Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Abstract

Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.

A Fully Open-Source Approach to Intelligent Edge Computing: AGILE’s Lesson

“…A higher level of interoperability among different devices connected either to the same IoT gateway or through independently developed systems to the same IoT infrastructure represents a key factor for the uptake of the global IoT market. In this domain, we are focusing our research and innovation efforts on solutions capable of ensuring interoperability through automatic protocol translation [ 34 ]. This approach should mitigate some of the major issues of traditional approaches based either on simple hard-coded protocol gateways or on more complex adapters, proxies, and middlewares characterized by low scalability and high maintenance costs.…”

Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering

“…The interoperability problem examined in this paper is defined in terms of cyber-physical systems (CPSs) [22], [23] that have some related degrees of freedom or measurable properties to be improved, and that sends semantically related but syntactically different messages within each CPS, see [9] for further details. We investigate the feasibility of training autoencoders [24] to automatically generate translators for messages communicated between CPS taking part in a SoS.…”

“…The CPS co-exist in an environment, y = f (u). Aiming to improve some utility J(y) [9] an SoS is engineered by interconnecting the otherwise independently operating CPS. the different decoders produce messages containing the correct (purposeful) information.…”

“…This calls for dynamic connectivity and interoperability among different autonomous systems, with interfaces and links forming and vanishing to enable the SoS to achieve and sustain its capabilities. Achieving this goal is a challenging problem, see [9]- [12] for references and further details.…”

“…The problem to automatically translate symbol and information representations between different domains of reference [13] is well studied in the field of natural language processing, where increasingly flexible and potent machine learning methods have set the standard for accurate translation over the last few years [14]. In the field of IoT and industrial automation, the work has focused on translation based on discrete symbolic metadata [15], [16], with relatively few [9], [10] recent investigations of subsymbolic data-driven methods, like the deep alignment of ontologies using auxiliary datasets [17].…”