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Erschienen in: The Journal of Supercomputing 6/2020

11.04.2018

Triangular fuzzy-based spectral clustering for energy-efficient routing in wireless sensor network

verfasst von: U. Nilabar Nisha, A. Mahabub Basha

Erschienen in: The Journal of Supercomputing | Ausgabe 6/2020

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Abstract

In this study, a triangular fuzzy-based spectral cluster routing (TF-SCR) mechanism is proposed in order to improve network lifetime and reliability of data packet transmission in WSN with minimum routing overhead. The designed TF-SCR mechanism contains three steps, namely preprocessing, clustering and post-processing. TF-SCR mechanism initially carried out preprocessing where the residual energy and received signal strength of all sensor nodes in WSN is determined. After preprocessing, TF-SCR mechanism performs clustering process with the application of spectral clustering in which the sensor nodes in WSN are grouped based on residual energy level. After completing clustering process, TF-SCR mechanism implements post-processing step where triangular fuzzy membership function is applied to select the sensor node with higher residual energy and signal strength as cluster head for routing data packets to sink nodes in WSN. This helps for TF-SCR mechanism to obtain energy-efficient routing in WSN. Thus, TF-SCR mechanism enhances the network lifetime and reliability of data broadcasting in WSN as compared to state-of-the-art works. The simulation of TF-SCR mechanism is conducted on factors such as energy consumption, network lifetime, routing overhead and reliability with respect to a number of sensor nodes and data packets. The simulation results demonstrate that the TF-SCR mechanism is able to improve the network lifetime and also minimize the overhead of routing with minimal energy consumption as compared to state-of-the-art works.
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Metadaten
Titel
Triangular fuzzy-based spectral clustering for energy-efficient routing in wireless sensor network
verfasst von
U. Nilabar Nisha
A. Mahabub Basha
Publikationsdatum
11.04.2018
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 6/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-018-2357-y

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