• Register
  • Login

European Journal of Molecular & Clinical Medicine

  1. Home
  2. Adaptive Discriminant Quadratic Boosting Classification Based Radix Hash Data Storage for Context Aware Cloud IoT Services

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Adaptive Discriminant Quadratic Boosting Classification Based Radix Hash Data Storage for Context Aware Cloud IoT Services

    Authors

    • S. Sivakamasundari 1
    • Dr.K. Dharmarajan 2

    1 Assistant Professor New Prince ShriBhavani Arts and Science College

    2 Associate Professor,Dept.of Information Technology,VISTAS

,

Document Type : Research Article

  • Article Information
  • Download
  • Export Citation
  • Statistics
  • Share

Abstract

Internet-of-Things (IoT) promises to give services to the users through connecting physical things using Internet. The conventional context aware system collects the data from users and stores it in cloud server. But, accuracy of classifying collected data using existing method was poor to store the user data with lower space complexity and to respond the user needed services with minimal time. In order to solve the above drawbacks, an Adaptive Discriminant Quadratic Boosting Classification based Radix Hash Cloud Data Storage (ADQBC-RHCDS) Model is proposed. The ADQBC-RHCDS Model is designed for providing the context aware IoT services to the cloud users with minimal response time and space complexity. In ADQBC-RHCDS model, Internet-of-Things (IoT) afford the services to the end cloud users by connecting an entity (i.e., person, place, or object) with sensors through Internet. Context Aware IoT helps to monitor and gather the information from users. After collecting the information, it is forwarded to the cloud server. Followed by, the cloud server classifies the collected information by designing Adaptive Discriminant Quadratic Boosting Ensemble Classifier (ADQBEC). After that, the classified data gets stored in the Radix Hash Tree Based Secured Cloud Data Storage (RHT-SCDS) for easy data access. Radix Hash Tree is a search tree used to store a set of data. Whenever the cloud user needs to access the data (i.e. insert or delete data), user sends the request to the cloud server. Then, cloud server provides the required services to the cloud user with minimal response time. Experimental evaluation of ADQBC-RHCDS model is carried out on factors such as classification accuracy, space complexity, and response time. The experimental result shows that the ADQBC-RHCDS model is able to reduce the space complexity and response time of context aware IoT services to the cloud users when compared to state-of-the-art works.

Keywords

  • Adaptive Discriminant Analysis
  • Cloud Users
  • Hash Value
  • Internet-of-Things
  • Quadratic Boosting
  • Radix Hash Tree
  • Strong Classifier
  • XML
  • PDF 661.14 K
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
    • Article View: 220
    • PDF Download: 182
European Journal of Molecular & Clinical Medicine
Volume 7, Issue 11
December 2020
Page 7808-7825
Files
  • XML
  • PDF 661.14 K
Share
Export Citation
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
Statistics
  • Article View: 220
  • PDF Download: 182

APA

Sivakamasundari, S., & Dharmarajan, D. (2021). Adaptive Discriminant Quadratic Boosting Classification Based Radix Hash Data Storage for Context Aware Cloud IoT Services. European Journal of Molecular & Clinical Medicine, 7(11), 7808-7825.

MLA

S. Sivakamasundari; Dr.K. Dharmarajan. "Adaptive Discriminant Quadratic Boosting Classification Based Radix Hash Data Storage for Context Aware Cloud IoT Services". European Journal of Molecular & Clinical Medicine, 7, 11, 2021, 7808-7825.

HARVARD

Sivakamasundari, S., Dharmarajan, D. (2021). 'Adaptive Discriminant Quadratic Boosting Classification Based Radix Hash Data Storage for Context Aware Cloud IoT Services', European Journal of Molecular & Clinical Medicine, 7(11), pp. 7808-7825.

VANCOUVER

Sivakamasundari, S., Dharmarajan, D. Adaptive Discriminant Quadratic Boosting Classification Based Radix Hash Data Storage for Context Aware Cloud IoT Services. European Journal of Molecular & Clinical Medicine, 2021; 7(11): 7808-7825.

  • Home
  • About Journal
  • Editorial Board
  • Submit Manuscript
  • Contact Us
  • Glossary
  • Sitemap

News

 

For Special Issue Proposal : editor.ejmcm21@gmail.com

Newsletter Subscription

Subscribe to the journal newsletter and receive the latest news and updates

© Journal Management System. Powered by ejournalplus