Smart factories are complex environments equipped with both production machinery and computing devices that collect, share, and analyze data. For this reason, the modeling of today's factories can no longer rely on traditional methods, and computer engineering tools, such as SysML, must be employed. At the same time, the current SysML v1. * standard does not provide the rigorousness required to model the complexity and the criticalities of a smart factory. Recently, SysML v2 has been proposed and is about to be released as the new version of the standard. Its release candidate version shows the new version aims at providing a more rigorous and complete modeling language, able to fulfill the requirements of the smart factory domain. In this paper, we explore the capabilities of the new SysML v2 standard by building a rigorous modeling strategy, able to capture the aspects of a smart factory related to the production process, the computation and the communication. We apply the proposed strategy to model a fully-fledged smart factory, and we rely on models to automatically configure the different pieces of equipment and software components in the factory.

The complexity of current manufacturing systems is growing, increasing the complexity of testing and validating control software. Manufacturing software must be extensively tested to minimize errors that may lead to production stops or machine breakdowns. However, testing on real manufacturing systems is often impractical or limited to small portions of the real systems. This paper presents Frost, an open-source platform supporting early manufacturing software validation and testing. Frost is built on top of the Lingua Franca framework, which ensures deterministic execution, enhancing the reliability of software prototyping and testing. The proposed platform implements a set of reactors and classes designed to model the different parts of a manufacturing system, such as sensors, machines, control software, and communication infrastructure. The effectiveness and capabilities of the Frost platform are validated by comparing the overhead introduced and by implementing a digital twin of a real manufacturing system.

With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. The SMART-IC project (DATE'24 MPP category: initial stage) works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data, with different goals: (1) the identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) the forecasting of new data of the future production; and (3) the automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced Manufacturing Execution System (MES), which reacts by optimizing the production process and management of the equipment maintenance policies. SMART-IC is a 300k€ academic project funded by the Italian Ministry of University and supported by STMicroelectronics and Technoprobe with industrial expertise and real-world applications. This paper shares the view of SMART-IC on the future of semiconductor manufacturing, the preliminary efforts, and the future results that will be reached by the end of the project, in 2025.

The DeFacto project, supported by the European Commission via a Marie Skłodowska-Curie Global Individual Fellowship, tackles the complexity arising from the transformation of industrial manufacturing systems into intricate cyber-physical systems. This evolution offers unprecedented opportunities but also poses intellectual and engineering challenges. DeFacto aims to advance the design automation of cyber-physical production systems by developing innovative modeling paradigms, scalable algorithms, software architectures, and tools. In the DeFacto approach, production systems are managed through service-oriented manufacturing software architectures. System-level models capture the features and the requirements of production systems, representing both production and computational processes as services provided by the infrastructure. Methodologies for system analysis and optimization rely on compositional abstractions of system behaviors grounded in assume-guarantee contracts. This paper outlines key research endeavors, findings, and lessons learned from the DeFacto project.

We present ContrArc, a methodology for the exploration of cyber-physical system architectures aiming to minimize a cost function while adhering to a set of heterogeneous constraints. We assume a system topology, defined as a graph, where components (nodes) are selected from an implementation library, and connections between components (edges) are drawn from a finite set of possible connection choices. ContrArc uses assume-guarantee contracts to formalize different viewpoints in the system requirements, such as timing and power consumption, as well as the interface of different components, and translate the exploration problem into a mixed integer linear programming problem. It then searches for efficient solutions by relying on contract decompositions and a method based on sub graph isomorphism to iteratively prune infeasible architectures out of the search space. Experiments on a reconfigurable production line and an aircraft power distribution network show up to two orders of magnitude acceleration in architectural exploration with respect to comparable approaches.

Smart manufacturing systems often use data to optimize production processes. Artificial intelligence algorithms can be used to tune the parameters of production recipes to achieve the desired results. Indeed, such algorithms will require to be trained by using data collected while executing manufacturing operations. However, the required sets of data are rarely available to most production companies. This work-in-progress paper introduces the Print+Mill dataset: a collection of data related to additive and subtractive manufac-turing operations. The data are collected during the execution of various production recipes, utilizing different materials and process parameters. For each recipe, the dataset includes data on the materials and parameters used, sensor readings from the machinery, such as power consumption and temperature, and information on the quality of the resulting product. The data are collected in a complex research facility; in the future, we plan to extend the dataset by considering other manufacturing operations, materials, and types of field data.

The continuous evolution of Industry 4.0 demands advanced solutions for optimizing production processes, reducing energy consumption, and enhancing the capabilities of both human operators and production devices in smart factories. As factory automation software becomes increasingly complex, ensuring its robustness through extensive testing is critical. However, testing on actual production systems is often impractical due to the critical nature of the software.This paper explores the use of digital twins to simulate factory environments, enabling thorough validation of automation software. We propose leveraging Lingua Franca and the Functional Mock-up Interface (FMI) standard to create comprehensive digital twins. Lingua Franca ensures deterministic execution in simulations, while FMI facilitates the integration of diverse simulators for accurate modeling of heterogeneous systems. We introduce an interfacing method to integrate FMI components within Lingua Franca and present a strategy for modeling software-machinery interactions using the OPC Unified Architecture (OPC UA) protocol. The methodology is applied to develop a digital twin of a logistics subsystem in a manufacturing system, demonstrating the effectiveness of the simulation environment through various scenarios.

This article introduces the regionalized resource process dependence graphs (RRPDGs): a manufacturing processes representation inspired by the regionalized value state dependence graphs traditionally used in software compilers. An RRPDG is an ordered sequence of nodes, each characterized by stereotyped input and output parameters, encapsulating a transformation of the process state (e.g., a manufacturing operation). RRPDG allow defining complex transformations by composing a set of nodes (i.e., regions), hiding the internal details. Then, RRPDGs are used to automatically reasoning over dynamic reconfiguration and process optimization: an instance of the A-star search algorithm is used to search for possible transformations while pursuing an optimization function. The rules defined in this article over RRPDG models enforce the transformations' correctness. We use RRPDGs to model a real production system while the transformation rules are applied to optimize the system's processes. The proposed representation reduced the search complexity in each experiment, allowing to reach an optimal solution also in the case for which classical approaches were unable to complete before reaching the timeout. In all the experiments, the cost of the solution produced by using the regionalized representation is minor than the the solution produced by using the classical representation.

We address the warehouse servicing problem (WSP) in automated warehouses, which use teams of mobile agents to bring products from shelves to packing stations. Given a list of products, the WSP amounts to finding a plan for a team of agents which brings every product on the list to a station within a given timeframe. The WSP consists of four subproblems, concerning what tasks to perform (task formulation), who will perform them (task allocation), and when (scheduling) and how (path planning) to perform them. These subproblems are NP-hard individually and are made more challenging by their interdependence. The difficulty of the WSP is compounded by the scale of automated warehouses, which frequently use teams of hundreds of agents. In this paper, we present a methodology that can solve the WSP at such scales. We introduce a novel, contract-based design framework which decomposes an automated warehouse into traffic system components. By assigning each of these components a contract describing the traffic flows it can support, we can syn-thesize a traffic flow satisfying a given WSP instance. Component-wise search-based path planning is then used to transform this traffic flow into a plan for discrete agents in a modular way. Evaluation shows that this methodology can solve WSP instances on real automated warehouses.

Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. Deep neural networks (DNNs) play an important role in this scenario, furnishing powerful decision mechanisms, at the price of a high computational effort. Consequently, powerful state-of-the-art DNNs are frequently split over various computational nodes, e.g., a first part stays on an embedded device and the rest on a server. Deciding where to split a DNN is a challenge in itself, making the design of deep learning applications even more complicated. Therefore, we propose Split-Et-Impera, a novel and practical framework that i) determines the set of the best-split points of a neural network based on deep network interpretability principles without performing a tedious try-and-test approach, ii) performs a communication-aware simulation for the rapid evaluation of different neural network rearrangements, and iii) suggests the best match between the quality of service requirements of the application and the performance in terms of accuracy and latency time.

We address the Warehouse Servicing Problem (WSP) in automated warehouses, which use teams of mobile robots to move products from shelves to packaging stations. Given a list of products, the WSP amounts to finding a motion plan which brings every product on the list from a shelf to a packaging station within a given time limit. The WSP consists of four subproblems, namely, deciding where to source and deposit a product (task formulation), who should transport each product (task assignment) and when (scheduling) and how (motion planning). These problems are NP-Hard individually and made more challenging by their interdependence. The difficulty of the WSP is compounded by the scale of automated warehouses, which use teams of hundreds of agents to transport thousands of products. In this paper, we present Contract-based Cyclic Motion Planning (CCMP), a novel contract-based methodology for solving the WSP at scale. CCMP decomposes a warehouse into a set of traffic system components. By assigning each component a contract which describes the traffic flows it can support, CCMP can generate a traffic flow which satisfies a given WSP instance. CCMP then uses a novel motion planner to transform this traffic flow into a motion plan for a team of robots. Evaluation shows that CCMP can solve WSP instances taken from real industrial scenarios with up to 1 million products while outperforming other methodologies for solving the WSP by up to 2.9×.

This paper presents a software architecture extending the classical automation pyramid to control and reconfigure flexible, service-oriented manufacturing systems. At the Planning level, the architecture requires a Manufacturing Execution System (MES) consistent with the International Society of Automation (ISA) standard. Then, the Supervisory level is automated by introducing a novel component, called Automation Manager. The new component interacts upward with the MES, and downward with a set of servers providing access to the manufacturing machines. The communication with machines relies on the OPC Unified Architecture (OPC UA) standard protocol, which allows exposing production tasks as “services”. The proposed software architecture has been prototyped to control a real production line, originally controlled by a commercial MES, unable to fully exploit the flexibility provided by the case study manufacturing system. Meanwhile, the proposed architecture is fully exploiting the production line's flexibility.

This paper proposes an automated framework for quantitative verification and design space exploration of cyber-physical systems in the presence of uncertainty, leveraging assume-guarantee contracts expressed in Stochastic Signal Temporal Logic (StSTL). We introduce quantitative semantics for StSTL and formulations of the quantitative verification and design space exploration problems as bi-level optimization problems. We show that these optimization problems can be effectively solved for a class of stochastic systems and a fragment of bounded-time StSTL formulas. Our algorithm searches for partitions of the upper-level design space such that the solutions of the lower-level problems satisfy the upper-level constraints. A set of optimal parameter values are then selected within these partitions. We illustrate the effectiveness of our framework on the design of a multi-sensor perception system and an automatic cruise control system.

Manufacturing lines are evolving into complex cyber-physical production systems. However, their growth in complexity is not matched by the development of structured modeling and design methodologies. In particular, approaches exploiting both models typical of the manufacturing domain and models used by computer engineers are still missing. In this work, we outline a design flow contemplating the reuse of already existing manufacturing lines' models, while designing novel advanced production systems. To enable such a flow, we propose a methodology extracting System Modeling Language (SysML) structural diagrams from AutomationML descriptions. Then, we propose to design the system functionalities on top of the produced diagrams. The paper shows the application of the methodology to a concrete manufacturing line, the structure of which was originally modeled using AutomationML. To exemplify the advantages of the methodology, we exploit the models being generated to automatically extract a digital twin for the production system transportation line. The resulting digital twin is compliant with a well-known plant simulation tool.

The Industry 4.0 trend speeds up the adoption of a variety of technologies. In modern manufacturing, system data are collected both from the field through sensors and by exploiting complex simulations. Data analysis techniques became crucial to build and maintain any efficient production line, while autonomous systems and robots are the main focus of researchers and practitioners. This pervasive use of artificial intelligence derived technologies pushed humans to the border of production systems. Industry 5.0 aims at bringing the attention back to humans in production lines while magnifying their interactions with intelligent systems. This new trend will impact the design of future manufacturing infrastructures, increasing their complexity. Engineers will need modeling and developing tools able to capture this complexity. In this paper, we analyze the modeling languages and tools being used, identifying their strengths and weaknesses. Then, we propose some possible directions to provide engineers with the expressive power needed to tackle the challenges posed by Industry 5.0.

This article presents a methodology to formalize the behavior of the machines composing a production line, and to automatically generate their virtual prototypes for efficient and correct plant simulation. The approach exploits assume-guarantee reasoning through contracts to model the interaction between the different components of a production line. The approach is guided by a well-known taxonomy of industrial machines and associated manufacturing processes to identify each elementary action related to a specific machine. Contracts enable to build executable models of all the machines available in the production line by using automatic synthesis. The generated models can be integrated into a state-of-the-practice industrial plant simulation software to estimate and validate the production line's behavior. The presentation of the methodology is supported by a running example based on a real production line, showing the step-by-step application of the approach to a concrete scenario.

The advent of Industry 4.0 is making production processes every day more complicated. As such, early process validation is becoming crucial to avoid production errors thus decreasing costs. In this paper, we present an approach to validate production recipes. Initially, the recipe is specified according to the ISA-95 standard, while the production plant is described using AutomationML. These specifications are formalized into a hierarchy of assume-guarantee contracts. Each contract specifies a set of temporal behaviors, characterizing the different machines composing the production line, their actions and interaction. Then, the formal specifications provided by the contracts are systematically synthesized to automatically generate a digital twin for the production line. Finally, the digital twin is used to evaluate, and validate, both the functional and the extra-functional characteristics of the system.The methodology has been applied to validate the production of a product requiring additive manufacturing, robotic assembling and transportation.

We present LKRDet: a framework based on a Trusted Execution Environment to detect Kernel rootkits in IoT devices. LKRDet checks the consistency of hardware events, occurring in specific system call routines, to detect abnormalities caused by the kernel rootkits. LKRDet relies on Hardware Performance Counters to efficiently and safely count the hardware events occurring in the system. We implement a prototype of LKRDet for the ARM TrustZone architecture, on top of the Open Portable Trusted Execution Environment and evaluate our prototype with four popular rootkits. Our evaluation reveals that LKRDet can accurately detect the presence of all the rootkits in the device.

Pervasive computing requires to build systems every day more complex and heterogeneous. Smart devices must be able to carry on sensing and actuation alongside with computation and communication. As such, many different technologies must be packed within the same object. Digital HW and SW coexist with analog components and Micro-Electro-Mechanical systems capable of sensing and controlling the physical environment. For this reason, the design of such devices must rely on the integration of many different descriptions belonging to different design domains. The high-level of heterogeneity involved in the modeling phase of the system development makes harder the validation of the system functionality, since holistic system simulation would require the integration of many different simulators. In this article, we propose a set of automatic abstraction techniques for multi-disciplines analog components. Then, we define a scheduling strategy to integrate the execution of continuous-time analog sub-components with automatically abstracted models of the digital HW parts of the system. As a final result, the proposed methodology produces a C++ virtual platform providing a holistic simulation of complex and heterogeneous devices.

The revolutionary development of the Internet of Things has triggered a huge demand for Internet of Things devices. They are extensively applied to various fields of social activities, and concerning manufacturing, they are a key enabling concept for the Industry 4.0 ecosystem. Industrial Internet of Things (IIoT) devices share common vulnerabilities with standard IoT devices, which are increasingly exposed to the attackers. As such, connected industrial devices may become sources of cyber, as well as physical, threats for people and assets in industrial environments. In this work, we examine the attack surfaces of a networked embedded system, composed of devices representative of those typically used in the IIoT field. We carry on an analysis of the current state of the security of IIoT technologies. The analysis guides the identification of a set of attack vectors for the examined networked embedded system. We set up the corresponding concrete attack scenarios to gain control of the system actuators and perform some hazardous operations. In particular, we propose a couple of variations of Mirai attack specifically tailored for attacking industrial environments. Finally, we discuss some possible

This paper analyzes a set of languages and standard used when designing industrial plants. It focuses on AutomationML and B2MML to specify respectively the architecture and the intended production of the system being designed. It also relies on the DIN 8580 standard to describe the actions performed by each machine composing the production line. Then, it outlines a methodology starting by mapping the information expressed by the analyzed languages and standards into the Assume-Guarantee Contracts formalism. It exploits contract-based design concepts to tackle the increase automation of the industrial plant design process and to enable the generation of digital twins. The approach is outlined by showing its applicability to a concrete manufacturing scenario.

The approach we present in this paper exploits assume-guarantee reasoning through contracts to model a production line, and to generate its virtual prototype for efficient and correct plant simulation. Contracts are used to model the different parts composing the line; the modeling is guided by a well-known taxonomy associating industrial machines to manufacturing processes and their elementary actions, each represented by a contract. The composition of contracts representing the actions of a machine specifies each possible manufacturing process implemented by the machine. Then, automatic synthesis from contracts is used to generate an executable model of the machines composing the plant. The generated models are finally integrated into a state-of-the-practice industrial plant simulation software to validate the execution of the production line.The entire methodology is presented by showing its step-by-step application to a concrete scenario.

Virtual platforms are a powerful support for the development and early validation of embedded SW. However, complex smart devices are built by aggregating heterogeneous components provided by different vendors, thus requiring the development of custom ad-hoc virtual platforms. Even worse, components of the underneath HW platform may belong to different design domains, that are usually expressed using a huge variety of different languages. This high degree of heterogeneity in terms of both design and specification languages must be effectively managed by the design flow so to help engineers in assembling the final system. This paper proposes a meet-in-the-middle approach to create virtual platforms of heterogeneous systems. The starting point is a set of heterogeneous models, developed by adopting the designer's favorite language and formalism. The methodology envisions the adoption of existing automatic translation and abstraction tools to automatically integrate models of components into a single homogeneous system-level executable description. The approach is supported by an analysis of the typical design flow, that leads to the definition of design domain/abstraction level taxonomies. Such taxonomies are then used to identify what characteristics would allow efficient system-level simulation, and the corresponding transformations to be applied to the starting models to achieve a “holistic” system executable representation. The benefit of such an approach is particularly evident on any kind of highly heterogeneous systems, such as smart devices. The proposed methodology has been applied to two case studies with different degrees of heterogeneity, with the goal exemplifying its adoption on concrete scenarios and to prove its effectiveness.

This paper presents a methodology that relies on Assume-Guarantee Contracts to decompose the problem of synthesizing control software for a multi-robot system. Initially, each contract describes either a component ( e.g. , a robot) or an aspect of the system. Then, the design problem is decomposed into different synthesis and verification sub-problems, allowing to tackle the complexity involved in the design process. The design problem is then recomposed by exploiting the rigorousness provided by contracts. This allows us to achieve system-level simulation capable to be used for validating the entire design. Once validated, the software synthesized during the process can be integrated into Robot Operating System (ROS) nodes and executed using state-of-the-practice packages and tools for modern robotic systems. We apply the methodology to generate a control strategy for an autonomous goods transportation system. Our results show a massive reduction of the time required to obtain automatically the control software implementing a multi-robot mission.

Development of cyber-physical systems' control algorithms usually relies on architecture-agnostic abstract models, often leading to ineffective implementations. This paper presents a technique to automatically integrate cycle-accurate models of digital HW components with continuous-time physical models. It proposes a solution to the semantic gap between the involved models of computation. Furthermore, model generation and integration for both Simulink-based proprietary environment and FMI-based portable standard are presented. The aim of such techniques is to produce cyber-physical virtual-platforms: a powerful tool to refine control algorithms up to their SW implementations on the actual HW platform.

This paper presents CHASE, a framework for requirement capture, formalization, and validation for cyber-physical systems. CHASE combines a practical front-end formal specification language based on patterns with a rigorous verification back-end based on assume-guarantee contracts. The front-end language can express temporal properties of networks using a declarative style, and supports automatic translation from natural-language constructs to low-level mathematical languages. The verification back-end leverages the mathematical formalism of contracts to reason about system requirements and determine inconsistencies and dependencies between them. CHASE features a modular and extensible software infrastructure that can support different domain-specific languages, modeling formalisms, and analysis tools. We illustrate its effectiveness on industrial design examples, including control of aircraft power distribution networks and arbitration of a mixed-criticality automotive bus.

The modelling process of Cyber-physical Systems aggregates semantics and languages tailored to different specific domains. The simulation of these complex systems involves different tools and their coupling requires computational effort. In the last few years both Academy and Industry worked toward the definition of standard interfaces able to overcome such issues. The Functional Mock-up Interface (FMI) standard emerged as one of the most promising tool to easily export and integrate heterogeneous models. However, the standard still shows some weaknesses, particularly when dealing with Functional Mock-up Units (FMUs) describing discrete-event systems. This paper explore the features of the standard to find its shortcomings when dealing with discrete models. Then, it proposes a systematic approach to fully exploit the features of the current standards to overcome such limitations. The solution is based on two concepts: (1) exposing the internal time of the FMUs, and (2) exploits the newly exposed information to implement temporal decoupling. The combination of these two concepts allows to optimize the FMUs coordination algorithms. It reduces the number synchronization points and move the simulation from cycle-accurate to transaction-accurate. The impact of these optimizations is measured on a set of benchmarks having different tread-offs of computation and control.

Analog components are fundamental blocks of smart systems, as they allow a tight interaction with the environment, in terms of both sensing/actuation and communication. This impacts on the design of the overall system, and mainly on the validation phase, that thus requires the joint simulation of digital and analog aspects. In this scenario, this paper proposes the automatic conversion of analog models to C++-based languages, to remove the overhead of co-simulation with traditional virtual platform tools. The proposed methodology allows to convert a given analog description to either: 1) a fully equivalent description or 2) an abstract representation for faster simulation which models only the aspects of interest. Effectiveness and correctness have been proved on a number of case studies that highlight the effectiveness and potentiality of the proposed methodology.

Modern smart systems are usually built by implementing SW functionalities executed on HW platforms composed of both digital and analog components. Validation is mainly implemented through simulation of the functional behavior of the entire smart system modeled by a Virtual Platform. It is thus crucial to achieve fast mixed-signal simulation by removing unnecessary overhead due to synchronization between multiple tools and unimportant details. This work proposes a methodology to abstract mixed-signal systems, by integrating digital and analog components in a homogeneous virtual platform model for efficient simulation. Two main contributions are provided: 1) an automatic abstraction technique for analog components, allowing to preserve only the details meaningful for the functional behavior of the entire platform by moving complexity from simulation to generation time and 2) a novel scheduling technique that exploits temporal decoupling and synchronization of digital and analog processes, to simulate them together in a homogeneous model.

Multi-discipline components introduce problems when inserted within virtual platforms of Smart Systems for functional validation. This paper lists the most common emerging problems and it proposes a set of solutions to them. It presents a set of techniques, unified in an automatic abstraction methodology, useful to achieve fast analog mixed-signal simulation even when different physical disciplines and modeling styles are combined into a single analog model. The paper makes use of a complex case study. It deals with multiple-discipline descriptions, non-electrical conservative models, non-linear equation systems, and mixed time/frequency domain models. The original component behavior has been modeled in Verilog-AMS by using electrical, mechanical and kinematic equations. Then, it has been abstracted and integrated within a virtual platform of a mixed-signal smart system for efficient functional simulation.

Simulation of accurate HW models is usually required to verify Embedded SW. However, heterogeneous system simulators do not easily allow it and designers must connect multiple simulators in complex co-simulation environments. This paper proposes the automatic generation of cycle-accurate Simulink blocks from the two most popular HW description languages: VHDL and Verilog. The methodology starts from an IP modeled in one of the two supported HW description languages. Then, it relies on state-of-the-art RTL models abstraction procedure to produce a functionally equivalent cycle-accurate model of the IP. Then, it proposes two alternative mapping and code-generation techniques. The first one relies on the portable FMI standard, while the other one exploits Mathworks' proprietary C MEX S-Functions. These blocks can be easily integrated within Simulink to simulate digital HW components while avoiding to build complex and computationally demanding co-simulation frameworks: a valuable feature when developing complex heterogeneous systems. A set of RTL IPs are used to compare the proposed approach to state-of-the-art co-simulation techniques. Furthermore, the experiments presented in this paper compares the two proposed alternatives to highlight their advantages and drawbacks.

Smart systems are miniaturized devices integrating computation, communication, sensing and actuation. As such, their design can not focus solely on functional behavior, but it must rather take into account different extra-functional concerns, such as power consumption or reliability. Any smart system can thus be modeled through a number of views, each focusing on a specific concern. Such views may exchange information, and they must thus be simulated simultaneously to reproduce mutual influence of the corresponding concerns. This paper shows how the IP-XACT standard, with some necessary extensions, can effectively support this simultaneous simulation. The extended IP-XACT descriptions allow to model extra-functional properties with a homogeneous format, defined by analysing requirements and characteristic of three main concerns, i.e., power, temperature and reliability. The IP-XACT descriptions are then used to automatically generate a skeleton of the simulation infrastructure in SystemC. The skeleton can be easily populated with models available in the literature, thus reaching simultaneous simulation of multiple concerns.

SystemC has recently been extended with the Analogue and Mixed Signal (AMS) library, with the ultimate goal of providing simulation support to analogue electronics and continuous time behaviours. SystemC-AMS allows modelling of systems that are either conservative and extremely low level or continuous time and behavioural, which is limited compared to other AMS HDLs. This work faces up this challenge, by extending SystemCAMS support to a new level of abstraction, called Analogue Behavioural Modelling (ABM), covering models that are both behavioural and conservative. This leads to a methodology that uses SystemC-AMS constructs in a novel way. Full automation of the methodology allows proof of its effectiveness both in terms of accuracy and simulation performance, and application of the overall approach to a complex industrial Micro Electro- Mechanical System (MEMS) case study. The effectiveness of the proposed approach is further highlighted in the context of virtual platforms for smart systems, as adopting a C++-based language for MEMS simulation reduces the simulation time by about 2x, thus enhancing the design and integration flow.

Several embedded system applications are used to control physical processes. Sensing, computation and actuation are combined thus involving a set of highly heterogeneous components, i.e., digital and analog hardware, software, energy sources, and external environment. Moreover, the growing use of networks contributes to introduce a further level of heterogeneity. All these aspects should be taken into account in the design process to find highly optimized solutions, therefore a Cyber-Physical System approach is needed. In particular, simulation is a key technique in the different design stages. However, the heterogeneity of components, together with the presence of the network, forces to adopt complex and slow co-simulation techniques to carry on the simulation of the entire system. This work aims at proposing SystemC as unified framework to model and simulate Networked Cyber-Physical Systems. Concerning the modeling of continuous-time components and a specific class of discrete-time components, the different Models of Computation provided by the Analog/Mixed-Signal (AMS) extension of SystemC are used. Regarding the network, SystemC and the SystemC Network Simulation Library are used to model communications at different abstraction levels. The accuracy and speed of different simulation alternatives are compared by the application to a networked control system.

The design of wireless embedded systems needs their efficient and realistic simulation to verify that requirements are met. The reproduction of communication behavior is crucial to assess the performance of hardware and software components, e.g., dependability and energy consumption. This work presents and discusses different levels of abstraction for the simulation of the communication behavior. Each level addresses different aspects of the communication and allows to specify different kinds of detail; furthermore, it requires a specific modeling approach. Their use and the corresponding computational overhead is shown in the specific case of the Bluetooth standard. We also show that simulation results obtained at a low abstraction level can be used at a higher one to drive design choices.

We present an approach for optimization of assertion placement in time-constrained HW/SW modules for detection of errors due to transient and intermittent faults. During the design phases, these assertions have to be inserted into the executable code and, hence, will always be executed with the corresponding code branches. As the result, they can significantly increase execution time of a module, in particular, contributing to a much longer execution of the worst case, and cause deadline misses. Assertions have different characteristics such as tightness (or "local error coverage") and execution latency. Taking into account these properties can increase efficiency of assertion checks in time-constrained embedded HW/SW modules. We have developed a design optimization framework, which (1) identifies candidate locations for assertions, (2) associates a candidate assertion to each location, and (3) selects a set of assertions in terms of performance degradation and assertion tightness. Experimental results have shown the efficiency of the proposed techniques.