Skip to main content
SpringerLink
Log in

M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Location information of a sensor node is the primary concern to process the sensed data in Wireless Sensor Networks (WSNs). The location of the sensor node is used in other domains of sensor network like message routing, node tracking, load balancing. For statically deployed sensor nodes, mobile anchor based localization is an efficient solution. The main challenge in mobile anchor based localization is designing an optimum path for the mobile anchor node considering the coverage, path length and localizability of sensor nodes as the key features. In this paper, we propose a novel path planning approach for mobile anchor based localization called “M-Curves”. Our proposed model promises that all the nodes in the network will receive at least three non-collinear beacon messages for localization. Our proposed trajectory assures full coverage, high localization accuracy as compared to other static models. Also, we optimize the localization process by using Dolphin Swarm Algorithm(DSA). The fitness function used for optimization in DSA, minimizes the localization error of the node in the network.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Li, X., Mitton, N., Simplot-Ryl, I., & Simplot-Ryl, D. (2012). Dynamic beacon mobility scheduling for sensor localization. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1439–1452.

    Article  Google Scholar 

  3. Moradi, M., Rezazadeh, J., & Ismail, A. S. (2012). A reverse localization scheme for underwater acoustic sensor networks. Sensors, 12(4), 4352–4380.

    Article  Google Scholar 

  4. Halder, S., & Ghosal, A. (2016). A survey on mobility-assisted localization techniques in wireless sensor networks. Journal of Network and Computer Applications, 60, 82–94.

    Article  Google Scholar 

  5. Chelouah, L., Semchedine, F., & Bouallouche-Medjkoune, L. (2018). Localization protocols for mobile wireless sensor networks: A survey. Computers & Electrical Engineering, 71, 733–751.

    Article  Google Scholar 

  6. Sichitiu, M. L., Ramadurai, V., et al. (2004). Localization of wireless sensor networks with a mobile beacon. MASS, 4, 174–183.

    Google Scholar 

  7. Ssu, K.-F., Ou, C.-H., & Jiau, H. C. (2005). Localization with mobile anchor points in wireless sensor networks. IEEE Transactions on Vehicular Technology, 54(3), 1187–1197.

    Article  Google Scholar 

  8. Lee, S., Kim, E., Kim, C., & Kim, K. (2009). Localization with a mobile beacon based on geometric constraints in wireless sensor networks. IEEE Transactions on Wireless Communications, 8(12), 5801–5805.

    Article  Google Scholar 

  9. Savvides, A., Han, C.-C., & Strivastava, M. B. (2001) . Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th annual international conference on Mobile computing and networking (pp. 166–179). ACM.

  10. Bulusu, N., Heidemann, J., & Estrin, D. (2000). Gps-less low-cost outdoor localization for very small devices. IEEE Personal Communications, 7(5), 28–34.

    Article  Google Scholar 

  11. Koutsonikolas, D., Das, S. M., & Hu, Y. C. (2007). Path planning of mobile landmarks for localization in wireless sensor networks. Computer Communications, 30(13), 2577–2592.

    Article  Google Scholar 

  12. Rezazadeh, J., Moradi, M., Ismail, A. S., & Dutkiewicz, E. (2014). Superior path planning mechanism for mobile beacon-assisted localization in wireless sensor networks. IEEE Sensors Journal, 14(9), 3052–3064.

    Article  Google Scholar 

  13. Jiang, J., Han, G., Xu, H., Shu, L., & Guizani, M. (2011). Lmat: Localization with a mobile anchor node based on trilateration in wireless sensor networks. In Global telecommunications conference (GLOBECOM, 2011 IEEE) (pp. 1–6). IEEE.

  14. Alomari, A., Comeau, F., Phillips, W., & Aslam, N. (2018). New path planning model for mobile anchor-assisted localization in wireless sensor networks. Wireless Networks, 24(7), 2589–2607.

    Article  Google Scholar 

  15. Nazir, U., Shahid, N., Arshad, M., & Raza, S. H. (2012). Classification of localization algorithms for wireless sensor network: A survey. In International conference on open source systems and technologies (ICOSST) (pp. 1–5). IEEE

  16. Blumenthal, J., Grossmann, R., Golatowski, F., & Timmermann, D. (2007). Weighted centroid localization in zigbee-based sensor networks. In IEEE international Symposium on intelligent signal processing, 2007. WISP (pp. 1–6). IEEE.

  17. Dong, Q., & Xu, X. (2014). A novel weighted centroid localization algorithm based on RSSI for an outdoor environment. Journal of Communications, 9(3), 279–285.

    Article  Google Scholar 

  18. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-free localization schemes for large scale sensor networks. In Proceedings of the 9th annual international conference on mobile computing and networking (pp. 81–95). ACM.

  19. Luo, R. C., Chen, O., & Pan, S. H. (2005). Mobile user localization in wireless sensor network using grey prediction method. In 31st annual conference of IEEE industrial electronics society, 2005. IECON 2005 (pp. 6–12). IEEE.

  20. Sheu, J.-P., Hu, W.-K., & Lin, J.-C. (2010). Distributed localization scheme for mobile sensor networks. IEEE Transactions on Mobile Computing, 9(4), 516–526.

    Article  Google Scholar 

  21. Neuwinger, B., Witkowski, U., Ruckert, U. (2009). Ad-hoc communication and localization system for mobile robots. In FIRA RoboWorld Congress (pp. 220–229). Springer.

  22. Wang, W., & Zhu, Q. (2009). Sequential monte carlo localization in mobile sensor networks. Wireless Networks, 15(4), 481–495.

    Article  Google Scholar 

  23. Wu, T.-Q., Yao, M., & Yang, J.-H. (2016). Dolphin swarm algorithm. Frontiers of Information Technology & Electronic Engineering, 17(8), 717–729.

    Article  Google Scholar 

  24. Gopakumar, A., & Jacob, L. (2008). Localization in wireless sensor networks using particle swarm optimization. In IET conference on wireless, mobile and multimedia networks.

  25. Kulkarni, R. V., & Venayagamoorthy, G. K. (2011). Particle swarm optimization in wireless sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics Part C (Applications and Reviews), 41(2), 262–267.

    Article  Google Scholar 

  26. Kulkarni, R. V., Venayagamoorthy, G. K., & Cheng, M. X. (2009). Bio-inspired node localization in wireless sensor networks. In IEEE international conference on systems, man and cybernetics, 2009. SMC 2009 (pp. 205–210). IEEE.

  27. Yang, Z., & Liu, Y. (2010). Quality of trilateration: Confidence-based iterative localization. IEEE Transactions on Parallel and Distributed Systems, 21(5), 631–640.

    Article  Google Scholar 

  28. Okdem, S. (2017). A real-time noise resilient data link layer mechanism for unslotted IEEE 802.15. 4 networks. International Journal of Communication Systems, 30(3), e2955.

    Article  Google Scholar 

  29. Perez-Solano, J. J., Claver, J. M., & Ezpeleta, S. (2017). Optimizing the mac protocol in localization systems based on IEEE 802.15. 4 networks. Sensors, 17(7), 1582.

    Article  Google Scholar 

  30. Rengasamy, M., Dutkiewicz, E., & Hedley, M. (2007). Mac design and analysis for wireless sensor networks with co-operative localisation. In International Symposium on communications and information technologies, ISCIT’07 (pp. 942–947). IEEE.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Goa, Ponda, Goa, 403401, India

    K. Kannadasan, Damodar Reddy Edla, Mahesh Chowdary Kongara & Venkatanareshbabu Kuppili

Authors
  1. K. Kannadasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Damodar Reddy Edla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahesh Chowdary Kongara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Venkatanareshbabu Kuppili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damodar Reddy Edla.

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

Kannadasan, K., Edla, D.R., Kongara, M.C. et al. M-Curves path planning model for mobile anchor node and localization of sensor nodes using Dolphin Swarm Algorithm. Wireless Netw 26, 2769–2783 (2020). https://doi.org/10.1007/s11276-019-02032-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-019-02032-4

Keywords

Search

Navigation