Skip to main content
Log in

Hop-Count Quantization Ranging and Hybrid Cuckoo Search Optimized for DV-HOP in WSNs

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Localization technology occupies a very important position in wireless sensor network (WSN). Distance vector-hop (DV-HOP) algorithm is range-free localization algorithm and has advantages of low overhead and can handle the case where a normal node has less than three neighbor anchors. However, considering the problem of high localization error of DV-HOP algorithm in WSN. An hybrid DV-HOP localization algorithm based on hop-count quantization anchor and modified cuckoo search (HMCS-D) is proposed. The algorithm using the correction factor to correct the number of hops that can reduce the error caused by the recording inaccurate minimum hops. The nodes of the hop neighbor nodes in the network are divided into three disjoint subsets and the distance between the nodes is estimated according geometric method. Then according to the weight of each anchor node to select average jump distance of unknown nodes. Finally, introduced hybrid cuckoo search which can dynamically adjust the search step size and using the search algorithm to calculate the node coordinates instead of the maximum likelihood estimation method. Simulation results show that compare with DV-HOP and cuckoo search DV-HOP algorithm, the average positioning error of HMCS-D algorithm decrease 39.7%, 10.6% respectively. Prove that the HMCS-D algorithm effectively improve the node localization accuracy, reduce the positioning error and without affecting the hardware cost.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Huang, B., Yu, C., & Anderson, B. D. O. (2013). Understanding error propagation in multihop sensor network localization. IEEE Transactions on Industrial Electronics, 60(12), 5811–5819.

    Article  Google Scholar 

  2. Gui, L., Val, T., & Wei, A. (2015). Improvement of range-free localization technology by a novel DV-HOP protocol in wireless sensor networks. Ad Hoc Networks, 24(PB), 55–73.

    Article  Google Scholar 

  3. Zivkovic, M., Nikolic, B., Protic, J., & Popovic, R. (2014). A survey and classification of wireless sensor networks simulators based on the domain of use. Ad Hoc & Sensor Wireless Networks, 20(3–4), 245–287.

    Google Scholar 

  4. Wu, Y., Li, L., Ren, Y., Yi, K., & Yu, N. (2014). A rssi localization algorithm and implementation for indoor wireless sensor networks. Ad Hoc & Sensor Wireless Networks, 22(3), 309–337.

    Google Scholar 

  5. Golestanian, M., & Poellabauer, C. (2016). Localization in heterogeneous wireless sensor networks using elliptical range estimation. In International conference on computing, networking and communications (pp. 1–7). IEEE.

  6. Mass-Sanchez, J., Ruiz-Ibarra, E., Cortez-González, J., Espinoza-Ruiz, A., & Castro, L. A. (2016). Weighted hyperbolic DV-HOP positioning node localization algorithm in WSNS. Wireless Personal Communications, 96, 1–23.

    Google Scholar 

  7. Farmani, M., Moradi, H., & Asadpour, M. (2012). A hybrid localization approach in wireless sensor networks using a mobile beacon and inter-node communication. In IEEE international conference on cyber technology in automation, control, and intelligent systems (pp. 269–274). IEEE.

  8. Henriques, V., & Malekian, R. (2016). Mine safety system using wireless sensor network. IEEE Access, 4, 3511–3521.

    Article  Google Scholar 

  9. Han, S., Gong, Z., Meng, W., Li, C., Zhang, D., & Tang, W. (2016). Automatic precision control positioning for wireless sensor network. IEEE Sensors Journal, 16(7), 2140–2150.

    Article  Google Scholar 

  10. Li, Y. (2014). An improved DV-HOP localization algorithm based on energy-saving non-ranging optimization. Journal of Networks, 9(11), 3182–3188.

    Google Scholar 

  11. Gui, L., Zhang, X., Ding, Q., Shu, F., & Wei, A. (2017). Reference anchor selection and global optimized solution for DV-HOP localization in wireless sensor networks. Wireless Personal Communications, 96(1), 1–11.

    Article  Google Scholar 

  12. Meng, W., Zhao, J., & Ma, S. (2016). High precision multi-communication radius DV-HOP localization algorithm. Communications Technology, 49(06), 701–710.

    Google Scholar 

  13. Cheng, C., Qian, Z., Fu, C., et al. (2015). Genetic optimization DV-HOP localization algorithm based on error distance weighted and hop algorithm selection. Journal of Electronics&Information Technology, 37(10), 2418–2423.

    Google Scholar 

  14. Wu, Y., & Li, J. W. (2012). Improved DV-HOP localization algorithm based on optimal communication radius of nodes. Journal of South China University of Technology (Natural Science Edition), 40(06), 36–42.

    Google Scholar 

  15. Liu, D. F., Zhang, L., Bing, X. Y., Shao, Y. Q., & Xu, B. G. (2017). Localization method based on modified cuckoo difference optimization for wireless sensor networks. Journal of System Simulation, 29(04), 791–797.

    Google Scholar 

  16. Aziz, M. A. E. (2015). Source localization using TDOA and FDOA measurements based on modified cuckoo search algorithm. Wireless Networks, 23(2), 1–9.

    MathSciNet  Google Scholar 

  17. Xue, Y. G., & Deng, H. W. (2014). The cuckoo search algorithm based on dynamic grouping to adjust flight scale. Applied Mechanics and Materials, 543–547, 1822–1826.

    Article  Google Scholar 

  18. Salimi, H., Giveki, D., Soltanshahi, M. A., & Hatami, J. (2012). Extended mixture of MLP experts by hybrid of conjugate gradient method and modified cuckoo search. International Journal of Artificial Intelligence & Applications, 3(1), 1–13.

    Article  Google Scholar 

  19. Zheng, H., & Zhou, Y. (2012). A novel cuckoo search optimization algorithm base on gauss distribution. Journal of Computational Information Systems, 8(10), 4193–4200.

    Google Scholar 

  20. Song, G., & Tam, D. (2015). Two novel DV-HOP localization algorithms for randomly deployed wireless sensor networks. Abingdon: Taylor & Francis Inc.

    Book  Google Scholar 

  21. Guo, Z., Min, L., Li, H., & Wu, W. (2012). Improved DV-HOP Localization algorithm based on RSSI value and hop correction. Berlin: Springer.

    Google Scholar 

  22. Sangwoo, L., Dongyul, L., & Chaewoo, L. (2011). Enhanced DV-HOP algorithm with reduced hop-size error in ad hoc networks. IEICE Transactions on Communications, 94-B(7), 2130–2132.

    Google Scholar 

  23. Pandey, S., & Varma, S. (2016). A range based localization system in multihop wireless sensor networks: A distributed cooperative approach. Wireless Personal Communications, 86(2), 615–634.

    Article  Google Scholar 

  24. Shahzad, F., Shaltami, T., & Shakshukhi, E. (2017). DV-maxHop: A fast and accurate range-free localization algorithm for anisotropic wireless networks. IEEE Transactions on Mobile Computing, 16(9), 2494–2505.

    Article  Google Scholar 

  25. Hu, Y., & Li, X. (2013). An improvement of DV-HOP localization algorithm for wireless sensor networks. Telecommunication Systems, 53(1), 13–18.

    Article  Google Scholar 

  26. Chao, J., Han, G., Zhu, C., Guo, H., & Shu, L. (2013). Performance evaluation of DV-HOP localization algorithm with mobility models for mobile wireless sensor networks. In Wireless communications and mobile computing conference (vol. 8711, pp. 1827–1832). IEEE.

  27. Cui, Z., Sun, B., Wang, G., Xue, Y., & Chen, J. (2016). A novel oriented cuckoo search algorithm to improve DV-HOP performance for cyber–physical systems. Journal of Parallel & Distributed Computing, 103, 42–52.

    Article  Google Scholar 

  28. Chen, Z., Li, X., Yang, B., & Zhang, Q. (2015). A self-adaptive wireless sensor network coverage method for intrusion tolerance based on trust value. Journal of Sensors, 2015(4), 1–10.

    Google Scholar 

  29. Cheng, J., & Xia, L. (2016). An effective cuckoo search algorithm for node localization in wireless sensor network. Sensors, 16(9), 1390.

    Article  Google Scholar 

  30. Li, X., & Yin, M. (2015). Modified cuckoo search algorithm with self adaptive parameter method. Information Sciences, 298(C), 80–97.

    Article  Google Scholar 

  31. Walton, S., Hassan, O., Morgan, K., & Brown, M. R. (2011). Modified cuckoo search: A new gradient free optimisation algorithm. Chaos, Solitons & Fractals, 44(9), 710–718.

    Article  Google Scholar 

  32. Piechocki, J., Ambroziak, D., Palkowski, A., & Redlarski, G. (2014). Use of modified cuckoo search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms. Applied Energy, 114(114), 901–908.

    Article  Google Scholar 

Download references

Acknowledgements

This work was in part supported by the State Key Laboratory of Safety and Health for Metal Mines (No. 2016-JSKSSYS-04);the Key Scientific Projects of Hunan Education Committee (No. 15A161).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiuwu Yu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, X., Hu, M. Hop-Count Quantization Ranging and Hybrid Cuckoo Search Optimized for DV-HOP in WSNs. Wireless Pers Commun 108, 2031–2046 (2019). https://doi.org/10.1007/s11277-019-06507-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06507-7

Keywords

Navigation