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

Autor: André, Geraldine, Bachelet, Bruno, Battistoni, Pietro, Belhassena, Amina, Bimonte, Sandro, Cariou, Christophe, Chabot, Frederic, Chalhoub, Gerard, Couvent, Adrian, Garani, Georgia, Laneurit, Jean, Moussa, Rim, Oikonomou, Konstantinos, Sammour, Ibrahim, Sebillo, Monica, Souza, Mateus Vilela, Tricot, Nicolas, Wrembel, Robert
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Zdroj: Cluster Computing; Oct2023, Vol. 26 Issue 5, p2993-3015, 23p
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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