Mostrar el registro sencillo del ítem
dc.contributor.author | Guillén-Navarro, Miguel A. | es_ES |
dc.contributor.author | Llanes, Antonio | es_ES |
dc.contributor.author | Imbernón, Baldomero | es_ES |
dc.contributor.author | Martínez-España, Raquel | es_ES |
dc.contributor.author | Bueno-Crespo, Andrés | es_ES |
dc.contributor.author | Cano, Juan-Carlos | es_ES |
dc.contributor.author | Cecilia-Canales, José María | es_ES |
dc.date.accessioned | 2023-09-13T18:00:25Z | |
dc.date.available | 2023-09-13T18:00:25Z | |
dc.date.issued | 2021-01 | es_ES |
dc.identifier.issn | 0920-8542 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/196324 | |
dc.description.abstract | [EN] The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming "Smart" thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which is opening up new paths to bring digitization into the new era. However, there is still a big gap between AI and IoT, which is basically in the computational power required by the former and the lack of computational resources offered by the latter. This is particularly true in rural IoT environments where the lack of connectivity (or low-bandwidth connections) and power supply forces the search for "efficient" alternatives to provide computational resources to IoT infrastructures without increasing power consumption. In this paper, we explore edge computing as a solution for bridging the gaps between AI and IoT in rural environment. We evaluate the training and inference stages of a deep-learning-based precision agriculture application for frost prediction in modern Nvidia Jetson AGX Xavier in terms of performance and power consumption. Our experimental results reveal that cloud approaches are still a long way off in terms of performance, but the inclusion of GPUs in edge devices offers new opportunities for those scenarios where connectivity is still a challenge. | es_ES |
dc.description.sponsorship | This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 (AEI/FEDER, UE) and RTC-2017-6389-5. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer-Verlag | es_ES |
dc.relation.ispartof | The Journal of Supercomputing | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Edge computing | es_ES |
dc.subject | LSTM | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Precision Agriculture | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s11227-020-03288-w | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RTC-2017-6389-5-AR//PLANIFICACIÓN Y GESTIÓN DE RECURSOS HÍDRICOS A PARTIR DE ANÁLISIS DE DATOS DE IOT/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.date.embargoEndDate | 1999-01-01 | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors | es_ES |
dc.description.bibliographicCitation | Guillén-Navarro, MA.; Llanes, A.; Imbernón, B.; Martínez-España, R.; Bueno-Crespo, A.; Cano, J.; Cecilia-Canales, JM. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing. 77:818-840. https://doi.org/10.1007/s11227-020-03288-w | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s11227-020-03288-w | es_ES |
dc.description.upvformatpinicio | 818 | es_ES |
dc.description.upvformatpfin | 840 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 77 | es_ES |
dc.relation.pasarela | S\425065 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia | es_ES |
dc.description.references | Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, et al. (2016) Tensorflow: a system for large-scale machine learning. In: 12th $$\{$$USENIX$$\}$$ Symposium on Operating Systems Design and Implementation ($$\{$$OSDI$$\}$$ 16), pp 265–283 | es_ES |
dc.description.references | Aliberti A, Bottaccioli L, Macii E, Di Cataldo S, Acquaviva A, Patti E (2019) A non-linear autoregressive model for indoor air-temperature predictions in smart buildings. Electronics 8(9):979 | es_ES |
dc.description.references | Bah MD, Dericquebourg E, Hafiane A, Canals R (2018) Deep learning based classification system for identifying weeds using high-resolution UAV imagery. In: Science and Information Conference. Springer, pp 176–187 | es_ES |
dc.description.references | Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. ACM, pp 13–16 | es_ES |
dc.description.references | Boubin J, Chumley J, Stewart C, Khanal S (2019) Autonomic computing challenges in fully autonomous precision agriculture. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), pp 11–17. IEEE | es_ES |
dc.description.references | Cass S (2019) Taking AI to the edge: Google’s TPU now comes in a maker-friendly package. IEEE Spectr 56(5):16–17 | es_ES |
dc.description.references | Chen X, Shi Q, Yang L, Xu J (2018) Thriftyedge: Resource-efficient edge computing for intelligent IoT applications. IEEE Netw 32(1):61–65 | es_ES |
dc.description.references | Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E (2014) cuDNN: efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 | es_ES |
dc.description.references | Farooq MS, Riaz S, Abid A, Abid K, Naeem MA (2019) A survey on the role of IoT in agriculture for the implementation of smart farming. IEEE Access 7:156237–156271 | es_ES |
dc.description.references | Gondchawar N, Kawitkar R (2016) Iot based smart agriculture. Int J Adv Res Comput Commun Eng 5(6):838–842 | es_ES |
dc.description.references | Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge | es_ES |
dc.description.references | Graves A, Mohamed Ar, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 6645–6649. IEEE | es_ES |
dc.description.references | Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418–424 | es_ES |
dc.description.references | Guillén-Navarro MA, Martínez-España R, Llanes A, Bueno-Crespo A, Cecilia JM (2020) A deep learning model to predict lower temperatures in agriculture. J Ambient Intell Smart Environ 12(1):21–34 | es_ES |
dc.description.references | Guillén-Navarro MA, Martínez-España R, López B, Cecilia JM (2019) A high-performance IoT solution to reduce frost damages in stone fruits. Concurr Comput Pract Exper. https://doi.org/10.1002/cpe.5299 | es_ES |
dc.description.references | Gulli A, Pal S (2017) Deep learning with Keras. Packt Publishing Ltd, Birmingham | es_ES |
dc.description.references | Halawa H, Abdelhafez HA, Boktor A, Ripeanu M (2017) Nvidia Jetson platform characterization. In: EuropEan Conference on Parallel Processing. Springer, pp 92–105 | es_ES |
dc.description.references | Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780 | es_ES |
dc.description.references | Kamilaris A, Prenafeta-Boldu FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90 | es_ES |
dc.description.references | Khosravi A, Koury R, Machado L, Pabon J (2018) Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustain Energy Technol Assessm 25:146–160 | es_ES |
dc.description.references | Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-247 | es_ES |
dc.description.references | Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778–782 | es_ES |
dc.description.references | Kuwata K, Shibasaki R (2015) Estimating crop yields with deep learning and remotely sensed data. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, pp 858–861 | es_ES |
dc.description.references | Li Y, Zhao K, Chu X, Liu J (2013) Speeding up k-means algorithm by gpus. J Comput Syst Sci 79(2):216–229 | es_ES |
dc.description.references | Liakos K, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: a review. Sensors 18(8):2674 | es_ES |
dc.description.references | Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M (2018) Deep learning for iot big data and streaming analytics: A survey. IEEE Commun Surv Tutor 20(4):2923–2960 | es_ES |
dc.description.references | Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419 | es_ES |
dc.description.references | Pierpaoli E, Carli G, Pignatti E, Canavari M (2013) Drivers of precision agriculture technologies adoption: a literature review. Proc Technol 8:61–69 | es_ES |
dc.description.references | Qi S, Wan L, Fu B (2018) Multisource and multiuser water resources allocation based on genetic algorithm. J Supercomput, pp 1–9 | es_ES |
dc.description.references | Rodriguez SAB, Klein L, Schrott AG, Van Kessel TG (2019) Autonomous mobile platform and variable rate irrigation method for preventing frost damage . US Patent App. 10/219,448 | es_ES |
dc.description.references | Salman AG, Heryadi Y, Abdurahman E, Suparta W (2018) Single layer & multi-layer long short-term memory (lstm) model with intermediate variables for weather forecasting. Proc Comput Sci 135:89–98 | es_ES |
dc.description.references | Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39 | es_ES |
dc.description.references | Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23 | es_ES |
dc.description.references | Seide F, Agarwal A (2016) CNTK: Microsoft’s open-source deep-learning toolkit. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 2135–2135 | es_ES |
dc.description.references | Simoens P, Xiao Y, Pillai P, Chen Z, Ha K, Satyanarayanan M (2013) Scalable crowd-sourcing of video from mobile devices. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp 139–152. ACM | es_ES |
dc.description.references | Zamora-Izquierdo MA, Santa J, Martínez JA, Martínez V, Skarmeta AF (2019) Smart farming iot platform based on edge and cloud computing. Biosyst Eng 177:4–17 | es_ES |
dc.description.references | Zaytar MA, El Amrani C (2016) Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int Comput Appl 143(11):7–11 | es_ES |
dc.description.references | Zhang N, Wang M, Wang N (2002) Precision agriculture a worldwide overview. Comput Electron Agric 36(2–3):113–132 | es_ES |
dc.description.references | Zhou Z, Chen X, Li E, Zeng L, Luo K, Zhang J (2019) Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proc IEEE 107(8):1738–1762 | es_ES |