Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks

Spoorthi, K.; Saha, Snehanshu; Mathur, Archana

doi:10.1515/cait-2016-0039

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In node

i

, the average amount of energy utilized for every bit transmitted is: The average quantity of data generated by a probability source is referred to as entropy. The image entropy is determined using Equation () if

X

is a random variable for pixel intensities with levels of intensity as

X = ({},, x 1, x 2 \dots italicxL)

H ((), x) goodbreak= goodbreak- \sum_{i = 0}^{n} true(p ((), x) \log ((), p ((), x_{i})),

where

p ((), x_{i})

normalized frequency of

x_{i}

and the number of levels of intensity is represented as

L

.…”

Section: Proposed Methodology For Redundant Node Identificationmentioning

confidence: 99%

Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

Rajkumar,

Paramasivan

2023

Int J Communication

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SummaryMany monitoring applications related to surveillance, tracking and multipurpose visual monitoring have taken into consideration the use of wireless visual sensor networks. When sensors are deployed over a monitored field that could potentially damage the monitoring capability and availability in the visual sensor network. In order to overcome these issues, faulty nodes are identified and replaced by redundant node based on Siamese network in this research. Initially, camera nodes are randomly deployed in the visual sensor network, and the data are received from the network through gateway. To identify the redundant nodes, initially, the frames are divided into equivalent time slot, and then, Siamese network is utilized to identify the redundant nodes in a network. Siamese neural network is type of convolutional neural network that is utilized to recognize the similar images in the network. After that, faulty nodes are identified based on some parameters such as entropy, energy, transmission delay and network coverage. If the average energy, entropy, transmission delay and network coverage are below the threshold value, then the node is identified as faulty node. Finally, replace the faulty node with redundant node to enhance the availability in the visual sensor network for critical monitoring applications. The simulation analysis shows that the developed approach takes 772 s to identify redundant node and take 27 s to identify fake nodes and the developed method is executed at 1308 s. Thus, this prediction model helps to improve the coverage quality of target‐based monitoring in order to achieve availability.

show abstract

“…In node

i

X

is a random variable for pixel intensities with levels of intensity as

X = ({},, x 1, x 2 \dots italicxL)

H ((), x) goodbreak= goodbreak- \sum_{i = 0}^{n} true(p ((), x) \log ((), p ((), x_{i})),

where

p ((), x_{i})

normalized frequency of

x_{i}

and the number of levels of intensity is represented as

L

.…”

Section: Proposed Methodology For Redundant Node Identificationmentioning

confidence: 99%

Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

Rajkumar,

Paramasivan

2023

Int J Communication

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show abstract

“…WSN has gained popularity in recent years [9]. It has major characteristics over wired networks such as remote sensing, low cost, rapid deployment, and self‐organisation [10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks

Gabr¹,

Ahmed²,

Salim³

2020

IET wirel. sens. syst.

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Cluster Head Recovery Algorithm for Wireless Sensor Networks

Titouna

Moumen

Ari

2019

2019 6th International Conference on Control, Decision and Information Technologies (CoDIT)

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Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks

Abstract: Exertion of wireless sensor networks has been increasing in recent years

Cited by 4 publications

References 14 publications

Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks

Cluster Head Recovery Algorithm for Wireless Sensor Networks

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