An Engineering Process model for managing a digitalised life-cycle of products in the Industry 4.0

The Internet of Things (IoT), and more specifically the industrial IoT, is revolutionising industry. This technology has catalyzed the fourth industrial revolution and inspired movements such as Industry 4.0, the Industrial Internet Consortium and Society 5.0. Morphing an industrial process or assembly line to aggregate Internet-connected devices and systems does not complete the picture. The concept penetrates all aspects of the engineering process (EP) which encompasses the full lifecycle of the product/solution. Phases of the EP traditionally tended to be sequential but, with the IoT, can now evolve and influence other phases throughout the product/solution lifecycle. The EU-funded Arrowhead Tools project aims to promote a service-oriented architecture (SOA) to allow tools within each phase of the engineering process to interact with each other. This paper, applies the proposed EP model to a real value chain composed of multiple stakeholders adopting different EPs for the life-cycle management of a Smart Boiler System

Modeling an Industrial Revolution: How to Manage Large-Scale, Complex IoT Ecosystems?

Advancements around the modern digital industry gave birth to a number of closely interrelated concepts: in the age of the Internet of Things (IoT), System of Systems (SoS), Cyber-Physical Systems (CPS), Digital Twins and the fourth industrial revolution, everything revolves around the issue of designing well-understood, sound and secure complex systems while providing maximum flexibility, autonomy and dynamics.The aim of the paper is to present a concise overview of a comprehensive conceptual framework for integrated modeling and management of industrial IoT architectures, supported by actual evidence from the Arrowhead Tools project; in particular, we adopt a three-dimensional projection of our complex engineering space, from modeling the engineering process to SoS design and deployment.In particular, we start from modeling principles of the the engineering process itself. Then, we present a design-time SoS representation along with a toolchain concept aiding SoS design and deployment. This brings us to reasoning about what potential workflows are thinkable for specifying comprehensive toolchains along with their data exchange interfaces. We also discuss the potential of aligning our vision with RAMI4.0, as well as the utilization perspectives for real-life engineering use-cases

Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension

The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches

An IoT Toolchain Architecture for Planning, Running and Managing a Complete Condition Monitoring Scenario

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases – from their design to their deployment, to their management –, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task (e.g., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. The paper presents the CONTREX European project and its preliminary results. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels. This article presents an overview of the CONTREX European project, its main innovative technology (extension of a model based design approach, functional and extra-functional analysis with executable models and run-time management) and the final results of three industrial use-cases from different domain (avionics, automotive and telecommunication).The work leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2011 under grant agreement no. 611146

From Heterogeneous Sensor Networks to Integrated Software Services: Design and Implementation of a Semantic Architecture for the Internet of Things at ARCES@UNIBO

The Internet of Things (IoTs) is growing fast both in terms of number of devices connected and of complexity of deployments and applications. Several research studies an- alyzing the economical impact of the IoT worldwide identify the interoperability as one of the main boosting factor for its growth, thanks to the possibility to unlock novel commercial opportunities derived from the integration of heterogeneous systems which are currently not interconnected. However, at present, interoperability constitutes a relevant practical issue on any IoT deployments that is composed of sensor platforms mapped on different wireless technologies, network protocols or data formats. The paper addresses such issue, and investigates how to achieve effective data interoperability and data reuse on complex IoT deployments, where multiple users/applications need to consume sensor data produced by heterogeneous sensor networks. We propose a generic three-tier IoT architecture, which decouples the sensor data producers from the sensor data consumers, thanks to the intermediation of a semantic broker which is in charge of translating the sensor data into a shared ontology, and of providing publish-subscribe facilities to the producers/consumers. Then, we describe the real-world implementation of such architecture devised at the Advanced Research Center on Electronic System (ARCES) of the University of Bologna. The actual system collects the data produced by three different sensor networks, integrates them through a SPARQL Event Processing Architecture (SEPA), and supports two front- end applications for the data access, i.e. a web dashboard and an Amazon Alexa voice service

Detection chain and electronic readout of the QUBIC instrument

The Q and U Bolometric Interferometer for Cosmology (QUBIC) Technical Demonstrator (TD) aiming to shows the feasibility of the combination of interferometry and bolometric detection. The electronic readout system is based on an array of 128 NbSi Transition Edge Sensors cooled at 350mK readout with 128 SQUIDs at 1K controlled and amplified by an Application Specific Integrated Circuit at 40K. This readout design allows a 128:1 Time Domain Multiplexing. We report the design and the performance of the detection chain in this paper. The technological demonstrator unwent a campaign of test in the lab. Evaluation of the QUBIC bolometers and readout electronics includes the measurement of I-V curves, time constant and the Noise Equivalent Power. Currently the mean Noise Equivalent Power is ~ 2 x 10⁻¹⁶ W/√Hz