research-article

Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning

Authors:

Hameedur Rahman,

Uzair Muzamil Shah,

Syed Morsleen Riaz,

Kashif Kifayat,

Syed Atif Moqurrab,

Joon YooAuthors Info & Claims

Volume 156, Issue C

Pages 285 - 300

https://doi.org/10.1016/j.future.2024.03.023

Published: 01 July 2024 Publication History

Abstract

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.

Highlights

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Addresses arable land challenges with Digital Twin.

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Utilizes real-time data for optimized decisions.

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Explores NGMN for enhanced IoT operations.

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Demonstrates concept with practical framework.

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Enhances Smart Greenhouse with ML techniques.

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Cited By

Hakiri ABen Yahia SGokhale AMellouli N(2024)Special Issue on Digital Twin for Future Networks and Emerging IoT Applications (DT4IoT)Future Generation Computer Systems10.1016/j.future.2024.06.056161:C(81-84)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.future.2024.06.056

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cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 156, Issue C

Jul 2024

339 pages

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Copyright © 2024.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2024

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Cited By

Hakiri ABen Yahia SGokhale AMellouli N(2024)Special Issue on Digital Twin for Future Networks and Emerging IoT Applications (DT4IoT)Future Generation Computer Systems10.1016/j.future.2024.06.056161:C(81-84)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.future.2024.06.056

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