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LambdAgrIoT: a new architecture for agricultural autonomous robots’ scheduling: from design to experiments

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Abstract

The usage of IoT and robots is more and more present in smart farming, and in particular in agro-ecology since robots are able to provide smart practices and avoid repetitive human tasks. However, these new technologies rise several research issues, which are strongly inter-related, about Farm Management Information System, such as robots’ programming, sensor data capture, management and processing at different layers of the IoT ecosystem. In particular, scheduling the tasks of different autonomous agricultural robots needs for a complex architecture that support at the same time real-time monitoring of robots and analysis of their historical data (Belhassena et al., Towards an architecture for agricultural autonomous robots’ scheduling. In: 2021 IEEE 25th international enterprise distributed object computing workshop (EDOCW), 2021. IEEE Computer Society, Los Alamitos, pp 194–203, 2021, https://doi.org/10.1109/EDOCW52865.2021.00049). Many studies investigated these issues, but to the best of our knowledge none has contributed with a fully-featured architecture design of monitoring and scheduling of autonomous agricultural robots. This work extends our previous work, where we propose a new architecture for autonomous agriculture robots scheduling, called LambdAgrIoT. LambdAgrIoT is designed to support big data and different types of workload (real-time, near real-time, analytic, and CRUD). We present the main features of each layer, and the implementation details. We also put to the test our LambdAgrIoT architecture using simulated data, and providing a real experience in a field. Results from real experiments show the feasibility of our new proposal.

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Data availability

The datasets generated during and/or analysed during the current study are not publicly available due to confidential reasons.

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  • 13 November 2022

    The original online version of this article was revised: The author biographies and photos were mismatched, the biographies and photos have been corrected now.

Notes

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Funding

This work is supported by the French National Research Agency Project ANR-19-LCV2-0011 Tiara, and French Government IDEX-ISITE Initiative 16-IDEX-0001 (CAP 20-25).

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Authors and Affiliations

  1. INRAE, TSCF, University Clermont Auvergne, Clermont-Ferrand, France

    Geraldine André, Amina Belhassena, Sandro Bimonte, Christophe Cariou, Frederic Chabot, Adrian Couvent, Jean Laneurit & Nicolas Tricot

  2. University of Salerno, Fisciano, Italy

    Pietro Battistoni & Monica Sebillo

  3. University of Clermont Auvergne, Clermont-Ferrand, France

    Bruno Bachelet, Gerard Chalhoub, Ibrahim Sammour & Mateus Vilela Souza

  4. University of Carthage, Carthage, Tunisia

    Rim Moussa

  5. Poznan University of Technology, Poznan, Poland

    Robert Wrembel

  6. University of Thessaly, Thessaly, Greece

    Georgia Garani & Konstantinos Oikonomou

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  1. Geraldine André
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. All authors write the manuscript. All authors read and approved the final manuscript.

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Correspondence to Geraldine André.

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This paper extends our previous EAIoT 2021 paper [7].

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André, G., Bachelet, B., Battistoni, P. et al. LambdAgrIoT: a new architecture for agricultural autonomous robots’ scheduling: from design to experiments. Cluster Comput 26, 2993–3015 (2023). https://doi.org/10.1007/s10586-022-03592-5

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