Open Access

ARTICLE

Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Souvik Pal^1,2,*, Hannah VijayKumar³, D. Akila⁴, N. Z. Jhanjhi^5,6, Omar A. Darwish⁷, Fathi Amsaad⁸

1 Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India
2 Post Doctoral Researcher, Sambalpur University, Sambalpur, India
3 Department of Computer Science, Anna Adarsh College for Women, Chennai, India
4 Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, India
5 School of Computer Science and Engineering, SCE, Taylor’s University, Subang Jaya, 47500, Selangor, Malaysia
6 Center for Smart Society 5.0, [CSS5], FIT, Taylor’s University, Subang Jaya, 47500, Selangor, Malaysia
7 Department of Information Security and Applied Computing, Eastern Michigan University, USA
8 School of Information Security and Applied Computing, Eastern Michigan University, USA

* Corresponding Author: Souvik Pal. Email:

Computers, Materials & Continua 2023, 74(2), 3865-3880. https://doi.org/10.32604/cmc.2023.029038

Received 23 February 2022; Accepted 21 June 2022; Issue published 31 October 2022

Abstract

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.

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Keywords

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