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Fast and Efficient Multiview Access Control Mechanism for Cloud Based Agriculture Storage Management System

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  • Kuldeep Sambrekar

    (Gogte Institute of Technology, Belgaum, India)

  • Vijay S. Rajpurohit

    (Gogte Institute of Technology, Belgaum, India)

Abstract

Agriculture and its related industries are the backbone of many countries' economic growth. To achieve an efficient agricultural management system, remote sensing forecasting and GIS technology are providing information to users/stakeholders of various agricultural application uses. This information is huge in size and is stored in the cloud computing storage environment. Minimizing data access and storage costs on such an environment is desired. For achieving fine-grained role-based access control mechanisms, researchers are now focusing on ensuring such roles are enforced correctly. Existing models, though they are using role-based access control at various levels, are facing challenges like high computation rates and storage overhead. Currently, existing systems are using XML and UML for role and user creation. To address these research challenges, this article presents a model Fast and Efficient Multi View Access Control (FEMVAC) using the Amazon S3 public cloud environment for agriculture. The model minimizes storage overhead by adopting a banarization method over UML/XML method. The experimental outcome shows that the FEMVAC method is efficient compared with existing models.

Suggested Citation

  • Kuldeep Sambrekar & Vijay S. Rajpurohit, 2019. "Fast and Efficient Multiview Access Control Mechanism for Cloud Based Agriculture Storage Management System," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 9(1), pages 33-49, January.
    • Handle: RePEc:igg:jcac00:v:9:y:2019:i:1:p:33-49
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    Cited by:

    1. Yu, Hongxin & Zhao, Yuanjun & Liu, Zheng & Liu, Wei & Zhang, Shuai & Wang, Fatao & Shi, Lihua, 2021. "Research on the financing income of supply chains based on an E-commerce platform," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Hinge, Gilbert & Surampalli, Rao Y. & Goyal, Manish Kumar & Gupta, Brij B. & Chang, Xiaojun, 2021. "Soil carbon and its associate resilience using big data analytics: For food Security and environmental management," Technological Forecasting and Social Change, Elsevier, vol. 169(C).

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