Cuckoo Search Optimization Based Mobile Node Deployment Scheme for Target Coverage Problem in Underwater Wireless Sensor Networks

  • Sangeeta KumariEmail author
  • Govind P. Gupta
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


In underwater wireless sensor networks (UWSNs), mobile node deployment for maximum target coverage is a challenging issue. To solve this issue, we have proposed cuckoo search optimization (CSO) based mobile node (MN) deployment scheme to obtain the optimal coverage ratio in the network. In this scheme, detection probability of MN is used to detect the target point. CSO-based mobile node deployment scheme is applied to find set of best location for the deployment of the MN to obtain maximum target coverage in the network. Performance of the proposed scheme is evaluated and compared with the existing fruit fly-based scheme by varying different parameters such as sensing range, and number of MN. Simulation results confirm the performance of the proposed scheme in terms of coverage ratio and convergence rate.


UWSNs Coverage issue Cuckoo search optimization Node deployment problem 


  1. 1.
    Felemban, E., Shaikh, F.K., Qureshi, U.M., Sheikh, A.A., Qaisar, S.B.: Underwater sensor network applications: a comprehensive survey. Int. J. Distrib. Sens. Netw. 11(11), 1–14 (2015)CrossRefGoogle Scholar
  2. 2.
    Lloret, J.: Underwater sensor nodes and networks. 13(9), 11782–11796 (2013)Google Scholar
  3. 3.
    Akyildiz, I.F., Pompili, D., Melodia, T.: Underwater acoustic sensor networks: research challenges. Ad Hoc Netw. 3(3), 257–279 (2005)CrossRefGoogle Scholar
  4. 4.
    Heidemann, J., Ye, W., Wills, J., Syed, A., Li, Y.: Research challenges and applications for underwater sensor networking. In: Wireless Communications and Networking Conference, vol. 1, pp. 228–235. IEEE (2006)Google Scholar
  5. 5.
    Al-Karaki, J.N., Gawanmeh, A.: The optimal deployment, coverage, and connectivity problems in wireless sensor networks: revisited. IEEE Access 5, 18051–18065 (2017)CrossRefGoogle Scholar
  6. 6.
    Senel, F.: Coverage-aware connectivity constrained unattended sensor deployment in underwater acoustic sensor networks. Wirel. Commun. Mob. Comput. 16(14), 2052–2064 (2016)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Wang, B., Xiong, Z.: A novel coverage algorithm based on 3D-Voronoi cell for underwater wireless sensor networks. In: Wireless Communications & Signal Processing, pp. 1–5. IEEE (2015)Google Scholar
  8. 8.
    Senel, F., Akkaya, K., Erol-Kantarci, M., Yilmaz, T.: Self-deployment of mobile underwater acoustic sensor networks for maximized coverage and guaranteed connectivity. Ad Hoc Netw. 34, 170–183 (2015)CrossRefGoogle Scholar
  9. 9.
    Liu, L.: A deployment algorithm for underwater sensor networks in ocean environment. J. Circuits Syst. Comput. 20(6), 1051–1066 (2011)CrossRefGoogle Scholar
  10. 10.
    Luo, X., Feng, L., Yan, J., Guan, X.: Dynamic coverage with wireless sensor and actor networks in underwater environment. IEEE/CAA J. Autom. Sin. 2(3), 274–281 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Xiaoyu, D., Lijuan, S., Linfeng, L.: Coverage optimization algorithm based on sampling for 3D underwater sensor networks. Int. J. Distrib. Sens. Netw. 9(9), 286–291 (2013)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Wang, B.: A novel node sinking algorithm for 3D coverage and connectivity in underwater sensor networks. Ad Hoc Netw. 56, 43–55 (2017)CrossRefGoogle Scholar
  13. 13.
    Gupta, G.P., Jha, S.: Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks. Wirel. Netw. 1–11 (2018)Google Scholar
  14. 14.
    Fang, W., Song, X., Xiaojun, W., Sun, J., Mengqi, H.: Novel efficient deployment schemes for sensor coverage in mobile wireless sensor networks. Inf. Fusion 41, 25–36 (2018)CrossRefGoogle Scholar
  15. 15.
    Li, H.P., Du, Q.W.: Energy efficient coverage control algorithm for wireless sensor networks. J. Chin. Comput. Syst. 32(2), 233–236 (2011)Google Scholar
  16. 16.
    Zhang, Y., Wang, M., Liang, J., Zhang, H., Chen, W., Jiang, S.: Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm. Soft Comput. 21(20), 6019–6029 (2017)CrossRefGoogle Scholar
  17. 17.
    Elhoseny, M., Tharwat, A., Yuan, X., Hassanien, A.E.: Optimizing K-coverage of mobile WSNs. Expert Syst. Appl. 92, 142–153 (2018)CrossRefGoogle Scholar
  18. 18.
    Bharamagoudra, M.R., Manvi, S.K.S.: Deployment scheme for enhancing coverage and connectivity in underwater acoustic sensor networks. Wirel. Pers. Commun. 89(4), 1265–1293 (2016)CrossRefGoogle Scholar
  19. 19.
    Chen, J.-F., Hsieh, H.-N., Do, Q.H.: Predicting student academic performance: a comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks. Algorithms 7(4), 538–553 (2014)CrossRefGoogle Scholar
  20. 20.
    Mahmoudi, S., Lotfi, S.: Modified cuckoo optimization algorithm (MCOA) to solve graph coloring problem. Appl. Soft Comput. 33, 48–64 (2015)CrossRefGoogle Scholar
  21. 21.
    Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Nature & Biologically Inspired Computing, pp. 210–214. IEEE (2009)Google Scholar
  22. 22.
    Lv, X., Li, H., Li, H.: A node coverage algorithm for a wireless-sensor-network-based water resources monitoring system. Clust. Comput. 20(4), 3061–3070 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyNational Institute of TechnologyRaipurIndia

Personalised recommendations