CSCRT Protocol with Energy Efficient Secured CH Clustering for Smart Dust Network Using Quantum Key Distribution

Rajesh, D.; Rajanna, Gowda Shankarappa

doi:10.18280/ijsse.120404

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…ERSCH2 progress enhanced efficiency accomplished by ERSCH1 by modified amount of CH. To conclude an identical power representation as in the study of Rajesh and Rajanna [22] is utilized.…”

Section: Remote Secured Cluster Headsmentioning

confidence: 99%

Enhancing Lifespan and Energy Efficiency in Mobile Smart Dust Networks

Dennison,

Dasebenezer

et al. 2023

ISI

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Mobile Smart Dust Networks play a crucial role in the Internet of Things (IoT) ecosystem, with their nodes contributing significantly to various tasks. However, these nodes primarily deplete energy, raising concerns regarding the overall lifespan of the system. The primary objective of this research is to reduce the energy consumption of these nodes, thereby extending the lifespan of the entire Smart Dust Network. This study proposes a novel clustering approach specifically designed for Smart Dust Networks aimed at reducing network energy consumption, which, in turn, significantly extends the network lifespan. The methodology primarily focuses on the allocation of Cluster Heads among the mobile nodes within the network. Simulations conducted in Network Simulator 2 (NS2) demonstrate a significant reduction in network energy consumption, consequently extending the network lifespan.

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“…ERSCH2 progress enhanced efficiency accomplished by ERSCH1 by modified amount of CH. To conclude an identical power representation as in the study of Rajesh and Rajanna [22] is utilized.…”

Section: Remote Secured Cluster Headsmentioning

confidence: 99%

Enhancing Lifespan and Energy Efficiency in Mobile Smart Dust Networks

Dennison,

Dasebenezer

et al. 2023

ISI

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“…approaches to efficient qualitative scanning for the diagnosis and treatment of ophthalmology. Numerous detection methods and their improvements, such as metastatic disease magneto retinography and computed tomography, are popular methods used to analyse anatomical and functional malformations in the retina in order to both perceive deviation and measure the disease's evolution rationally [12,13]. Computerized clinical decision support systems (CDSSs) for ophthalmic, including CASNET/glaucoma, are made to make efficient prediction models for identifying illness pathophysiology in people's eyes [14].…”

Section: Related Workmentioning

confidence: 99%

Identification and Categorization of Microaneurysms in Optic Images by Applying DTCWT and Log Gabor Characteristics

Sujitha¹,

Subhajini²

2022

RIA

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Ophthalmology is known as the "virtually silent mobster of vision." Ophthalmology is the leading cause of sight problems globally, aside from Diabetic Retinopathy. Intense pressure within the retina causes damage to the retinal image and, as a consequence, modest but undeniable vision problems. Ophthalmology is frequently obscured in its sufferers expecting final phase because the revival of the deteriorated nervous system fibers isn't suited healing properties. In 2010, it was estimated that approximately 60.5 million people over the age of 40 had cataracts. By 2020, this amount may have risen to 80 million. Recent advent of advanced imaging have resulted in excellent qualitative imaging solutions for the detection and monitoring of ophthalmology. Exterior brightness can be used to effectively complete ophthalmology orders. The fourier channels used in this research are daubechies and symlet3, which would improve the accuracy and performance of cataractous image categorization. A conventional 2-D Discrete Wavelet Transform (DWT), which is used to automatically extract and assess variations, is used to evaluate those channels. The extracted characteristics are fed into a convolutional machine classification, which distinguishes between physiological and pathological ophthalmology pictures.

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