sensor networks. Wireless Networks, 20(4), 681–694.CrossRefGoogle Scholar
  13. 13.
    Zhang, Y. J., Wang, K., Yuan, S. F., Yang, H., Chen, Z. X., & Ge, L. S. (2012). Research of WSN node localization algorithm based on weighted DV-HOP. In Proceedings of the Chinese control and decision conference (CCDC’2012), Taiyuan, China, May 2012 (pp. 3826–3829).Google Scholar
  14. 14.
    Zhang, W., Yang, X., & Song, Q. (2015). Improved dv-hop algorithm based on artificial bee colony. International Journal of Control & Automation, 8(11), 135–144.CrossRefGoogle Scholar
  15. 15.
    Yang, X., & Zhang, W. (2016). An improved dv-hop localization algorithm based on bat algorithm. Cybernetics & Information Technologies, 16(1), 89–98.CrossRefGoogle Scholar
  16. 16.
    Wang, Z., & Chen, X. (2015). Node localization of wireless sensor networks based on DV-Hop and Steffensen iterative method. International Journal of Future Generation Communication & Networking, 8(2), 1–8.CrossRefGoogle Scholar
  17. 17.
    Peng, B., & Li, L. (2015). An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cognitive Neurodynamics, 9(2), 249–256.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kaur, A., Kumar, P., & Gupta, G. (2016). A novel DV-Hop algorithm based on Gauss–Newton method. In Proceedings of the 4th international conference on parallel, distributed and grid computing (PDGC’2016), Waknilshat, Solan-173234, Himachal Pradesh, India, December 2016 (pp. 625–629).Google Scholar
  19. 19.
    Tomic, S., & Mezei, I. (2016). Improvements of DV-Hop localization algorithm for wireless sensor networks. Telecommunication System, 61(1), 93–106.CrossRefGoogle Scholar
  20. 20.
    Hu, Y., & Li, X. (2013). An improvement of DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 53(1), 13–18.CrossRefGoogle Scholar
  21. 21.
    Liu, Y., Chen, J., & Xu, Z. (2017). Improved DV-Hop localization algorithm based on bat algorithm in wireless sensor networks. KSII Transactions on Internet and Information Systems, 11(1), 215–236.Google Scholar
  22. 22.
    Mehrabi, M., Taheril, H., & Taghdiri, P. (2017). An improved DV-Hop localization algorithm based on evolutionary algorithms. Telecommunication Systems, 64, 639–647.CrossRefGoogle Scholar
  23. 23.
    Eslami, M., Shareef, H., Khajehzadeh, M., & Mohamed, A. (2012). A survey of the state of the art in particle swarm optimization. Research Journal of Applied Sciences, Engineering and Technology, 4(9), 1181–1197.Google Scholar
  24. 24.
    Dharavath, R., & Dharavath, R. (2016). Entity resolution-based Jaccard similarity coefficient for heterogeneous distributed database. In Proceedings of the 2nd international conference on computer and communication technologies (IC3T ’2015), Hyderabad, India, July 2015 (pp. 497–507).Google Scholar
  25. 25.
    Langendoen, K., & Reijers, N. (2003). Distributed localization in wireless sensor networks: A quantitative comparison. Computer Networks, 43, 499–518.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and Information EngineeringShanghai Institute of TechnologyShanghaiChina
  2. 2.Suzhou Zhongke Advanced Technology Research Institute Co., Ltd.SuzhouChina
  3. 3.Sino Parking Tech Co., Ltd.ShanghaiChina

Personalised recommendations