The technical work performed within the first 18 months of the project, even if some limited deviations have been necessary mainly due to the pandemic situation, has achieved the primary planned objectives.
About the technical work packages two primary categories of innovation activities have been performed, namely the work about the IoTwins framework and the work about the realization of the different testbeds.
In the first category, WP2 and WP3 have intensely worked on the common understanding, definition, design, implementation, and first validation of the IoTwins framework, which has already started to be used by the IoTwins testbeds, as detailed below. In particular and in short, WP2 has worked on identifying the requirements, selecting the technologies, and developing the mechanisms needed in the IoTwins framework to distributedly manage digital representations of physical assets in both the manufacturing and facility management domains. The primary results already obtained by WP2 are: i) identification of requirements and growth of common understanding about distributed and hybrid digital twins among the partners; ii) definition of the reference architecture for the IoTwins framework, capable of fulfilling the need for extreme flexibility, adaptivity, and extensibility to embrace the different requirements and characteristics of the multiple IoTwins testbeds; iii) preparation of instantiations of the IoTwins platform for different use cases, with differentiated solutions to be integrated at edge nodes and at the backbone infrastructure (cloud and HPC resources). This has also required the development and extension of orchestration and security solutions, as in the Proof of Concept demonstrator of the IoTwins reference architecture, which for example uses the Indigo PaaS Orchestrator, IAM for authentication, and Mesos clusters at the edge.
By passing to WP3, as planned, this work package has worked on the definition, design, and implementation of the AI service part of the IoTwins framework, with the primary goal of developing AI services that can be re-used and can benefit multiple IoTwins use-cases (and others in the future). The IoTwins AI services have already started to be tailored to the specific needs of some testbeds, which are adopting and integrating them in their workflows; they are already available in a common repository, structured into classes for easier usability (8 general services, 4 anomaly detection ones, 8 time-series specific one, 1 about model introspection, and 1 optimization service, developed so far). This is an initial working version of the IoTwins AI services; they will be modified and extended by following the evaluation and feedback stage of the following months. Let us recall the crucial point that the provided services are especially devised for the specific needs of industrial partners and of SMEs, thus making them quite different from the general-purpose services that can be offered by “traditional” and commercial cloud service providers. IoTwins AI services were developed as a collection of self-contained Docker containers; their code is available at the following Git repository:
https://gitlab.hpc.cineca.it/iotwins/ai-services/(opens in new window).
About all the testbed activities (WP4, WP5, and WP6), very careful attention was posed to correctly communicating the methodology and the significant advantages of adopting the common IoTwins framework, also by trying to win the natural resistance to innovation associated with the fact that several industrial testbeds were not developed from scratch (similarly to what happens almost always in the industrial domain and to what will be regular for SMEs adopting IoTwins solutions in the future). The details about the activity progress for each single testbed (4 in the advanced manufacturing domain, 3 in the facility management domain, and two for replicability/scalability currently in their early stage of development) are reported in the WP-level technical reports.
