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Computing paradigms for smart farming in the era of drones: a systematic review

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Abstract

In the current era of agricultural robotization, it is necessary to use a suitable automated data collection system for constant plant, animal, and machine monitoring. In this context, cloud computing (CC) is a well-established paradigm for building service-centric farming applications. However, the huge amount of data has put an important burden on data centers and network bandwidth and pointed out issues that cloud-based applications face such as large latency, bottlenecks because of central processing, compromised security, and lack of offline processing. Fog computing (FC), edge computing (EC), and mobile edge computing (MEC) (or flying edge computing FEC) are gaining exponential attention and becoming attractive solutions to bring CC processes within reach of users and address computation-intensive offloading and latency issues. These paradigms from cloud to mobile edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations and challenges. This paper carries out a systematic review of the latest high-quality literature and aims to identify similarities, differences, and the main use cases in the mentioned computing paradigms, particularly when using drones. Our expectation from this work is to become a good reference for researchers and help them address hot topics and challenging issues related to this scope.

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Funding

This research was conducted under the project PHC-Utique 21G1116 funded by the partnership Hubert Curien “Utique” of the French Ministry of Europe and Foreign Affairs and the Tunisian Ministry of Higher Education and Scientific Research.

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  1. ENSI, University of Mannouba Cristal Lab, Manouba, Tunisia

    Sourour Dhifaoui, Chiraz Houaidia & Leila Azouz Saidane

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Dhifaoui, S., Houaidia, C. & Saidane, L.A. Computing paradigms for smart farming in the era of drones: a systematic review. Ann. Telecommun. 79, 35–59 (2024). https://doi.org/10.1007/s12243-023-00997-0

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