Energy Efficient K-Means Clustering Technique for Underwater Wireless Sensor Network
Author(s) -
Sunpreet Kaur,
Vinay Bhardwaj
Publication year - 2016
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2016910606
Subject(s) - computer science , underwater , wireless sensor network , cluster analysis , energy (signal processing) , real time computing , data mining , computer network , artificial intelligence , statistics , geology , oceanography , mathematics
The communication range of underwater wireless sensor networks (UWSN) is limited by the underwater environment. Acoustic networks with huge number of sensors may have long communication range with appropriate protocols in literature. On the other hand, especially, the networks including small number of nodes have communication problems for long ranges. In energy constrained 3D underwater system environment it is essential to discover approaches to enhance the lifetime of the sensor nodes. Underwater sensors cannot utilize sunlight-based vitality to recharge the batteries. To challenge this problem, Multihop communication in underwater acoustic networks is a promising solution. In this study, a novel approach, Multihop Energy Efficient K-Means Clustering algorithm (MH-EKMC) is introduced and developed. The goal of this paper is to produce simulation results that would show the exhibitions of the proposed protocol for a given metric such as Network lifetime, No of dead nodes per round and total energy consumption. From the results, proposed protocol shows better performance for an energy-constrained network.
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