A Study of Simple Partially-Recovered Sensor Data Imputation Methods

Sydora, Christoph; Jung, Johannes; Nikolaidis, Ioanis

doi:10.23919/cnsm46954.2019.9012748

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2020

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“…A description of wireless sensor denoted with continuous sensing and intermittent connectivity has been discussed using Markovian models. The storage capacity has been estimated in a per node basis [9]. The methodology fails during longer waiting duration in missing connectivity to sink.…”

Section: Related Workmentioning

confidence: 99%

Data Imputation in Wireless Sensor Networks using Regression Models

2020

IJATCSE

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Wireless sensor nodes and its inconsistency in reporting sensory data information tend to inaccurate processing at sink. Imputing information of sensors with collocated sensory information is needed to provide intermittent less processing at sink. This work deals with regression model in selecting the nearest sensor based upon nature of dependant variable. Poisson Regression based Imputed Data Information (PRIDI) has been used when there are equidispersion criteria of dependant variable is observed with collocated sensors. Negative Binomial Distribution model using Imputed Data Information (NBDIDI) has been used when over dispersion criteria of dependant variable is observed with collocated sensors. Thus both protocols estimate the imputed information considering the nature of collocated sensor data and type of reported interval information acquired at sink. Simulation comparison has been done with discrete event simulator for proposed protocols in network simulator 2.

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Section: Related Workmentioning

confidence: 99%

Data Imputation in Wireless Sensor Networks using Regression Models

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

A Study of Simple Partially-Recovered Sensor Data Imputation Methods

Cited by 1 publication

References 12 publications

Data Imputation in Wireless Sensor Networks using Regression Models

Data Imputation in Wireless Sensor Networks using Regression Models

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