Priya Dharsini,T. Jemima Jebaseeli ,D. Jasmine David,




Sensor,cuckoo,search, underwater,network, node,


In the underwater sensor network, the accurate position of every sensor node is of prime importance and the procedure of finding the node coordinates is known as localization. Localization plays a vital role in the designing and functioning of any Underwater Sensor Network(UWSN).Cheng et al(III) prove effective localization algorithm has a greater influence on the performance of the network.Recent research exists in the field of exploring meta-heuristic based localizationalgorithms for effective sensor node localization by Kulkarniet al. (XI), and Kumaret al.(XII). The research contributions of  Li& Wang (XIII), Goyal S Patterh& MS (VII) have proved that the cuckoo search(CS) algorithm is comparatively effectivebecause of its distinctiveness of few parameters thus dropping the computational complication and communication overhead.CS has also proved to have better proficient


I. Adnan A and Razzaque MA, “A comparative study of Particle Swarm Optimization and Cuckoo Search techniques through problem-specific distance function”, Proceedings of Information and Communication Technology (ICoICT), vol. 160(1), pp. 83-92, 2013.
II. Arora S and Singh S,“A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search”, Proceedings of International conference on Control Computing Communication and Materials (ICCCCM), pp. 1-4, 2013.
III. Cheng J and Xia L, “An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network”,Sensors, Vol.16(9), pp.1390-1407, 2016.
IV. Cheng W, Teymorian AY, Ma L, Cheng X, Lu X, and Lu Z, “Underwater Localization in Sparse 3D Acoustic Sensor Networks”, Proceedings of 27th IEEE Conference on Computer Communications, pp. 236-240, 2008.
V. Doherty L,Pister K, and El Ghaoui L, “Convex Position Estimation in Wireless Sensor Networks”, Proceedings of the INFOCOM 2001- Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, Helsinki, Finland, Volume 3, pp. 1655–1663, 2001.
VI. Gao J, Shen X, Zhao R ,Mei H, and Wang H, “A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks” , Sensors, Vol.17(5), pp.984-1001, 2017.
VII. Goyal S and Patterh MS, “Wireless sensor network localization based on cuckoo search algorithm”, Journal of Wireless Personal Communication, vol. 79, pp. 223-234, 2014.
VIII. Han G, Jiang J, Shu L, Xu Y, and Wang F, “Localization Algorithms of Underwater Wireless Sensor Networks: A Survey”, Journal of Sensors, pp. 2026-2061, 2012.
IX. Han G, Zhang C, Shu L, and Rodrigues JJPC, “Impacts of Deployment Strategies on Localization Performance in Underwater Acoustic Sensor Networks”, IEEE Transactions on Industrial Electronics, vol. 62(3), pp. 1725-1733, 2015.
X. Harikrishnan R, Kumar VJS, and Ponmalar PS, “Firefly algorithm approach for localization in wireless sensor networks”, Proceedings of 3rd International Conference on Advanced Computing, pp. 209-214, 2016.
XI. Kulkarni RV, Venayagamoorthy GK, and Cheng MX, “Bioinspired node localization in wireless sensor networks”, Proceedings of International Conference on Systems, Man and Cybernetics, IEEE, pp. 205-210, 2009.
XII. Kumar A, Khosla A, Saini JS, and Singh S, “Meta-heuristic range based node localization algorithm for Wireless Sensor Networks”, In Proceedings of the IEEE International Conference on Localization and GNSS, pp. 1-7, 2012.
XIII. Li SP and Wang XH, “The research on Wireless Sensor Network node positioning based on DV-hop algorithm and cuckoo searching algorithm”, Proceedings of the IEEE International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), pp. 620-623, 2013.
XIV. Priyadharsini Cand Kannimuthu S, “Polyhedron Model for Three Dimensional Node Deployment in Underwater Sensor Network”, Journal of Computational and Theoretical Nanoscience, vol. 14(12), pp. 5858-5862, 2017.
XV. Solihin MI and Zanil MF, “Performance comparison of cuckoo search and differential evolution algorithm for constrained optimization”, International Engineering Research and Innovation Symposium (IRIS), pp. 1-8, 2016.
XVI. Yang XS and Deb S, “Cuckoo Search via Levy Flights”, Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp. 210-214, 2009.

View Download