blockchain; cloud computing; edge computing; fog computing; Internet of Things; machine learning; Block-chain; Cloud-computing; Data stream; Edge computing; Learning models; Machine-learning; Open datum; Prototype models; Stream data; Unified architecture; Information Systems
Abstract :
[en] The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms.
Disciplines :
Computer science
Author, co-author :
Debauche, Olivier ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges (BIODYNE) ; Elevéo, R&D Service, Innovation Department, Awé Group, Ciney, Belgium ; Faculty of Engineering, ILIA Unit, University of Mons, Mons, Belgium
Nkamla Penka, Jean Bertin ; Faculty of Engineering, ILIA Unit, University of Mons, Mons, Belgium
Hani, Moad ; Faculty of Engineering, ILIA Unit, University of Mons, Mons, Belgium
Guttadauria, Adriano ; Faculty of Engineering, ILIA Unit, University of Mons, Mons, Belgium
Ait Abdelouahid, Rachida ; Faculty of Sciences Ben M’sik, Hassan II University—Casablanca, Casablanca, Morocco
Gasmi, Kaouther ; National Engineering School of Tunis, Tunis El Manar University, Tunis, Belgium
Ben Hardouz, Ouafae ; Faculty of Sciences Ben M’sik, Hassan II University—Casablanca, Casablanca, Morocco
Lebeau, Frédéric ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges (BIODYNE)
Bindelle, Jérôme ; Université de Liège - ULiège > TERRA Research Centre > Animal Sciences (AS)
Soyeurt, Hélène ; Université de Liège - ULiège > Département GxABT > Modélisation et développement
Gengler, Nicolas ; Université de Liège - ULiège > TERRA Research Centre > Animal Sciences (AS)
Manneback, Pierre ; Faculty of Engineering, ILIA Unit, University of Mons, Mons, Belgium
Benjelloun, Mohammed ; Faculty of Engineering, ILIA Unit, University of Mons, Mons, Belgium
This research was integrally funded Elevéo by Awé Group. Elevéo owns all intellectual property related to this research. The APC was integrally funded by MDPI Information.
Bagozi A. Bianchini D. De Antonellis V. Garda M. Marini A. A Relevance-based approach for Big Data Exploration Future Gener. Comput. Syst. 2019 101 51 69 10.1016/j.future.2019.05.056
Lee K.M. Yoo J. Kim S.W. Lee J.H. Hong J. Autonomic machine learning platform Int. J. Inf. Manag. 2019 49 491 501 10.1016/j.ijinfomgt.2019.07.003
Baylor D. Breck E. Cheng H.-T. Fiedel N. Foo C.Y. Haque Z. Haykal S. Ispir M. Jain V. Koc L. et al. Tfx: A tensorflow-based production-scale machine learning platform Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’17) Halifax, NS, Canada 13 August 2017 1387 1395
Zaharia M. Chen A. Davidson A. Ghodsi A. Hong S.A. Konwinski A. Murching S. Nykodym T. Ogilvie P. Parkhe M. et al. Accelerating the machine learning lifecycle with MLflow IEEE Data Eng. Bull. 2018 41 39 45
Nolack Fote F. Roukh A. Mahmoudi S. Mahmoudi S.A. Debauche O. Toward a big data knowledge-base management system for precision livestock farming Procedia Comput. Sci. 2020 177 136 142 10.1016/j.procs.2020.10.021
Debauche O. Trani J.-P. Mahmoudi S. Manneback P. Bindelle J. Mahmoudi S.A. Guttadauria A. Lebeau F. Data management and internet of things: A methodological review in smart farming Internet Things 2021 14 100378 10.1016/j.iot.2021.100378
Debauche O. Mahmoudi S. Manneback P. Lebeau F. Cloud and distributed architectures for data management in agriculture 4.0: Review and future trends J. King Saud-Univ.-Comput. Inf. Sci. 2022 34 7494 7514 10.1016/j.jksuci.2021.09.015
Nkamla Penka J.B. Mahmoudi S. Debauche O. An Optimized Kappa Architecture for IoT Data Management in Smart Farming Int. J. Ubiquitous Syst. Pervasive Netw. 2022 17 59 65
Chen T. Li M. Li Y. Lin M. Wang N. Wang M. Xiao T. Xu B. Zhang C. Zhang Z. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems arXiv 2015 1512.01274
Paszke A. Gross S. Massa F. Lerer A. Bradbury J. Chanan G. Killeen T. Lin Z. Gimelshein N. Antiga L. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library Advances in Neural Information Processing Systems Wallach H. Larochelle H. Beygelzimer A. d’Alché-Buc F. Fox E. Garnett R. Curran Associates, Inc. New York, NY, USA 2019 4399
Sergeev A. Del Balso M. Horovod: Fast and easy distributed deep learning in TensorFlow arXiv 2018 1802.05799
Chen K.-H. Su H.-P. Chuang W.-C. Hsiao H.-C. Tan W. Tang Z. Liu X. Liang Y. Lo W.-C. Ji W. et al. Apache submarine: A unified machine learning platform made simple Proceedings of the 2nd European Workshop on Machine Learning and Systems (EuroMLSys’22) Rennes, France 5–8 April 2022 101 108
Aggarwal C.C. Philip S.Y. Han J. Wang J. A framework for clustering evolving data streams Proceedings of the 29th International Conference on Very Large Databases (VLDB) Berlin, Germany 12–13 September 2003 R81 R92
Sayed D. Rady S. Aref M. Enhancing CluStream algorithm for CLUSTERING big data streaming over sliding window Proceedings of the 12th International Conference on Electrical Engineering (ICEENG) Cairo, Egypt 7–9 July 2020 108 114
Ahsani S. Sanati M.Y. Mansoorizadeh M. Improvement of CluStream algorithm using sliding window for the clustering of data streams Proceedings of the 11th International Conference on Computer Engineering and Knowledge (ICCKE) Mashhad, Iran 28–29 October 2021 434 440
Sangam R.S. Om H. Equi-Clustream: A framework for clustering time evolving mixed data Adv. Data Anal. Classif. 2018 12 973 995 10.1007/s11634-018-0316-3
Wang X. Sun Q. Research on Clustream Algorithm Based on Spark Proceedings of the 10th International Symposium on Computational Intelligence and Design (ISCID) Hangzhou, China 9–10 December 2017 Volume 2 219 222
Kumar A. Singh A. Singh R. An efficient hybrid-clustream algorithm for stream mining Proceedings of the 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Jaipur, India 4–7 December 2017 430 437
Grua E.M. Hoogendoorn M. Malavolta I. Lago P. Eiben A.E. Clustream-GT: Online clustering for personalization in the health domain Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI’19) Thessaloniki, Greece 14–17 October 2019 270 275
Akbar A. Khan A. Carrez F. Moessner K. Predictive Analytics for Complex IoT Data Streams IEEE Internet Things J. 2017 4 1571 1582 10.1109/JIOT.2017.2712672
Calabrese M. Cimmino M. Fiume F. Manfrin M. Romeo L. Ceccacci S. Paolanti M. Toscano G. Ciandrini G. Carrotta A. et al. SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0 Information 2020 11 202 10.3390/info11040202
Machorro-Cano I. Alor-Hernández G. Paredes-Valverde M.A. Rodríguez-Mazahua L. Sánchez-Cervantes J.L. Olmedo-Aguirre J.O. HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving Energies 2020 13 1097 10.3390/en13051097
Rashid R.A. Chin L. Sarijari M.A. Sudirman R. Ide T. Machine Learning for Smart Energy Monitoring of Home Appliances Using IoT Proceedings of the 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) Zagreb, Croatia 2–5 July 2019 66 71
Elsisi M. Tran M.-Q. Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles Sensors 2021 21 8467 10.3390/s21248467
Flores-Martin D. Rojo J. Moguel E. Berrocal J. Murillo J.M. Smart Nursing Homes: Self-Management Architecture Based on IoT and Machine Learning for Rural Areas Wirel. Commun. Mob. Comput. 2021 2021 8874988 10.1155/2021/8874988
Anh Khoa T. Phuc C.H. Lam P.D. Nhu L.M.B. Trong N.M. Phuong N.T.H. Dung N.V. Tan-Y N. Nguyen H.N. Duc D.N.M. Waste Management System Using IoT-Based Machine Learning in University Wirel. Commun. Mob. Comput. 2020 2020 6138637 10.1155/2020/6138637
Debauche O. Nkamla Penka J.B. Mahmoudi S. Lessage X. Hani M. Manneback P. Lufuluabu U.K. Bert N. Messaoudi D. Guttadauria A. RAMi: A New Real-Time Internet of Medical Things Architecture for Elderly Patient Monitoring Information 2022 13 2078 2489 10.3390/info13090423
Wang C. Huang X. Qiao J. Jiang T. Rui L. Zhang J. Kang R. Feinauer J. McGrail K.A. Wang P. et al. Apache IoTDB: Time-series database for internet of things Proc. VLDB Endow. 2020 13 2901 2904 10.14778/3415478.3415504
Abadi M. Agarwal A. Barham P. Brevdo E. Chen Z. Citro C. Corrado G.S. Davis A. Dean J. Devin M. et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems arXiv 2015 1603.04467
Roukh A. Fote F.N. Mahmoudi S.A. Mahmoudi S. Big data processing architecture for smart farming Procedia Comput. Sci. 2020 177 78 85 10.1016/j.procs.2020.10.014
Roukh A. Fote F.N. Mahmoudi S.A. Mahmoudi S. Wallesmart: Cloud platform for smart farming Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Vienna, Austria 7–9 July 2020 1 4
Kurtzer G.M. Sochat V. Bauer M.W. Singularity: Scientific containers for mobility of compute PLoS ONE 2017 12 e0177459 10.1371/journal.pone.0177459 28494014
