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Aspect-Oriented Modeling of Spatial Data Interpolation for Estimating Missing Data in Internet of Things (IoT) Service Discovery
Published in ASTM International
2019
Volume: 47
   
Issue: 6
Abstract
The Internet of Things (IoT) is a model of future Internet and pervasive computing that has its own particular difficulties gained from the Internet as far as adaptability, vague topology, and so on are concerned. The proposed work means to determine the difficulties posed by IoT in the service discovery field. Most of the data analytics algorithms applied for data collected through sensors and actuators assume that the data are complete such that each property of the instances is filled with the appropriate value. These data have temporal and spatial correlation between them, and missing such data results in a significant decrease in accuracy and reliability of data analysis performed. Considering the importance of estimating the spatial data and the intricacies involved in estimating it using interpolation techniques, the proposed work bases its system development using an aspect-oriented programming improvement technique, thereby addressing the interpolation strategy as a cross-cutting aspect and reducing the complexity involved thereof. The proposal analyzes the situation of missing data and appropriately weaves the aspect and the application together, thereby decreasing the complexity in handling the interpolating data. The woven aspect estimates the missing data using an inverse distance weighting method and updates the information. Observation of the experimental results reveals significant improvement in response time compared with estimating the unknown value in a conventional manner. © 2019 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959
About the journal
JournalData powered by TypesetJournal of Testing and Evaluation
PublisherData powered by TypesetASTM International
ISSN0090-3973
Impact FactorNo
Open Access0
Citation Styleunsrt