Journal Published Online: 01 April 2019
Volume 47, Issue 6

Aspect-Oriented Modeling of Spatial Data Interpolation for Estimating Missing Data in Internet of Things (IoT) Service Discovery

CODEN: JTEVAB

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.

Author Information

Balakrishnan, Senthil Murugan
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Pages: 13
Price: $25.00
Related
Reprints and Permissions
Reprints and copyright permissions can be requested through the
Copyright Clearance Center
Details
Stock #: JTE20180508
ISSN: 0090-3973
DOI: 10.1520/JTE20180508