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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, calledLambdAgrIoT.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 ourLambdAgrIoT 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.

Change history

  • 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.

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References

  1. Afrin, M., Jin, J., Rahman, A., et al.: Resource allocation and service provisioning in multi-agent cloud robotics: a comprehensive survey. IEEE Commun. Surv. Tutor. (2021).https://doi.org/10.1109/COMST.2021.3061435

    Article  Google Scholar 

  2. Aissi, H., Bazgan, C., Vanderpooten, D.: Min–max and min–max regret versions of combinatorial optimization problems: a survey. Eur. J. Oper. Res.197(2), 427–438 (2009)

    Article MathSciNet MATH  Google Scholar 

  3. Alsahfi, T., Almotairi, M., Elmasri, R.: A survey on trajectory data warehouse. Spat. Inf. Res.28(1), 53–66 (2020)

    Article  Google Scholar 

  4. Arooj, A., Farooq, M.S., Akram, A., et al.: Big data processing and analysis in Internet of vehicles: architecture, taxonomy, and open research challenges. Arch. Comput. Methods Eng.29, 793–829 (2021)

    Article  Google Scholar 

  5. Ayaz, M., Ammad-Uddin, M., Sharif, Z., et al.: Internet-of-Things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access7(1), 129551–129583 (2019)

    Article  Google Scholar 

  6. Bechtsis, D., Moisiadis, V., Tsolakis, N., et al.: Scheduling and control of unmanned ground vehicles for precision farming: a real-time navigation tool. In: International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA), 2017, pp. 180–187 (2017)

  7. Belhassena, A., Battistoni, P., Souza, M., et al.: Towards an architecture for agricultural autonomous robots’ scheduling. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), 2021, pp 194–203. IEEE Computer Society, Los Alamitos (2021).https://doi.org/10.1109/EDOCW52865.2021.00049

  8. Belhassena, A., Bimonte, S., Battistoni, P., et al.: On modeling data for IoT agroecology applications by means of a UML profile. In: International Conference on Management of Digital EcoSystems (MEDES), 2021 (2021)

  9. Belhassena, A., Bimonte, S., Battistoni, P., et al.: On modeling data for IoT agroecology applications by means of a UML profile. In: Chbeir, R., Manolopoulos, Y., Bellatreche, L., et al. (eds) MEDES ’21: Proceedings of the 13th International Conference on Management of Digital EcoSystems, Virtual Event, Tunisia, 1–3 November 2021, pp 120–128. ACM (2021).https://doi.org/10.1145/3444757.3485109

  10. Bimonte, S.: Current approaches, challenges, and perspectives on spatial OLAP for agri-environmental analysis. Int. J. Agric. Environ. Inf. Syst.7(4), 32–49 (2016).https://doi.org/10.4018/IJAEIS.2016100103

    Article  Google Scholar 

  11. Bimonte, S., Edoh-Alove, É., Coulibaly, F.A.: Map4OLAP: a web-based tool for interactive map visualization of OLAP queries. In: Chen, Y., Ludwig, H., Tu, Y., et al. (eds) 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 15–18 December 2021, pp 3747–3750. IEEE (2021).https://doi.org/10.1109/BigData52589.2021.9671574

  12. Bochtis, D.D., Sørensen, C.G., Busato, P.: Advances in agricultural machinery management: a review. Biosyst. Eng.126, 69–81 (2014)

    Article  Google Scholar 

  13. Card, S.K., Mackinlay, J.D., Shneiderman, B.: Readings in Information Visualization—Using Vision to Think. Academic, London (1999)

    Google Scholar 

  14. Cariou, C., Gobor, Z., Seiferth, B., et al.: Mobile robot trajectory planning under kinematic and dynamic constraints for partial and full field coverage. J. Field Robot.34(7), 1297–1312 (2017)

    Article  Google Scholar 

  15. Cordeau, J.F., Desaulniers, G., Desrousiers, J., et al.: VRP with time windows. In: The Vehicle Routing Problem. SIAM Monographs on Discrete Mathematics and Applications, pp. 157–193. SIAM, Philadelphia (2002)

  16. Dalgaard, T., Hutchings, N., Porter, J.: Agroecology, scaling and interdisciplinarity. Agric. Ecosyst. Environ.100(1), 39–51 (2003)

    Article  Google Scholar 

  17. Debauche, O., El Moulat, M., Mahmoudi, S., et al.: Irrigation pivot-center connected at low cost for the reduction of crop water requirements. In: International Conference on Advanced Communication Technologies and Networking (CommNet), 2018, pp 1–9. IEEE (2018)

  18. Deremetz, M., Couvent, A., Lenain, R., et al.: A generic control framework for mobile robots edge following. In: Proceedings of International Conference on Informatics in Control, Automation and Robotics, 2019, pp 104–113 (2019)

  19. Dobbelaere, P., Esmaili, K.S.: Kafka versus RabbitMQ: a comparative study of two industry reference publish/subscribe implementations: industry paper. In: ACM International Conference on Distributed and Event-based Systems (DEBS), 2017, pp. 227–238 (2017)

  20. Edwards, G.T., Hinge, J., Skou-Nielsen, N., et al.: Route planning evaluation of a prototype optimised infield route planner for neutral material flow agricultural operations. Biosyst. Eng.153, 149–157 (2017)

    Article  Google Scholar 

  21. Emani, C.K., Cullot, N., Nicolle, C.: Understandable big data: a survey. Comput. Sci. Rev.17, 70–81 (2015)

    Article MathSciNet  Google Scholar 

  22. Emmi, L., Gonzalez-de Soto, M., Pajares, G., et al.: New trends in robotics for agriculture: integration and assessment of a real fleet of robots. Sci. World J. (2014).https://doi.org/10.1155/2014/404059

    Article  Google Scholar 

  23. Fountas, S., Carli, G., Sørensen, C.G., et al.: Farm management information systems: current situation and future perspectives. Comput. Electron. Agric.115, 40–50 (2015)

    Article  Google Scholar 

  24. Gonzalez-de Santos, P., Fernández, R., Sepúlveda, D., et al.: Field robots for intelligent farms—inhering features from industry. Agronomy10(11), 1638 (2020)

    Article  Google Scholar 

  25. Hesse, G., Matthies, C., Uflacker, M.: How fast can we insert? An empirical performance evaluation of Apache Kafka. In: IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2020, pp 641–648 (2020)

  26. Iftikhar, N., Lachowicz, B.P., Madarasz, A., et al.: Real-time visualization of sensor data in smart manufacturing using Lambda architecture. In: International Conference on Data Science, Technology and Applications (DATA), 2020, pp. 215–222 (2020)

  27. Khorov, E., Kiryanov, A., Lyakhov, A., et al.: A tutorial on IEEE 802.11ax high efficiency WLANs. IEEE Commun. Surv. Tutor. (2019).https://doi.org/10.1109/COMST.2018.2871099

    Article  Google Scholar 

  28. Krishnan, A., Swarna, S., Balasubramanya, S.H.: Robotics, IoT, and AI in the automation of agricultural industry: a review. In: Proceedings of B-HTC, 2020, pp. 1–6 (2020)

  29. Lujak, M., Sklar, E., Semet, F.: Agriculture fleet vehicle routing: a decentralised and dynamic problem. AI Commun.34(1), 55–71 (2021)

    Article MathSciNet  Google Scholar 

  30. Luo, X., Zhang, L.: The optimal scheduling model for agricultural machinery resources with time-window constraints. Int. J. Simul. Model.15(4), 721–731 (2016)

    Article  Google Scholar 

  31. MacEachren, A.M., Gahegan, M., Pike, W., et al.: Geovisualization for knowledge construction and decision support. IEEE Comput. Graph. Appl.24(1), 13–17 (2004)

    Article  Google Scholar 

  32. Mahale, R.B., Sonavane, S.: Smart poultry farm monitoring using IoT and wireless sensor networks. Int. J. Adv. Res. Comput. Sci. (2016).https://doi.org/10.26483/ijarcs.v7i3.2665

    Article  Google Scholar 

  33. Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning, New York (2015)

    Google Scholar 

  34. Maurel, V.B., Huyghe, C.: Putting agricultural equipment and digital technologies at the cutting edge of agroecology. Ol. Corps Gras Lipides24(3), 1–7 (2017)

    Google Scholar 

  35. Narayan, S., Jayawardena, C., Wang, J., et al.: Performance test of IEEE 802.11ac wireless device. In: International Conference on Computer Communication and Informatics, 2015 (2015).https://doi.org/10.1109/ICCCI.2015.7218076

  36. Navarro, E., Costa, N., Pereira, A.: A systematic review of IoT solutions for smart farming. Sensors20(15), 4231 (2020)

    Article  Google Scholar 

  37. Nukala, R., Panduru, K., Shields, A., et al.: Internet of Things: a review from ‘farm to fork’. In: Irish Signals and Systems Conference (ISSC), 2016, pp. 1–6 (2016)

  38. Pandya, A., Odunsi, O., Liu, C., et al.: Adaptive and efficient streaming time series forecasting with Lambda architecture and Spark. In: IEEE International Conference on Big Data, 2020, pp. 5182–5190. IEEE (2020)

  39. Percivall, G.: Realizing the geospatial potential of mobile, IoT and big data. In: Liao, L. (ed.) International Conference on Computing for Geospatial Research and Application, 2012, p. 8. ACM (2012)

  40. Pinedo, M., Zacharias, C., Zhu, N.: Scheduling in the service industries: an overview. J. Syst. Sci. Syst. Eng.24(1), 1–48 (2015)

    Article  Google Scholar 

  41. Ribeiro de Almeida, D., de Souza, B.C., Gomes de Andrade, F., et al.: A survey on big data for trajectory analytics. ISPRS Int. J. Geo-Inf.9(2), 88 (2020)

    Article  Google Scholar 

  42. Rossit, D.A., Tohmé, F., Frutos, M.: Industry 4.0: smart scheduling. Int. J. Prod. Res.57(12), 3802–3813 (2019)

    Article  Google Scholar 

  43. Roukh, A., Fote, F.N., Mahmoudi, S.A., et al.: Big data processing architecture for smart farming. Procedia Comput. Sci.177, 78–85 (2020)

    Article  Google Scholar 

  44. Saint-Guillain, M., Deville, Y., Solnon, C.: A multistage stochastic programming approach to the dynamic and stochastic VRPTW. In: Michel, L. (ed.) International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 2015, pp. 357–374 (2015)

  45. Seyyedhasani, H., Dvorak, J.S.: Dynamic rerouting of a fleet of vehicles in agricultural operations through a dynamic multiple depot vehicle routing problem representation. Biosyst. Eng.171, 63–77 (2018)

    Article  Google Scholar 

  46. Solomon, M.M.: Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res.35(2), 254–265 (1987)

    Article MathSciNet MATH  Google Scholar 

  47. Villa-Henriksen, A., Edwards, G.T., Pesonen, L.A., et al.: Internet of Things in arable farming: implementation, applications, challenges and potential. Biosyst. Eng.191, 60–84 (2020).https://doi.org/10.1016/j.biosystemseng.2019.12.013

    Article  Google Scholar 

  48. Vitali, G., Francia, M., Golfarelli, M., et al.: Crop management with the IoT: an interdisciplinary survey. Agronomy11(1) (2021).https://www.mdpi.com/2073-4395/11/1/181

  49. Wellington, C., Courville, A., Stentz, A.T.: A generative model of terrain for autonomous navigation in vegetation. Int. J. Robot. Res.25(12), 1287–1304 (2006)

    Article  Google Scholar 

  50. Wolfert, S., Ge, L., Verdouw, C., et al.: Big data in smart farming—a review. Agric. Syst.153, 69–80 (2017)

    Article  Google Scholar 

  51. Wulfsohn, D., Zamora, F.A., Téllez, C.P., et al.: Multilevel systematic sampling to estimate total fruit number for yield forecasts. Precis. Agric.13(2), 256–275 (2012)

    Article  Google Scholar 

  52. Zaharia, M., Chowdhury, M., Das, T., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: USENIX Symposium on Networked Systems Design and Implementation, 2012, pp. 15–28 (2012)

  53. Zaharia, M., Xin, R.S., Wendell, P., et al.: Apache Spark: a unified engine for big data processing. Commun. ACM59(11), 56–65 (2016)

    Article  Google Scholar 

  54. Zhai, Z., Martínez, J.F., Beltran, V., et al.: Decision support systems for agriculture 4.0: survey and challenges. Comput. Electron. Agric. (2020).https://doi.org/10.1016/j.compag.2020.105256

    Article  Google Scholar 

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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

Authors
  1. Geraldine André

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  2. Bruno Bachelet

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  3. Pietro Battistoni

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  4. Amina Belhassena

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  5. Sandro Bimonte

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  6. Christophe Cariou

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  7. Frederic Chabot

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  8. Gerard Chalhoub

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  9. Adrian Couvent

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  10. Georgia Garani

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  11. Jean Laneurit

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  12. Rim Moussa

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  13. Konstantinos Oikonomou

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  15. Monica Sebillo

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  16. Mateus Vilela Souza

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  17. Nicolas Tricot

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  18. Robert Wrembel

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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 toGeraldine 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 Comput26, 2993–3015 (2023). https://doi.org/10.1007/s10586-022-03592-5

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