Model-driven interoperability: engineering heterogeneous IoT systems

Interoperability remains a significant burden to the developers of Internet of Things systems. This is because resources and APIs are dynamically composed; they are highly heterogeneous in terms of their underlying communication technologies, protocols and data formats, and interoperability tools remain limited to enforcing standards-based approaches. In this paper, we propose model-based engineering methods to reduce the development effort towards ensuring that complex software systems interoperate with one another. Lightweight interoperability models can be specified in order to monitor and test the execution of running software so that interoperability problems can be quickly identified, and solutions put in place. A graphical model editor and testing tool are also presented to highlight how a visual model improves upon textual specifications. We show using case-studies from the FIWARE Future Internet Service domain that the software framework can support non-expert developers to address interoperability challenges

A framework for Model-Driven Engineering of resilient software-controlled systems

AbstractEmergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones

Enabling High-Level Application Development in the Internet of Things

International audienceThe sensor networking field is evolving into the Internet of Things~(IoT), owing in large part to the increased availability of consumer sensing devices, including modern smart phones. However, application development in the IoT still remains challenging, since it involves dealing with several related issues, such as lack of proper identification of roles of various stakeholders, as well as lack of suitable (high-level) abstractions to address the large scale and heterogeneity in IoT systems. Although the software engineering community has proposed several approaches to address the above in the general case, existing approaches for IoT application development only cover limited subsets of above mentioned challenges. In this paper, we propose a multi-stage model-driven approach for IoT application development based on a precise definition of the role to be played by each stakeholder involved in the process -- domain expert, application designer, application developer, device developer, and network manager. The abstractions provided to each stakeholder are further customized using the inputs provided in the earlier stages by other stakeholders. We have also implemented code-generation and task-mapping techniques to support our approach. Our initial evaluation based on two realistic scenarios shows that the use of our techniques/framework succeeds in improving productivity in the IoT application development process

Low-Code as Enabler of Digital Transformation in Manufacturing Industry

[EN] Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided low-code framework able to manage the overall network of a collaborative manufacturing and logistics environment that enables humans, applications, and Internet of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment, promoting resilient digital transformation.This work was supported in part by the European Commission under the Grant Agreements No. 723710 and 825631.Sanchis, R.; Garcia-Perales, O.; Fraile Gil, F.; Poler, R. (2020). Low-Code as Enabler of Digital Transformation in Manufacturing Industry. Applied Sciences. 10(1):1-17. https://doi.org/10.3390/app10010012S117101Sanchis, R., & Poler, R. (2019). Enterprise Resilience Assessment—A Quantitative Approach. Sustainability, 11(16), 4327. doi:10.3390/su11164327Lowcomote: Training the Next Generation of Experts in Scalable Low-Code Engineering Platformshttps://www.se.jku.at/lowcomote-training-the-next-generation-of-experts-in-scalable-low-code-engineering-platforms/Waszkowski, R. (2019). Low-code platform for automating business processes in manufacturing. IFAC-PapersOnLine, 52(10), 376-381. doi:10.1016/j.ifacol.2019.10.060Lundell, B., & Lings, B. (2004). Changing perceptions of CASE technology. Journal of Systems and Software, 72(2), 271-280. doi:10.1016/s0164-1212(03)00087-6Fuggetta, A. (1993). A classification of CASE technology. Computer, 26(12), 25-38. doi:10.1109/2.247645Troy, D., & McQueen, R. (1997). An approach for developing domain specific CASE tools and its application to manufacturing process control. Journal of Systems and Software, 38(2), 165-192. doi:10.1016/s0164-1212(96)00120-3Huff, C. C. (1992). Elements of a realistic CASE tool adoption budget. Communications of the ACM, 35(4), 45-54. doi:10.1145/129852.129856Orlikowski, W. J. (1993). CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development. MIS Quarterly, 17(3), 309. doi:10.2307/249774Iivari, J. (1996). Why are CASE tools not used? Communications of the ACM, 39(10), 94-103. doi:10.1145/236156.236183Zolotas, C., Chatzidimitriou, K. C., & Symeonidis, A. L. (2018). RESTsec: a low-code platform for generating secure by design enterprise services. Enterprise Information Systems, 12(8-9), 1007-1033. doi:10.1080/17517575.2018.1462403GAVRILĂ, V., BĂJENARU, L., & DOBRE, C. (2019). Modern Single Page Application Architecture: A Case Study. Studies in Informatics and Control, 28(2). doi:10.24846/v28i2y201911Wu, Y., Wang, S., Bezemer, C.-P., & Inoue, K. (2018). How do developers utilize source code from stack overflow? Empirical Software Engineering, 24(2), 637-673. doi:10.1007/s10664-018-9634-5Hamming, R. W. (1950). Error Detecting and Error Correcting Codes. Bell System Technical Journal, 29(2), 147-160. doi:10.1002/j.1538-7305.1950.tb00463.xForresterhttps://go.forrester.com/The Maturity of Visual Programming. Режим дoступуhttp://www. craft. ai/blog/the-maturity-of-visualprogrammingVirtual Factory Operating Systemwww.vf-OS.euvf-OS D1.1: Vision Consensushttps://www.vf-os.eu/resultsvf-OS Wikihttps://cigipsrv1.cigip.upv.es:4430/mediawiki/index.php/Wiki_Homevf-OS D2.1: Global Architecture Definitionhttps://www.vf-os.eu/resultsSiemens MindSpherehttps://new.siemens.com/vn/en/products/software/mindsphere.htmlPTC ThingWorx Platformhttps://www.ptc.com/en/resources/iiot/product-brief/thingworx-platformGE Predixhttps://www.ge.com/digital/iiot-platformIBM Cloudhttps://www.ibm.com/cloudMicrosoft Azure IOT Suitehttps://azure.microsoft.com/es-es/blog/microsoft-azure-iot-suite-connecting-your-things-to-the-cloud/Software AG ADAMOShttps://www.softwareag.com/corporate/company/adamos/default.htm