Abstract
Smart Factories characterize as context-rich, fast-changing environments where heterogeneous hardware appliances are found beside of also heterogeneous software components deployed in (or directly interfacing with) IoT devices, as well as in on-premise mainframes, and on the Cloud. This inherent heterogeneity poses major challenges particularly when a high degree of resiliency is needed, and the ubiquitously deployed software components must be replaced or reconfigured at real-time to respond to the most diverse events, ranging from an out-of-range sensor detection, to a new order issued by a customer. In this work, a software framework is presented, which allows to deploy, (re)configure, run, and monitor the most diverse software across all the three layers of the Smart Factory (edge, fog, Cloud), from remote, via API calls, in a standardised uniform manner, relying on containerization technologies, and on a variety of software technologies, frameworks, and programming languages, including Node-RED, MQTT, Scala, Apache Spark, and Kafka. The most recent advances in the framework design, implementation, and demonstration, which led to the introduction of the so-called Crazy Nodes, are presented and motivated. A comprehensive proof-of-concept is given, where user interfaces and distributed systems are created from scratch via API calls to implement AI-based alerting systems, Big Data stream filtering and transformation, AI model training, storage, and usage for one-shot as well as stream predictions, and real-time Big Data visualization through line plots, histograms, and pie charts.
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant Number SFI/16/RC/3918 (Confirm). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. 48(2), 527–540 (1999)
Bortolini, M., Galizia, F.G., Mora, C.: Reconfigurable manufacturing systems: literature review and research trend. J. Manuf. Syst. 49, 93–106 (2018)
Zennaro, I., Finco, S., Battini, D., Persona, A.: Big size highly customised product manufacturing systems: a literature review and future research agenda. Int. J. Prod. Res. 57(15–16), 5362–5385 (2019)
Lebovitz, R., Graban, M.: The journey toward demand driven manufacturing. In: Proceedings 2nd International Workshop on Engineering Management for Applied Technology. EMAT 2001, pp. 29–35 (2001). IEEE
Haghnegahdar, L., Joshi, S.S., Dahotre, N.B.: From IoT-based cloud manufacturing approach to intelligent additive manufacturing: industrial Internet of Things—an overview. Int. J. Adv. Manuf. Technol. 119, 1–18 (2021). https://doi.org/10.1007/s00170-021-08436-x
Adamo, A., et al.: On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system. Science 352(6281), 61–67 (2016)
Singh, P.: Airflow. In: Learn PySpark, pp. 67–84. Apress, Berkeley, CA (2019). https://doi.org/10.1007/978-1-4842-4961-1_4
Soderi, M., Kamath, V., Morgan, J., Breslin, J.G.: Ubiquitous System Integration as a Service in Smart Factories. In: 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), pp. 261–267 (2021). IEEE
Soderi, M., Kamath, V., Morgan, J., Breslin, J.G.: Advanced analytics as a Service in Smart Factories. In: 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000425–000430 (2022). IEEE
Soderi, M., Kamath, V., Breslin, J.G.: A demo of a software platform for ubiquitous big data engineering, visualization, and analytics, via reconfigurable micro-services, in smart factories. In: 2022 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–3 (2022). IEEE
Soderi, M., Kamath, V., Breslin, J.G.: Toward an API-driven infinite cyber-screen for custom real-time display of big data streams. In: 2022 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 153–155 (2022). IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Soderi, M., Breslin, J.G. (2022). Crazy Nodes: Towards Ultimate Flexibility in Ubiquitous Big Data Stream Engineering, Visualisation, and Analytics, in Smart Factories. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Practice. ISoLA 2022. Lecture Notes in Computer Science, vol 13704. Springer, Cham. https://doi.org/10.1007/978-3-031-19762-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-19762-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19761-1
Online ISBN: 978-3-031-19762-8
eBook Packages: Computer ScienceComputer Science (R0)