GDL

Journal from gdlhub / 2019-06-24 10:13:51
Oleh : Vani Krishnaswamy, Sunil Kumar S. Manvi, Telkomnika
Dibuat : 2019-06-24, dengan 1 file

Keyword : belongingness, clustering, euclidean distance, SSE, UWASN
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/11379
Sumber pengambilan dokumen : WEB

Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in researchers due to its wide area of applications. To extract the data from underwater and transmit to watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of clustering and data aggregation schemes are to decrease the consumption of energy and prolong the lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm. The simulation results indicate that the proposed scheme performs better in maximizing network lifetime and minimizing energy consumption.

Deskripsi Alternatif :

Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in researchers due to its wide area of applications. To extract the data from underwater and transmit to watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of clustering and data aggregation schemes are to decrease the consumption of energy and prolong the lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm. The simulation results indicate that the proposed scheme performs better in maximizing network lifetime and minimizing energy consumption.

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