Quality, Reliability, Safety and Cyber-Security

Modern technologies and new digitised services are key to ensuring the stable growth and development of the European Union and its society. These new technologies are largely based on smart electronic components and systems (ECS). Highly automated or autonomous transportation systems, improved healthcare, industrial production, information and communication networks, and energy grids all depend on the availability of electronic systems. The main societal functions145 and critical infrastructure are governed by the efficient accessibility of smart systems and the uninterrupted availability of services.

Ensuring the reliability, safety and security of ECS is a Major Challenge since the simultaneous demand for increased functionality and continuous miniaturisation of electronic components and systems causes interactions on multiple levels. This Chapter addresses these complex interdependencies by considering input from, and necessary interaction between, major disciplines. The quality, reliability, safety and cybersecurity of electronic components and systems are, and will be, fundamental to digitised society (see Figure F.42). In addition, the tremendous increase of computational power and reduced communication latency of components and systems, coupled with hybrid and distributed architectures, impose to rethink many “traditional” approaches and expected performances towards safety and security, exploiting AI and ML.

In practice, ensuring reliability, safety, and security of ECS is part of the Design, Implementation, and Validation/Testing process of the respective manufacturers and – for reasons of complexity and diversity/heterogeneity of the systems – must be supported by (analysing and testing) tools. Thus, the techniques described in Chapter 2.3 (Architecture and Design: Method and Tools) are complementary to the techniques presented here: in that Chapter, corresponding challenges are described from the design process viewpoint, whereas here we focus on a detailed description of the challenges concerning reliability, safety, and security within the levels of the design hierarchy.

F.42 - Role of quality, reliability, safety and cybersecurity of electronic components and systems for digitalisation.

“The role of the technology is to allow persons to express their potential”. Hans Rosling, in his book Factfulness: Ten Reasons We're Wrong About the World – and Why Things Are Better Than You Think, plots the life quality of the world’s population in groups at successive levels. He shows how such groups, even those at the bottom level, will move forward over time to the next level. Technology can help accelerate that progression. An emblematic example of that is the project launched by Facebook and the Internet Society (ISOC) to develop internet exchange points (IXPs) throughout Africa. Albeit not without difficulty, IXPs help promote e-learning to improve education in the continent, and for connected drones to deliver medicines and other products to remote populations.

The recent Covid-19 pandemic has emphasised the importance of digital technology to the western world, with the recourse to robots in several hazardous situations, from disinfecting airplanes and hospital rooms, to delivering medication to isolated patients. Digital technology that can fit these diverse needs should address holistically concerns such as quality of service, reliability, safety, trustworthy, privacy, cybersecurity and human–system integration. A degraded behaviour in any of these dimensions, or an incorrect integration among them, would affect vital properties and could cause serious damage. In addition, such shortcomings in safety, reliability and security might even outweigh the societal and individual benefits perceived by users, thus lowering trust in, and acceptance of, the technologies. All these topics and features constitute the core of this Chapter.

Europe is internationally known for its high-quality product standards, which enjoy a strong international reputation. The European Union (EU) has a robust and reliable safety and product liability regulatory framework, and a rigorous body of safety standards, complemented by national, non-harmonised liability regulations. In the past, this has been a big success for European embedded systems in almost all industries, including automotive, telecommunications, manufacturing, railway, avionic and military defence, to name but a few of the many sectors where people rely on them.

However, in light of the two main drivers of digitalisation and connectivity, Europe is highly dependent on the supply of hardware and software from countries outside of Europe. Dominating market players in the information and communications technology (ICT) sector – such as those in the expanding sectors of social networks, logistic and e-commerce are expanding their products towards industrial domains. In addition, recent revelations regarding espionage and state-sponsored surveillance have initiated a debate on the protection of core EU values such as security, privacy, data protection and trust. Therefore, digital strategic autonomy – the ability of the EU to maintain a high level of control and security of its products, responding quickly if potential vulnerabilities are noticed – is of utmost importance. A strategic advantage can be achieved by designing reliable, safe and secure products where the dependencies to foreign products are transparently considered. A difference for EU products can also be achieved by treating privacy and necessary human interaction with its own set of independent standards, where technology will keep its limits according to European values when interacting with citizens.

To introduce the topic presented in this Chapter, we first present some definitions that will be useful to clarify the concepts described in the Major Challenges.

  • Production quality: often defined as “the ability of a system being suitable for its intended purpose while satisfying customer expectations”, this is a very broad definition that basically includes everything. Another widely used definition is “the degree a product meets requirements in specifications” – but without defining the underlying specifications, the interpretation can vary a lot between different stakeholders. Therefore, in this Chapter quality will be defined “as the degree to which a product meets requirements in specifications that regulate how the product should be designed and manufactured, including environmental stress screening (such as burn-in) but no other type of testing”. In this way, reliability, dependability and cybersecurity, which for some would be expected to be included under quality, will be treated separately.
  • Reliability: this is the ability or the probability, respectively, of a system or component to function as specified under stated conditions for a specified time.
  • Prognostics and health management: a method that permits the assessment of the reliability of the product (or system) under its application conditions.
  • Functional safety: the ability of a system or piece of equipment to control recognized hazards to achieve an acceptable level of risk, such as to maintain the required minimum level of operation even in the case of likely operator errors, hardware failures and environmental changes to prevent physical injuries or damages to the health of people, either directly or indirectly.
  • Dependability: according to IEC 60050-192:2015, dependability (192-01-22) is the ability of an item to perform as and when required. An item here (192-01-01) can be an individual part, component, device, functional unit, equipment, subsystem or system. Dependability includes availability (192-01-23), reliability (192-01-24), recoverability (192-01-25), maintainability (192-01- 27) and maintenance support performance (192-01-29), and in some cases other characteristics such as durability (192-01-21), safety and security. A more extensive description of dependability is available from the IEC technical committee on dependability (IEC TC 56).
  • Safety: freedom from unacceptable risk of harm [CENELEC 50126].
  • Security: measures can provide controls relating to physical security (control of physical access to computing assets) or logical security (capability to login to a given system and application) (IEC 62443-1-1):
    • measures taken to protect a system;
    • condition of a system that results from the establishment and maintenance of measures to protect the system;
    • condition of system resources being free from unauthorized access, and from unauthorized or accidental change, destruction or loss;
    • capability of a computer-based system to provide adequate confidence that unauthorized persons and systems can neither modify the software and its data nor gain access to the system functions, and yet ensure that this is not denied to authorized persons and systems;
    • prevention of illegal or unwanted penetration of, or interference with, the proper and intended operation of an industrial automation and control system.
  • Cybersecurity: the protection of information against unauthorized disclosure, transfer, modification or destruction, whether accidental or intentional (IEC 62351-2).
  • Robust root of trust systems: these are based on cryptographic functionalities that ensure the authenticity and integrity of the hardware and software components of the system, with assurance that it is resilient to logical and physical attacks.
  • Emulation and Forecasting: cybersecurity evolution in parallel to increasing computation power and hybrid threats mixing geopolitical, climate change and any other external threats impose to anticipate the horizon of resilience, safety and security of systems forecasting attacks and incidents fast evolution.

Five Major Challenges have been identified:

  • Major Challenge 1: Ensuring HW quality and reliability.
  • Major Challenge 2: Ensuring dependability in connected software.
  • Major Challenge 3: Ensuring cyber-security and privacy.
  • Major Challenge 4: Ensuring of safety and resilience.
  • Major Challenge 5: Human systems integration.       Major challenge 1: Ensuring HW quality and reliability

With the ever-increasing complexity and demand for higher functionality of electronics, while at the same time meeting the demands of cutting costs, lower levels of power consumption and miniaturization in integration, hardware development cannot be decoupled from software development. Specifically, when assuring reliability, separate hardware development and testing according to the second-generation reliability methodology (design for reliability, DfR) is not sufficient to ensure the reliable function of the ECS. A third-generation reliability methodology must be introduced to meet these challenges. For the electronic smart systems used in future highly automated and autonomous systems, a next generation of reliability is therefore required. This new generation of reliability assessment will introduce in situ monitoring of the state of health on both a local (e.g. IC packaging) and system level. Hybrid prognostic and health management (PHM) supported by Artificial Intelligence (AI) is the key methodology here. This marks the main difference between the second and the third generation. DfR concerns the total lifetime of a full population of systems under anticipated service conditions and its statistical characterization. PHM, on the other hand, considers the degradation of the individual system in its actual service conditions and the estimation of its specific remaining useful life (RUL).

Since embedded systems control so many processes, the increased complexity by itself is a reliability challenge. Growing complexity makes it more difficult to foresee all dependencies during design. It is impossible to test all variations, and user interfaces need greater scrutiny since they have to handle such complexity without confusing the user or generating uncertainties.

The trend towards interconnected, highly automated and autonomous systems will change the way we own products. Instead of buying commodity products, we will instead purchase personalized services. The vision of Major challenge 1 is to provide the requisite tools and methods for novel ECS solutions to meet ever- increasing product requirements and provide availability of ECS during use in the field. Therefore, availability will be the major feature of ECS. Both the continuous improvement of existing methods (e.g. DfR) and development of the new techniques (PHM) will be the cornerstone of future developments in ECS (see also Challenges 1 and 2, and especially the key focus areas on lifecycle-aware holistic design flows in Chapter 2.3 Architecture and Design: Methods and Tools). The main focus of Major challenge 1 will circulate around the following topics.

  • Digitization, by improving collaboration within the supply chain to introduce complex ECS earlier in the market.
  • Continuous improvement of the DfR methodology through simultaneous miniaturization and increasing complexity.
  • Model-based design is a main driver of decreasing time-to-market and reducing the cost of products.
  • Availability of the ECS for highly automated and autonomous systems will be successfully introduced in the market based on PHM.
  • Data science and AI will drive technology development and pave the way for PHM implementation for ECS.
  • AI and PHM based risk management.

Quality: In situ and real-time assessments

Inline inspection and highly accelerated testing methods for quality and robustness monitoring during production of ECS with ever-increasing complexity and heterogeneity for demanding applications should increase the yield and reduce the rate of early fails (failures immediately following the start of the use period).

  • Controlling, beyond traditional approaches, the process parameters in the era of Industry 4.0 to minimize deviations and improve quality of key performance indicators (KPIs).
  • Process and materials variabilities will have to be characterized to quantify their effects on hardware reliability, using a combination of empirical studies, fundamental RP models and AI approaches.
  • Advanced/smart monitoring of process output (e.g. measuring the 3D profile of assembled goods) for the detection of abnormities (using AI for the early detection of standard outputs).
  • Early detection of potential yield/reliability issues by simulation-assisted design for assembly/design for manufacturing (DfM/DfA) as a part of virtual prototyping.

Digitization: A paradigm shift in the fabrication of ECS from supplier/customer to partnership

Digitization is not possible without processing and exchange data between partners.

  • Involving European stakeholders to resolve the issue of data ownership:
    • Create best practices and scalable workflows for sharing data across the supply chain while maintaining intellectual property (IP).
    • Standardize the data exchange format, procedures and ownership, and create an international legal framework.
    • Conceive and validate business models creating economic incentives and facilitating sharing data, and machine learning algorithms dealing with data.
  • Handling and interpreting big data:
    • Realise consistent data collection and ground truth generation via annotation/labelling of relevant events.
    • Create and validate a usable and time-efficient workflow for supervised learning.
    • Standardized model training and model testing process.
    • Standardized procedures for model maintenance and upgrade.
  • Make a link between data from Industry 4.0 and model-based engineering:
    • Derive working hypotheses about system health.
    • Validate hypothesis and refine physics-based models.
    • Construct data models-based embedding (new) domain knowledge derived from model-based engineering.
  • Identify significant parameters that must be saved during production to be re-used later for field-related events, and vice versa – i.e. feed important insights derived from field data (product usage monitoring) into design and production. This is also mandatory to comply with data protection laws.
  • Evaluate methods for the indirect characterization of ECS using end-of-line test data.
  • Wafer fabrication (pre-assembly) inline and offline tests for electronics, sensors and actuators, and complex hardware (e.g. multicore, graphics processing unit, GPU) that also cover interaction effects such as heterogeneous 3D integration and packaging approaches for advanced technologies nodes (e.g. thin dice for power application – dicing and grinding).

Reliability: Tests and modelling

Continuous improvement of physics of failure (PoF) based methodologies combined with new data-driven approaches: tests, analyses and degradation, and lifetime models (including their possible reconfiguration):

  • Identifying and adapting methodology to the main technology drivers.
  • Methods and equipment for dedicated third-level reliability assessments (first level: component; second level: board; third level: system with its housing, e.g. massive metal box), as well as accounting for the interactions between the hierarchy levels (element, device, component, sub-module, module, system, application).
  • Comprehensive understanding of failure mechanisms, lifetime prediction models (including multi-loading conditions), continuously updating for new failure mechanisms related to innovative technologies (advanced complementary metal–oxide–semiconductor (CMOS), µ-fluidics, optical input/output (I/O), 3D printing, wide bandgap technologies, etc.).New materials and production processes (e.g. 3D printing, wide bandgap technologies, etc.), and new interdisciplinary system approaches and system integration technologies (e.g. µ-fluidics, optical input/output (I/O), etc.).
  • Accelerated testing methods (e.g. high temperature, high power applications) based on mission profiles and failure data (from field use and tests):
    • Use field data to derive hypotheses that enable improved prioritization and design of testing.
    • Usage of field, PHM and test data to build models for ECS working at the limit of the technology as accelerating testing is limited.
  • Standardize the format of mission profiles and the procedure on how mission profiles are deducted from multimodal loading.
  • Design to field – better understanding of field conditions through standardized methodology over supply chain using field load simulator.
  • Understanding and handling of new, unforeseen and unintended use conditions for automated and autonomous systems.
  • Embedded reliability monitoring (pre-warning of deterioration) with intelligent feedback towards autonomous system(s).
  • Identification of the 10 most relevant field-related failure modes based on integrated mission profile sensors.
  • Methods to screen out weak components with machine learning (ML) based on a combination of many measured parameters or built-in sensor data.
  • New standards/methodologies/paradigms that evaluate the “ultimate” strength of systems – i.e. no longer test whether a certain number of cycles are “pass”, but go for the limit to identify the actual safety margin of systems, and additionally the behavior of damaged systems, so that AI can search for these damage patterns.
  • Digital twin software development for reliability analysis of assets/machines, etc.
  • Comprehensive understanding of the SW influence on HW reliability and its interaction:
    • SW Rel: start using maturity growth modelling techniques, develop models and gather model parameters.
    • SW/HW Rel modelling: find ways as to combine the modelling techniques (in other words: scrunch the different time domains).
    • SW/HW Rel testing: find ways as to test systems with software and find the interaction failure modes.

Design for reliability: Virtual reliability assessment prior to the fabrication of physical HW

Approaches for exchanging digital twin models along the supply chain while protecting sensitive partner IP and adaptation of novel standard reliability procedures across the supply chain.

  • Digital twin as main driver of robust ECS system:
    • Identifying main technology enablers.
    • Development of infrastructure required for safe and secure information flow.
    • Development of compact PoF models at the component and system level that can be executed in situ at the system level – metamodels as the basis of digital twins.
    • Training and validation strategies for digital twins.
    • Digital twin-based asset/machine condition prediction.
  • Electronic design automation (EDA) tools to bridge the different scales and domains by integrating a virtual design flow.
  • Virtual design of experiment as a best practice at the early design stage.
  • Realistic material and interface characterization depending on actual dimensions, fabrication process conditions, ageing effects, etc., covering all critical structures, generating strength data of interfaces with statistical distribution.
  • Mathematical reliability models that also account for the interdependencies between the hierarchy levels (device, component, system).
  • Mathematical modelling of competing and/or superimposed failure modes.
  • New model-based reliability assessment in the era of automated systems.
  • Development of fully harmonized methods and tools for model-based engineering across the supply chain:
    • Material characterization and modelling, including effects of ageing.
    • Multi-domain physics of failure simulations.
    • Reduced modelling (compact models, metamodels, etc.).
    • Failure criteria for dominant failure modes.
    • Verification and validation techniques.
  • Standardization as a tool for model-based development of ECS across the supply chain:
    • Standardization of material characterization and modeling, including effects of ageing.
    • Standardization of simulation-driven design for excellence (DfX).
    • Standardization of model exchange format within supply chain using functional mock-up unit (FMU) and functional mock-up interface (FMI) (and also components).
    • Simulation data and process management.
    • Initiate and drive standardization process for above-mentioned points.
    • Extend common design and process failure mode and effect analysis (FMEA) with reliability risk assessment features (“reliability FMEA”).
    • Generic simulation flow for virtual testing under accelerated and operational conditions (virtual “pass/fail” approach).
  • Automation of model build-up (databases of components, materials).
  • Use of AI in model parametrization/identification, e.g. extracting material models from measurement.
  • Virtual release of ECS through referencing.

Prognostics and health management of ECS: Increase in functional safety and system availability

  • Self-monitoring, self-assessment and resilience concepts for automated and autonomous systems based on the merger of PoF, data science and ML for safe failure prevention through timely predictive maintenance.
  • Self-diagnostic tools and robust control algorithms validated by physical fault-injection techniques (e.g. by using end-of-life (EOL) components).
  • Hierarchical and scalable health management architectures and platforms, integrating diagnostic and prognostic capabilities, from components to complete systems.
  • Standardized protocols and interfaces for PHM facilitating deployment and exploitation.
  • Monitoring test structures and/or monitor procedures on the component and module levels for monitoring temperatures, operating modes, parameter drifts, interconnect degradation, etc.
  • Identification of early warning failure indicators and the development of methods for predicting the remaining useful life of the practical system in its use conditions.
  • Development of schemes and tools using ML techniques and AI for PHM.
  • Implementation of resilient procedures for safety-critical applications.
  • Big sensor data management (data fusion, find correlations, secure communication), legal framework between companies and countries).
  • Distributed data collection, model construction, model update and maintenance.
  • Concept of digital twin: provide quality and reliability metrics (key failure indicator, KFI).
  • Using PHM methodology for accelerated testing methods and techniques.
  • Development of AI-supported failure diagnostic and repair processes for improve field data quality.
  • AI-based asset/machine/robot life extension method development based on PHM.
  • AI-based autonomous testing tool for verification and validation (V&V) of software reliability.
  • Lifecycle management – modeling of the cost of the lifecycle       Major Challenge 2: Ensuring dependability in connected software

Connected software applications such as those used on the Internet of Things (IoT) differ significantly in their software architecture from traditional reliable software used in industrial applications. The design of connected IoT software is based on traditional protocols originally designed for data communications for PCs accessing the internet. This includes protocols such as transmission control protocol/internet protocol (TCP/IP), the re-use of software from the IT world, including protocol stacks, web servers and the like. This also means the employed software components are not designed with dependability in mind, as there is typically no redundancy and little arrangements for availability. If something does not work, end-users are used to restarting the device. Even if it does not happen very often, this degree of availability is not sufficient for critical functionalities, and redundancy hardware and back-up plans in ICT infrastructure and network outages still continue to occur. Therefore, it is of the utmost importance that we design future connected software that is conceived either in a dependable way or can react reliably in the case of infrastructure failures to achieve higher software quality.

The vision is that networked systems will become as dependable and predictable for end-users as traditional industrial applications interconnected via dedicated signal lines. This means that the employed connected software components, architectures and technologies will have to be enriched to deal with dependability for their operation. Future dependable connected software will also be able to detect in advance if network conditions change – e.g. due to foreseeable transmission bottlenecks or planned maintenance measures. If outages do happen, the user or end application should receive clear feedback on how long the problem will last so they can take potential measures. In addition, the consideration of redundancy in the software architecture must be considered for critical applications. The availability of a European ecosystem for reliable software components will also reduce the dependence on current ICT technologies from the US and China.

Dependable connected software architectures

In the past, reliable and dependable software was always directly deployed on specialised, reliable hardware. However, with the increased use of IoT, edge and cloud computing, critical software functions will also be used that are completely decoupled from the location of use (e.g. in use cases where the police want to stop self-driving cars from a distance):

  • Software reliability in the face of infrastructure instability.
  • Dependable edge and cloud computing, including dependable and reliable AI/ML methods and algorithms.
  • Dependable communication methods, protocols and infrastructure.
  • Formal verification of protocols and mechanisms, including those using AI/ML.
  • Monitoring, detection and mitigation of security issues on communication protocols.
  • Quantum key distribution (“quantum cryptography”).
  • Increasing software quality by AI-assisted development and testing methods.
  • Infrastructure resilience and adaptability to new threats.
  • Secure and reliable over-the-air (OTA) updates.
  • Using AI for autonomy, network behaviour and self-adaptivity.
  • Dependable integration platforms.
  • Dependable cooperation of System of Systems (SoS).

This Major Challenge is tightly interlinked with the cross-sectional technology of 2.2 Connectivity Chapter, where the focus is on innovative connectivity technologies. The dependability aspect covered within this challenge is complementary to that Chapter since dependability and reliability approaches can also be used for systems without connectivity.

Software-defined networking (SDN) market size by 2025 (Source: Global Markets Insight, Report ID GMI2395, 2018)

Dependable softwarisation and virtualisation technologies

Changing or updating software by retaining existing hardware is quite common in many industrial domains. However, keeping existing reliable software and changing the underlying hardware is difficult, especially for critical applications. By decoupling software functionalities from the underlying hardware, softwarisation and virtualisation are two disruptive paradigms that can bring enormous flexibility and thus promote strong growth in the market (see Figure F.43). However, the softwarisation of network functions raises reliability concerns, as they will be exposed to faults in commodity hardware and software components:

  • Software-defined radio (SDR) technology for highly reliable wireless communications with higher immunity to cyber-attacks.
  • Network functions virtualisation infrastructure (NFVI) reliability.
  • Reliable containerisation technologies.
  • Resilient multi-tenancy environments.
  • AI-based autonomous testing for V&V of software reliability, including the software-in-the-loop (SiL) approach.
  • Testing tools and frameworks for V&V of AI/ML-based software reliability, including the SiL approach.
F.43 - Software-defined networking (SDN) market size by 2025 (Source: Global Markets Insight, Report ID GMI2395, 2018)

Combined SW/HW test strategies

Unlike hardware failures, software systems do not degrade over time unless modified. The most effective approach for achieving higher software reliability is to reduce the likelihood of latent defects in the released software. Mathematical functions that describe fault detection and removal phenomenon in software have begun to emerge. These software reliability growth models (SRGM), in combination with Bayesian statistics, need further attention within the hardware-orientated reliability community over the coming years:

  • HW failure modes are considered in the software requirements definition.
  • Design characteristics will not cause the software to overstress the HW, or adversely change failure-severity consequences on the occurrence of failure.
  • Establish techniques that can combine SW reliability metrics with HW reliability metrics.
  • Develop efficient (hierarchical) test strategies for combined SW/HW performance of connected products.

Dependability in connected software is strongly connected with other Chapters in this document. In particular, additional challenges are handled in following Chapters:

  • 1.3 Embedded Software and Beyond: Major Challenge 1 (MC1) efficient engineering of software; MC2 continuous integration of embedded software; MC3 lifecycle management of embedded software; and MC6 Embedding reliability and trust.
  • 1.4 System of Systems: MC1 SoS architecture; MC4 Systems of embedded and cyber-physical systems engineering; and MC5 Open system of embedded and cyber-physical systems platforms.
  • 2.1 Edge Computing and Embedded Artificial Intelligence: MC1: Increasing the energy efficiency of computing systems.
  • 2.2 Connectivity: MC4: Architectures and reference implementations of interoperable, secure, scalable, smart and evolvable IoT and SoS connectivity.
  • 2.3 Architecture and Design: Method and Tools: MC3: Managing complexity.         Major Challenge 3: Ensuring cyber-security and privacy

We have witnessed a massive increase in pervasive and potentially connected digital products in our personal, social and professional spheres, enhanced by new features of 5G networks and beyond. Connectivity provides better flexibility and usability of these products in different sectors, with a tremendous growth of sensitive and valuable data. Moreover, the variety of deployments and configuration options and the growing number of sub-systems changing in dynamicity and variability increase the overall complexity. In this scenario, new security and privacy issues have to be addressed, also considering the continuously evolving threat landscape. New approaches, methodologies and tools for risk and vulnerability analysis, threat modeling for security and privacy, threat information sharing and reasoning are required. Artificial intelligence (e.g. machine learning, deep learning and ontology) not only promotes pervasive intelligence supporting daily life, industrial developments, personalisation of mass products around individual preferences and requirements, efficient and smart interaction among IoT in any type of services, but It also fosters automation, to mitigate such complexity and avoid human mistakes.

Embedded and distributed AI functionality is growing at speed in both (connected) devices and services. AI-capable chips will also enable edge applications allowing decisions to be made locally at device level. Therefore, resilience to cyber-attacks is of utmost importance. AI can have a direct action on the behaviour of a device, possibly impacting its physical life inducing potential safety concerns. AI systems rely on software and hardware that can be embedded in components, but also in the set of data generated and used to make decisions. Cyber-attacks, such as data poisoning or adversarial inputs, could cause physical harm and/or also violate privacy. The development of AI should therefore go hand in hand with frameworks that assess security and safety to guarantee that AI systems developed for the EU market are safe to use, trustworthy, reliable and remain under control (C.f. Chapter 1.3 “Embedded Software and beyond” for quality of AI used in embedded software when being considered as a technology interacting with other software components).

The combination of composed digital products and AI highlights the importance of trustable systems that weave together privacy and cybersecurity with safety and resilience. Automated vehicles, for example, are adopting an ever-expanding combination of Advanced Driver Assistance Systems (ADAS) developed to increase the level of safety, driving comfort exploiting different type of sensors, devices and on-board computers (sensors, Global Positioning System (GPS), radar, lidar, cameras, on-board computers, etc.). To complement ADAS systems, Vehicle to X (V2X) communication technologies are gaining momentum. Cellular based V2X communication provides the ability for vehicles to communicate with other vehicle and infrastructure and environment around them, exchanging both basic safety messages to avoid collisions and, according to the 5g standard evolutions, also high throughput sensor sharing, intent trajectory sharing, coordinated driving and autonomous driving. The connected autonomous vehicle scenarios offer many advantages in terms of safety, fuel consumption and CO2 emissions reduction, but the increased connectivity, number of devices and automation, expose those systems to several crucial cyber and privacy threats, which must be addressed and mitigated.

Autonomous vehicles represent a truly disruptive innovation for travelling and transportation, and should be able to warrant confidentiality of the driver’s and vehicle’s information. Those vehicles should also avoid obstacles, identify failures (if any) and mitigate them, as well as prevent cyber-attacks while staying safely operational (at reduced functionality) either through human-initiated intervention, by automatic inside action or remotely by law enforcement in the case of any failure, security breach, sudden obstacle, crash, etc.

In the evoked scenario the main cybersecurity and privacy challenges deal with:

  • Interoperable security and privacy management in heterogeneous systems including cyber-physical systems, IoT, virtual technologies, clouds, communication networks, autonomous systems.
  • Real time monitoring and privacy and security risk assessment to manage the dynamicity and variability of systems.
  • Developing novel privacy preserving identity management and secure (post quantum) cryptographic solutions.
  • Developing new approaches, methodologies and tools empowered by AI in all its declinations (e.g. machine learning, deep learning, ontology).
  • Investigating the interworking among safety, cybersecurity, trustworthiness, privacy and legal compliance of systems.
  • Evaluating the impact in term of sustainability and green deal of the adopted solutions.

The cornerstone of our vision rests on the following four pillars. First, a robust root of trust system, with unique identification enabling security without interruption from the hardware level right up to the applications, including AI, involved in the accomplishment of the system’s mission in dynamic unknown environments. This aspect has a tremendous impact on mission critical systems with lots of reliability, quality and safety & security concerns. Second, protection of the EU citizen’s privacy and security while at the same keeping usability levels and operation in a competitive market where also industrial Intellectual Protection should be considered. Third, the proposed technical solutions should contribute to the green deal ambition, for example by reducing their environmental impact. Finally, proof-of-concept demonstrators that are capable of simultaneously guaranteeing (a given level of) security and (a given level of) privacy, as well as potentially evolving in-reference designs that illustrate how practical solutions can be implemented (i.e. thereby providing guidelines to re-use or adapt).

End to end encryption of data, both in transit and at rest is kept to effectively protect privacy and security. The advent of quantum computing technology introduces new risks and threats, since attacks using quantum computing may affect traditional cryptographic mechanisms. New quantum safe cryptography is required, referring both to quantum cryptography and post quantum cryptography with standard crypto primitives.

Putting together seamlessly security and privacy requirements is a difficult challenge that also involves some non-technical aspects. The human factor can often cause security and privacy concerns, despite of technologically advanced tools and solutions. Another aspect relates to security certification versus certification cost. A certification security that does not mitigate the risks and threats, increases costs with minimal benefits. Therefore, all techniques and methodology to reduce such a cost are in the scope of the challenge.

In light of this scenario, this Major Challenge aims at contributing to the European strategic autonomy plan in terms of cybersecurity, digital trustworthiness and the protection of personal data.


Digital Trust is mandatory in a global scenario, based on ever-increasing connectivity, data and advanced technologies. Trustworthiness is a high-level concern including not only privacy and security issues, but also safety and resilience and reliability. The goal is a robust, secure, and privacy preserving system that operates in a complex ecosystem without interruption, from the hardware level up to applications, including systems that may be AI-enabled. This challenge calls for a multidisciplinary approach, spanning across technologies, regulations, compliance, legal and economic issues. To this end, the main expected outcomes can be declined in:

  • Defining different methods and techniques of trust for a system, and proving compliance to a security standard via certification schemes.
  • Defining methods and techniques to ensure trustworthiness of AI algorithms, included explainable (XAI).
  • Developing methodologies and techniques from hardware trustworthy to software layers trustworthy.
  • Defining methods and tools to support the composition and validation of certified parts addressing multiple standards.
  • Definition and future consolidation of a framework providing guidelines, good practices and standards oriented to trust.

Security and privacy-by-design

The main expected outcome is a set of solutions to ensuring the protection of personal data in the embedded AI and data-driven digital economy against potential cyber-attacks:

  • Ensuring cybersecurity and privacy of systems in the Edge to cloud continuum, via efficient automated verification and audits.
  • Ensuring performance in AI-driven algorithms (which needs considerable data) while guaranteeing compliance with European privacy standards (e.g. general data protection regulation - GDPR).
  • Establishing a cybersecurity and privacy-by-design European data strategy to promote data sovereignty.
  • Establishing Quantum-Safe Cryptography Modules.

Ensuring both safety and security properties

The main expected outcome is to ensure compatibility, adequacy and coherence in the joint use of the promoted security solutions, and the safety levels required by the system or its components:

  • Maintaining the nominal or degraded system safe level behaviour when the system’s security is breached or there are accidental failures.
  • Guaranteeing information properties under cyber-attacks (quality, coherence, integrity, reliability, etc.).
  • Ensuring safety, security and privacy of embedded intelligence (c.f. Chapter 1.3 “Embedded Software and beyond”).
  • Guaranteeing a system’s coherence among different heterogeneous requirements (i.e. secure protocols, safety levels, computational level needed by the promoted mechanisms) and different applied solutions (i.e. solutions for integrity, confidentiality, security, safety) in different phases (i.e. design, operation, maintenance, repair and recovery).
  • Developing rigorous methodology supported by evidence to prove that a system is secure and safe, thus achieving a greater level of transparency without compromising information and trustworthiness.
  • Evaluating the environmental impact of the implemented safety and security solutions (the green chapter connection).      Major Challenge 4: Ensuring of safety and resilience

Safety has always been a key concept at the core of human civilisation. Throughout history, its definition, as well as techniques to provide it, has evolved significantly. In the medical application domain, for example, we have witnessed a transformation from safe protocols to automatic medication machines, such as insulin pumps and respiratory automation, which have integrated safety provisions. Today, we can build a range of different high-integrity systems, such as nuclear power plants, aircraft and autonomous metro lines. The safety of such systems is essentially based on a combination of key factors, including: (i) determinism (the system’s nominal behaviour is always the same under the same conditions); (ii) expertise and continuous training of involved personnel; (iii) deep understanding of nominal and degraded behaviours of the system; (iv) certification/qualification; and (v) clear liability and responsibility chains in the case of accidents.

Nowadays, the digitalisation of ubiquitous systems, and the embedding of AI components (hardware or software) in them, highlights the limits of traditional safety techniques, which need to be extended and/or embedded in new overall safety-case arguments. These techniques for building safe systems include fault-tree analysis, failure modes and effect analysis, evidence-based development standards (such as ISO26262 and ISO 21448), redundancy, diversification and defence-in-depth (c.f. Chapter 1.3 “Embedded Software and beyond” for Major Challenge 1: Efficient engineering of embedded software to enable transition from embedded software engineering to embedded systems engineering.). As a result of the realities in modern systems and their usage, one promising approach is to move the safety paradigm has moved from safety as traditionally studied in embedded systems, to resilience. Most of the methodical factors mentioned above are currently insufficient to cope with resilience in its full meaning. New innovations are required to increase the resilience of systems by tackling challenges involving cross-cutting considerations such as legal concerns and user abilities. For example, the inherent inscrutability of AI algorithms combined with the increasing autonomy of the system threatens liability and responsibility chains in the case of an accident. Understanding the nominal and degraded behaviours of AI-driven system is also extremely complex, and operators of several AI-driven systems are the main users of the system (for example, a child that uses an autonomous vehicle) – i.e. users not necessarily expert in the system itself, unlike the operators in the traditional high-integrity systems, such as operators of nuclear power plants.

The vision points to the development of safe and resilient autonomous cyber-physical systems in dynamic environments, with a continuous chain-of-trust from the hardware level up to the applications that is involved in the accomplishment of the system’s mission, including AI. Our vision takes into account physical limitations (battery capacity, quality of sensors used in the system, hardware processing power needed for autonomous navigation features, etc.) and also considers optimizing the energy usage and system resources of safety-related features to support sustainability of future cyber-physical systems. Civilian applications of (semi-) autonomous cyber-physical systems are increasing significantly. For example, drones can be deployed for monitoring social distancing and providing safety to the population (and also to deliver medicine in the UK). However, the use of drones is not accident-free. In 2015, at the Pride Parade in Seattle, a drone crashed and caused an accident that resulted in a woman being knocked out. Civilian applications thus inherently entail safety, and in the case of an accident or damage (for example, in uploading a piece of software in an AI system) liability should be clearly traceable, as well as the certification/qualification of AI systems.

The increasing trend towards the adoption of AI in civilian applications represents a great opportunity for European economic growth. However, unlike traditional high-integrity systems, the hypothesis that only expert operators can manipulate the final product undermines the large-scale adoption of the new generation of autonomous cyber-physical systems.

In addition to the key focus areas below, the challenges cited in Chapter 2.3 on Architecture and Design: Methods and Tools are also highly relevant for this topic, and on Chapter 1.3 on Embedded Software and beyond.

Safety and resilience of (autonomous AI) systems in dynamic environments

The expected outcome is systems that are resilient under physical constraints:

  • Use of AI in the design process – e.g. using ML to learn fault injection parameters and test priorities for test execution optimization.
  • Resources’ management of all system’s components to accomplish the mission system in a safe and resilient way by considering to minimize the energy usage and system resources of safety-related features to support sustainability of future cyber-physical systems.
  • Identify and address transparency and safety-related issues introduced by AI applications.
  • Concepts and principles for trustable integration and the V&V of intelligent functions in systems/products under uncertain and/or dynamic environments.

Modular certification of trustable systems and liability

The expected outcome is a clear traceability of liability during integration and in the case of accident:

  • Having explicit workflows for automated and continuous layered certification/qualification, both when designing the system and for checking certification/qualification during run-time or dynamic safety contracts, to ensure continuing trust in dynamic adaptive systems in changing environments.
  • Contract-based co-design methodologies, consistency management techniques in multi-domain collaborations.
  • Certificates of extensive testing, new code coverage metrics (e.g. derived from mutation testing), formal methods providing guaranteed trustworthiness.

Dynamic adaptation and configuration, self-repair capabilities, (decentralised instrumentation and control for) resilience of complex and heterogeneous systems

The expected outcome is resilient systems that are able to dynamically adapt their behaviour in dynamic environments:

  • Responding to uncertain information based on digital twin technology, run-time adaptation and redeployment based on simulations and sensor fusion.
  • Automatic prompt self-adaptability at low latency to dynamic and heterogeneous environments.
  • Architectures that support distribution, modularity and fault containment units to isolate faults, possibly with run-time component verification.
  • Develop explainable AI models for human interaction, system interaction and certification.
  • Support for dependable dynamic configuration and adaptation/maintenance to help cope with components that appear and disappear, as ECS devices to connect/disconnect, and communication links that are established/released depending on the actual availability of network connectivity (including, for example, patching) to adapt to security countermeasures.
  • Concepts for SoS integration, including legacy system integration.

Safety aspects related to the human/system interaction

The expected outcome is to ensure safety for the human and environment during the nominal and degraded operations in the working environment (cf. Major Challenge 5 below):

  • Understanding the nominal and degraded behaviour of a system, with/without AI functionality.
  • Minimising the risk of human or machine failures during the operating phases.
  • Ensuring that the human can safely interface with machine in complex systems and SoS, and also that the machine can prevent unsafe operations.
  • New self-learning safety methods to ensure safety system operations in complex systems.
  • Ensuring safety in machine-to-machine interaction.      Major Challenge 5: Human systems integration

This ECS SRIA roadmap aligns societal needs and challenges to the R & D & I for electronic components. The societal benefits thereby motivate the foundational and cross-sectional technologies as well as the concrete applications in the research agenda. Thereby, many technological innovations occur on a subsystem level that are not directly linked to societal benefits themselves until assembled and arranged into larger systems. Such larger systems then most of the time require human users and beneficiary to utilize them and thereby achieve the intended societal benefits. Thereby, it is common that during the subsystem development human users and beneficiaries stay mostly invisible. Only once subsystems are assembled and put to an operational system, the interactions with a human user become apparent. At this point however, it is often too late to make substantial changes to the technological subsystems and partial or complete failure to reach market acceptance and intended societal benefits can result. To avoid such expensive and resource intensive failures, Human Systems Integration (HSI) efforts attempt to accompany technological maturation that is often measured as Technological Readiness Levels (TRL) with the maturation of Human Readiness Levels (HRL). Failures to achieve high HRL beside high TRLs have been demonstrated in various domains such as military, space travel, and aviation. Therefore, HSI efforts to achieve high HRLs need to be appropriately planned, prepared, and coordinated as part of technological innovation cycles. As this is currently only rarely done in most industrial R&D activities, this Chapter describes the HSI challenges and outlines a vision to address them.

There are three high-level HSI challenges along ECS-based products:

  • The first challenge consists of conceiving technologies that are acceptable, trustworthy, and therefore sustainably used and thereby have a chance to achieve the overall targeted individual, societal, and organizational benefits. Thereby, the overall vision for the practical use of a product by real users within their context must often precede the technological specification of the subsystems. In many current innovation environments, this works in the opposite way such that the available technological capabilities are assembled toward use cases that are only meant to demonstrate the technological capabilities. Thereby, sufficiently detailed operational knowledge of the environmental, organizational, and user characteristics is often either not available or cannot be integrated into established technology development cycles. Therefore, the conception of accepted and trusted, and sustainably used technologies is often more the result of trial-and-error than strategically planned development efforts.
  • The second challenge consists of designing envisioned products to achieve the appropriate characteristics that lead to accepted, trusted, sustained usage. Increasingly complex and smart products require often intricate user interaction and understanding than the often much simpler products of the past. Even for a potentially highly promising product, the developing engineers often do not know the concrete usage conditions or constraints of their users and make architecture decisions that can only be costly undone later on. For example, drivers and workers generally do not like to purely monitor or supervise automated functions, while losing their place as active process participants. This is especially critical when humans have to suddenly jump back into action and take control when unexpected conditions require to do so. Therefore, aligning the automation capabilities with the acceptable tasks, available knowledge, and expectable responsibilities of the human users are becoming paramount to bring a product to actual fruition. Thereby, required and desirable human competences and skills need to be formulated so that educational curricula can start working toward achieving them.
  • Thirdly, continuous product updates and maintenance are creating dynamically changing products that can be challenging for user acceptance, trust, and sustained usage. Frequent and increasingly automated software updates have become common place to achieve acceptable security and to enable the latest feature sets as well as allow self-learning algorithms to adapt to user preferences and usage history, and improved performance. However, such changes can be confusing to users if they come unprepared or are difficult to understand. Also, the incorrect usage that may results from this may lead to additional security and acceptance risks. Therefore, the product maintenance and update cycles need to be designed appropriately within the whole product lifecycle to ensure maximum user acceptance and include sufficient information on the side of the users. Here HSI extends beyond initial design and fielding of products.

The vision and expected outcome is that these three HSI challenges can be addressed by appropriately orchestrating the assessments of needs, constraints, and abilities of the human users with the usage conditions in terms of environmental and organizational context, during conception, design, and lifetime support phases of product. Specifically, the vision can be formulated around three cornerstones:

  • Vision cornerstone 1: conceiving systems and their missions that lead to their sustained acceptance and usage start in the early assessment of the usage context, as well as user needs and constraints and to translate this information into a useful form to inform system design and development. Such information is currently not readily available to the conceivers of new systems and such knowledge is currently either hidden or not assessed at the time when it is needed to make an impact during system conception. Needed assessments include the user population and the usage situation including criticality, responsibilities, environment, required tasks and concurrent tasks. Also, the organizational conditions and processes within which the users use the system play an important role that should be allowed to impact design decisions, for example to decide on appropriate explainability methods. The recording and sharing of such information in a format that is understandable to system conceivers, architects, and engineering teams requires special activities and tools.
  • Vision cornerstone 2: to translate a well-conceived system into orchestrated system development requires holistic design processes where multifaceted developer communities jointly work together to achieve acceptable, safe, and trustworthy products. Thereby, the product is not designed and developed in isolation but within actively explored contextual infrastructures that allow to surround the development and design communities within the use environment and conditions of the product. Considering this larger contextual field in the design of products requires advanced R&D approaches and methodologies, to pull together the various fields of expertise and allow mutual fertilization. This requires sufficiently large, multi-disciplinary research environments for active collaboration and enablement of a sufficient intermixture between experts and innovation approaches. This also requires virtual tool sets for collaboration, data sharing, and solution generation.
  • Vision cornerstone 3: detailed knowledge about the user and use conditions are also pertinent to appropriately plan and design the continuous adaptations and updates of products during the lifecycle. Converging of user knowledge and expectations will allow more standardized update policies. This will be addressed by bringing the European end-users, workers, and operators toward achieving the digital literacy with a chance to enable the intended societal benefits. The formation of appropriate national and international training and educational curricula will work toward shaping users with sufficiently converging understanding of new technology principles and expectations as well as knowledge about responsibilities and common failure modes to facilitate sustained and positively perceived interactions.

Within these cornerstones, the vision is to intermingle the multi-disciplinary areas of knowledge, expertise, and capabilities within sufficiently inter-disciplinary research and development environments where experts can interact with stakeholders to jointly design, implement, and test novel products. Sufficiently integrated simulation and modeling that includes human behavioral representations are established and link the various tasks. The intermingling starts with user needs and contextual assessments that are documented and formalized sufficiently to stay available during the development process. Specifically, the skills and competences are formally recorded and made available for requirements generation.

F.44 - Human Systems Integration in the ECS SRIA

  • Systematize methods for user, context, and environment assessments and sharing of information for user-requirement generation. Such methods are necessary to allow user centered methods to achieve an impact on overall product design.
  • Develop simulation and modeling methods for the early integration of Humans and Technologies. The virtual methods link early assessments, holistic design activities, and lifelong product updates and bring facilitate convergence among researchers, developers, and stakeholders.
  • Establish multi-disciplinary research and development centers and sandboxes. Interdisciplinary research and development centers allow for the intermingling of experts and stakeholders for cross-domain coordinated products and life-long product support.



SHORT TERM (2023–2027)

MEDIUM TERM (2028–2032)


Major Challenge 1:

Ensuring HW quality and reliability

Topic 1.1: Quality: in situ and real-time assessments

Create an environment to fully exploit the potential of data science to improve efficiency of production through smart monitoring to facilitate the quality of ECS and reduce early failure rates

  • Establish a procedure to improve future generation of ECS based on products that are currently in the production and field
  • Feedback loop from the field to design and development

Provide a platform that allows for data exchange within the supply chain while maintaining IP rights

Topic 1.2: Reliability: tests and modelling

Development of methods and tools to enable third generation of reliability – from device to SoS

Implementation of a novel monitoring concept that will empower reliability monitoring of ECS

Identification of the 80% of all field-relevant failure modes and mechanisms for the ECS used in autonomous systems

Topic 1.3: Design for (EoL) reliability: virtual reliability assessment prior to the fabrication of physical HW

Continuous improvement of EDA tools, standardisation of data exchange formats and simulation procedures to enable transfer models and results along full supply chain

Digital twin as a major enabler for monitoring of degradation of ECS

AI/ML techniques will be a major driver of model-based engineering and the main contributor to shortening the development cycle of robust ECS

Topic 1.4: PHM of ECS: increase in functional safety and system availability

Condition monitoring will allow for identification of failure indicators for main failure modes

Hybrid PHM approach, including data science as a new potential tool in reliability engineering, based on which we will know the state of ECS under field loading conditions

Standardisation of PHM approach along all supply chains for distributed data collection and decision-making based on individual ECS

Major Challenge 2:

Dependable connected software architectures

Topic 2.1: Dependable connected software architectures

Development of necessary foundations for the implementation of dependable connected software to be extendable for common SW systems (open source, middleware, protocols)

Set of defined and standardised protocols, mechanisms and user-feedback methods for dependable operation

Availability of European ecosystem for dependable software, including certification methods

Topic 2.2: Dependable softwarisation and virtualisation technologies

Create the basis for the increased use of commodity hardware in critical applications

Definition of softwarisation and virtualisation standards, not only in networking but in other applications such as automation and transport

Widely applied in European industry

Topic 2.3: Combined SW/HW test strategies

Establish SW design characteristics that consider HW failure modes

Establish techniques that combine SW reliability metrics with HW reliability metrics

Efficient test strategies for combined SW/HW performance of connected products

Major Challenge 3:

Ensuring privacy and cybersecurity

Topic 3.1: Trustworthiness

  • Root of trust system, and unique identification enabling security without interruption from the hardware level up to applications, including AI
  • Definition of a framework providing guidelines, good practices and standards oriented to trust
  • Definition of a strategy for (modular) certification under uncertain and dynamically changing environments
  • Consolidation of a framework providing guidelines, good practices and standards oriented to trust


Topic 3.2: Security and privacy by design

Establishing a secure and privacy-by-design European data strategy and data sovereignty

  • Ensuring the protection of personal data against potential cyber-attacks in the data-driven digital economy
  • Ensuring performance and AI development (which needs considerable data) by guaranteeing GDPR compliance


Topic 3.3: Ensuring both safety and security properties

Guaranteeing information properties under cyber-attacks (quality, coherence, integrity, reliability, etc.) independence, geographic distribution, emergent behaviour and evolutionary development

  • Ensuring the nominal and degraded behaviour of a system when the underlying system security is breached or there are accidental failures
  • Guaranteeing a system’s coherence while considering different requirements, different applied solutions, in different phases
  • Evaluating the impact of the contextualisation environment on the system’s required levels of safety and security

Developing rigorous methodology supported by evidence to prove that a system is secure and safe, thus achieving a greater level of trustworthiness

Major Challenge 4:

Ensuring safety and resilience

Topic 4.1: safety and resilience of (autonomous AI) systems in dynamic environments

  • Resources’ management of all system’s components to accomplish the mission system in a safe and resilient way
  • Use of AI in the design process – e.g. using ML to learn fault injection parameters and test priorities for test execution optimisation

Apply methods for user context and environment assessments and sharing of information for stakeholder-requirement generation to prototypical use cases, establish practices of use and generally applicable tools

  • Develop standard processes for stakeholder context and environment assessments and sharing of information
  • Develop standard processes for stakeholder knowledge, skills, and competence capturing techniques to inform requirements generation
  • Develop educational programs to increase the levels of common stakeholder knowledge, skills and competences for sustainable product uptake across Europe

Topic 4.2: modular certification of trustable systems and liability

Contract-based co-design methodologies, consistency management techniques in multi-domain collaborations

Develop centers of excellence for early assessments, holistic design activities, and lifelong product updates and bring facilitate convergence among researchers, developers, and stakeholders can be realized, practiced, and established as lighthouses of holistic Design and Development of embedded components


Topic 4.3: Dynamic adaptation and configuration, self-repair capabilities (decentralised instrumentation and control for), resilience of complex systems

  • Support for dependable dynamic configuration and adaptation/maintenance
  • Concepts for SoS integration, including the issue of legacy system integration
  • Using fault injection methods, models-of-the-physics and self-diagnostic architecture principles to understand the true nature of the world, and respond to uncertain information (included sensor’s false positives) or attacks in a digital twin
  • run-time adaptation and redeployment based on simulations and sensor fusion
  • Architectures that support distribution, modularity and fault containment units to isolate faults, possibly with run-time component verification

Develop prototypical use cases where interdisciplinary research and development centers allow for the intermingling of experts and stakeholders for cross-domain coordinated products and life-long product support. This should allow sufficient demonstrate


Topic 4.4: safety aspects related to HCI

  • Minimising the risk of human or machine failures during the operating phases
  • Ensuring that the human can safely interface with the machine, and also that the machine prevents unsafe operations
  • Ensuring safety in machine-to-machine interaction



Major Challenge 5:

Human–systems integration

Topic 5.1: systematize methods for user, context, and environment assessments and sharing of information for user-requirement requirements generation

  • Provide means for user centered methods to achieve an impact on overall product design
  • Establish stakeholder knowledge, skills, and competence capturing techniques to inform requirements generation



Topic 5.2: develop simulation and modeling methods for the early integration of Humans and Technologies

  • Link early assessments, holistic design activities, and lifelong product updates and bring facilitate convergence among researchers, developers, and stakeholders
  • Establish tools to bring stakeholder knowledge, skills, and competence capturing techniques to inform design and development activities



Topic 5.3: establish multi-disciplinary research and development centers and sandboxes

  • Interdisciplinary research and development centers allow for the intermingling of experts and stakeholders for cross-domain coordinated products and life-long product support
  • Establish tools and processes to update stakeholder knowledge, skills, and competence capturing techniques to inform design and development activities



The Major Challenge “Ensuring HW quality and reliability” is a key element for any ECS, which is why it can be linked to any application area. It is directly linked to the technology Chapter: Components, Modules and Systems Integration. For quality, the novel design of reliability methodologies such as PHM requires direct connection to all cross-sectional technologies (Edge Computing and Embedded Artificial Intelligence; and Architecture and Design: Methods and Tools).

The Major Challenge “Ensuring dependability in connected software” is strongly linked to the Chapter Embedded Software and Beyond as implementations will cover embedded devices to a high degree. It is also linked to the Connectivity Chapter and the Edge Computing and Embedded Artificial Intelligence Chapter since software must reliably interact remotely, from a system to the edge and to the cloud. From a different perspective, it is also linked to the Chapter on System of Systems considering that software-based systems will be integrated over distances.

The Major Challenges “Cybersecurity and privacy” and “Safety and resilience” address robust and resilient systems in a complex ecosystem without interruption, from the hardware level up to applications, including systems that may be enabled by AI. The outcome of these challenges supports all application Chapters, in particular Health and Wellbeing, Mobility, Digital Industry, Digital Society and Agrifood and Natural Resources. Moreover, they are also linked to the Chapters Edge Computing and Embedded Artificial Intelligence, Architecture and Design: Methods and Tools, Embedded Software and Beyond and System of Systems.

145 Vital societal functions: services and functions for maintaining the functioning of a society. Societal functions in general: various services and functions, public and private, for the benefit of a population and the functioning of society.