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A Framework for Analysis of Incompleteness and Security Challenges in IoT Big Data

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  • Kimmi Kumari

    (M. S. Ramaiah Institute of Technology, India)

  • Mrunalini M.

    (M. S. Ramaiah Institute of Technology, India)

Abstract

Data quality (DQ) is gaining traction as a new area to focus on for increasing organisational effectiveness. Despite the fact that the implications of poor data quality are often felt in the day-to-day operations of businesses, only a small percentage of companies use particular approaches for measuring and monitoring data quality. In this paper, the focus is on the efficiency and incompleteness of IOT big data and since security is the major concern in large clusters, map reduce technique is proposed in order to overcome the issues and challenges faced on regular basis while dealing with huge volume of information. Dealing with veracity is need of an hour and therefore, the work in this paper can be categorised into analysis, observation, proposing model and testing its accuracy and performance.

Suggested Citation

  • Kimmi Kumari & Mrunalini M., 2022. "A Framework for Analysis of Incompleteness and Security Challenges in IoT Big Data," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 16(2), pages 1-13, April.
    • Handle: RePEc:igg:jisp00:v:16:y:2022:i:2:p:1-13
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISP.308305
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    Cited by:

    1. Aristeidis Karras & Anastasios Giannaros & Christos Karras & Leonidas Theodorakopoulos & Constantinos S. Mammassis & George A. Krimpas & Spyros Sioutas, 2024. "TinyML Algorithms for Big Data Management in Large-Scale IoT Systems," Future Internet, MDPI, vol. 16(2), pages 1-29, January.

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