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Branching based Underwater Clustering Protocol


  • Department of IT, Faculty of Computing, Sathyabama University, Chennai - 600119, Tamil Nadu, India
  • Department of ECE, St. Joseph’s College of Engineering, Chennai - 600119, Tamil Nadu, India
  • Center for Remote Sensing and Geo Informatics, Sathyabama University, Chennai - 600119, Tamil Nadu, India


Background/Objectives: Underwater wireless sensor network has widely influenced the scientist to explore the data and to study the underwater environment deep inside the ocean. Methods: The new clustering technique which is effective in prolonging the lifetime of the underwater nodes. An AODV (Ad-Hoc on-Demand Vector) routing protocol has been used in order to find the shortest path between nodes. SNR (Signal-to-Noise Ratio) based dynamic clustering mechanism partition the nodes into various clusters and select the Cluster Head (CH) among the nodes based on energy whereas other nodes join with a specific CH based on the SNR values the clustering technique is effective in prolonging the lifetime of the UWSN (Underwater Sensor Network). Findings: Due to the mobility nature of underwater sensors, ocean currents and unique characteristics of acoustic signals such as long propagation delay, low bit error rate, low bandwidth make the transmission period longer. Hence more energy is consumed by the sensor nodes while transmission. Our proposed system introduces signal to noise ratio based clustering mechanism which improves the energy consumption of network and minimizes the transmission delay. Applications/Improvements: The simulation result verifies the effectiveness and feasibility of the proposed technique and also shows increased rate in PDR (Packet Delivery Ratio) and less energy consumption.


Ad-Hoc on-Demand Vector Routing, Cluster Head Component, Signal to Noise Ratio, Underwater Sensor Network.

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  • Caiti A, Grythe K, Hovem JM, Jesus SM, Lie A, Munafo A, Reinen TA, Silva A, Zabel F. Linking acoustic communications and network performance: integration and experimentation of an underwater acoustic network. IEEE Journal of Oceanic Engineering. 2013 Oct; 38(4):758–71.
  • Naik SS, Nene MJ. Self organizing localization algorithm for large scale underwater sensor network. Proceedings of RACSS; 2012 Apr. p. 207–13.
  • Harb H, Makhoul A, Couturier R. An enhanced K-means and ANOVA-based clustering approach for similarity aggregation in underwater wireless sensor network. IEEE Sensors Journal. 2015 Oct; 15(10):5483–93.
  • Sasikumar R, AnanthanarayananV , Rajeswari A. An intelligent pico cell range expansion technique for heterogeneous wireless networks. Indian Journal of Science and Technology. 2016 Mar; 9(9):1–9.
  • Gomathi RM, Manickam MLJ. A comparative study on routing strategies for underwater acoustic wireless sensor network. Contemporary Engineering Sciences. 2016 Jan; 9(2):71–80.
  • Khan G, Gola KK, Ali W. Energy efficient routing algorithm for UWSN – A clustering approach. Proceedings of 2nd ICACCE; 2015 May. p. 150–5.
  • Yu H, Martin P, Hassanien H. Cluster-based replication for large-scale mobile ad-hoc networks. Proceedings of International Conference on Wireless Networks Communications and Mobile computing; 2005 Jun. p. 552– 7.
  • Gomathi RM, Manickam ML, Madhukumar JT. Energy preserved mobicast routing prorocol with static node for underwater acoustic sensor network. IEEE International Conference on Innovation Information in Computing Technologies (ICIICT); 2015 Feb. p. 1–8.


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