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Automotive meets ICT-enabling the shift of value creation supported by European R&D

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Abstract and Figures

Digitalization is proving to be a game changer in bridging the gap between the heterogeneous skills and markets. It increases productivity through optimisation over the entire supply chain and lets new services emerge through the convergence of applications domains. In this paper, we are providing a review of the main automotive trends and are highlighting how digitalization (especially by information communication technologies-ICT) is supporting, even pushing innovation. We are especially mapping to the IOT4CPS and SCOTT projects to present key results related to Internet of Things supporting the digital transition in the automotive domain.
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This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
Automotive meets ICT enabling the shift of
value creation supported by European R&D
Eric Armengaud
1
, Bernhard Peischl
2
, Peter Priller
3
, Omar Veledar
4
Abstract Digitalization is proving to be a game changer in bridging the gap between the het-
erogeneous skills and markets. It increases productivity through optimisation over the entire
supply chain and lets new services emerge through the convergence of applications do-
mains. In this paper, we are providing a review of the main automotive trends and are high-
lighting how digitalization (especially by information communication technologies ICT) is
supporting, even pushing innovation. We are especially mapping to the IOT4CPS and
SCOTT projects to present key results related to Internet of Things supporting the digital
transition in the automotive domain.
1. Introduction
Digitalization is a game changer in bridging the gap between the heterogeneous skills and
markets. Firstly, digitalization supports the increase of productivity through optimization over
the entire supply chain by seamlessly connecting the various stages of the product lifecycle.
This enables an earlier and more accurate prediction of the real behavior during a concept
phase (frontloading) as well as faster and more tailored improvement of development and
production processes taking into account real product’s usage. Secondly, digitalization ena-
bles the emergence of new services through the convergence of different applications do-
mains.
Figure 1: Digitalization as game changer
This is especially true for the automotive domain. Nowadays, the automotive sector is con-
fronted to four main trends
Electrification [1], with the introduction of e-mobility (hybrid, pure electric vehicle) to
optimize or even completely remove the internal combustion engine, finally reducing
the resulting pollutant emissions during vehicle operation
1
AVL List GmbH, eric.armengaud@avl.com
2
AVL List GmbH, bernhard.peischl@avl.com
3
AVL List GmbH, peter.priller@avl.com
4
AVL List GmbH, omar.veledar@avl.com
This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
ADAS and autonomous driving functions [2], with the purpose of providing more
comprehensive information to the driver for better context awareness, up to taking
over specific driving maneuvers finally reducing the demands on the driver and re-
ducing number and impact of possible accidents
Connected vehicles [3] enabling optimization of vehicle’s operation or the emergence
of new services while relying on external information, e.g. from other vehicles or from
the infrastructure
Diverse mobility [4] targeting the efficient movement of people and goods with re-
spect to different factors such that time, energy consumption, ecological footprint.
For all the trends, digitalization is playing a pivotal role as supporting technology (e.g, power
electronics and smart control for electrification) as well as enabling technology. This is evi-
dent for autonomous driving with the development and deployment of smart systems for ad-
vanced environment sensing and complex decision making supported by cyber-physical sys-
tems, for connectivity where Internet of Things is playing a key role for optimized machine to
machine communication, or for diverse mobility taking advantage of the organization of
complex and fairly independent data sets from different domains to finally enable the emer-
gence of new services.
The contribution of this paper is a proposal to structure the technology stack, therefore mak-
ing the link between value creation and required expertise. The remaining of the paper is
organized as follow: Section 2 discusses the challenges of deploying market specific end-to-
end ICT stacks; Sections 3 and 4 are providing an overview of the R&D projects SCOTT
5
and IOT4CPS
6
as respectively large European and Austrian initiatives for Internet of Things,
thus illustrating main research domains. Finally, Section 5 concludes this work.
2. The ICT stack
The size of the software in software-intensive systems follows Moore’s law and increases
with an order of magnitude every 5 to 10 years, depending on the domain. In a modern pre-
mium car, software amounts to overall more than 100 million object code instructions, total-
ing close to 1 gigabyte of software [5]. With this trend being continued, the value creation in
and around cars is increasingly determined by embedded software and data. This is driven
by three main trends.
Firstly, due to the large-scale adoption (e.g., consider the mobile phone market), the cost of
sensors and actuators has dropped dramatically over the last decade [6]. Due to this inex-
pensive and wide-scale deployment of sensors and actuators, today’s ICT systems obtain
orders of magnitude more data about the environment in which these systems operate,
when compared to earlier days. Secondly, the cost of data storage has dropped radically,
making it possible to store and analyze this data lake to an unprecedented extent. Finally,
due to the increasing amount of computational power available to software-intensive sys-
tems, we manage to analyze data and to make considerably more dynamic, contextual deci-
sions as compared to earlier days. Although there are promising application fields (e.g.,
trends such as electrification, ADAS, connected vehicles and diverse mobility in the automo-
tive sector), there is no established methodology for systematically building homogenous
end-to-end solutions.
Figure 2 proposes a high-level overview of a technology stack for data-driven automotive
applications: a) the connected devices (or cyber-physical systems CPS), implementing
digitalization at the edge, will be in charge to interact with the physical world in order to local-
ly digitalize relevant information and act on the environment. They are typically specialized
5
https://scottproject.eu/
6
https://iot4cps.at/
This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
for a dedicated application and market. b) The data platforms and analytics will target the
data gathering, organization processing, as well as communication management. These
layers typically present a high degree of standardization to enable higher number of partici-
pants to collaborate. c) The market-specific application, on top, will provide the value crea-
tion for the customer while relying on the lower layers. This layer is typically tailored for a
specific market need. d) The stack is further complemented by data management, including
security (privacy, confidentiality) and ethics, as well as interfaces to interact with external
data markets and digital ecosystems.
Figure 2: Technology stack
2.1 Cyber-Physical Systems - Digitalization at the Edge
Cyber-physical systems (CPS) focus on the digitalization at the edge. CPS are responsible
to interact with the physical world through advanced sensing, local (power-aware) processing
and acting. CPS encompass different aspects:
Sensing and acting: Refers to the capability to interact with the environment by accurate
measurements of physical behaviors and actuations thereof (e.g., photonics).
Embedded computing platforms: Denotes the capability for local processing, making a
trade-off between power awareness, and performance (e.g., computing power, memory
size, bandwidth).
Embedded software: Refers to the capability to develop a software optimized for limited
computing resources, low memory footprint and low energy consumption.
Smart systems integration & dependability: Denotes the capability to deploy and tailor
CPS for dedicated industrial applications, while taking into account safety, security, relia-
bility, and timing requirements.
This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
Important European initiatives addressing the abovementioned aspects are ECSEL
7
as part-
nership between Aeneas, EPoSS
8
and ARTEMIS-IA
9
, as well as the European technology
platform Photonics21
10
.
2.2 Data platforms and the IoT - Collaborative Digitalization
The complementary capability to edge computing is the capability to coordinate information
streams and activities, therefore leveraging the local smart devices with other smart devices
cloud computing, centralized systems with higher performances, and / or by interfacing
with expert knowledge. Collaborative digitalization relies on the following pillars:
Networks: Networks need to provide the capability to exchange information in a power-
aware and performance-aware manner.
Machine to machine connectivity (Internet of Things): Refers to the capability to organize
information exchange between heterogenous components and to implement functions on
distributed systems.
Data analytics, visualization: Denotes the capability to extract knowledge out of large data
sets, e.g., through interaction with experts. Note that high-performance computing is
playing a key role here to provide the computing resources for processing huge amounts
of (often poorly structured) data.
Data management: With the need to comply to General Data Protection Regulation
11
(GDPR, [10]), the capability to appropriately manage the data from an ethical point of
view taking into account confidentiality and privacy aspects is gaining importance.
Important European initiatives are the 5G Public Private Partnership (5G-PPP
12
) for connec-
tivity, the Alliance for Internet of Things Innovation (AIOTI) for the internet of things, the BIG
Data Value Association (BDVA
13
) for big data, and the European Technology Platform for
High Performance Computing (ETP4HC
14
) for high-performance computing.
2.3 Market-specific applications and -services
The concepts sketched in the previous two sections build up the technology stack for innova-
tion and differentiation on the market as these technologies enable new use cases and ser-
vices in the automotive context. However, considering companies such as Tesla or Uber,
none of these companies disrupted the market because they have used better or more so-
phisticated technology when compared to their competitors. Instead, Tesla and Uber under-
stood the unexpressed needs from their customers and came up with innovative services to
meet these needs. Thus, identifying and meeting such needs is a pivotal issue. As success-
ful innovations are often developed before customers even express the needs that the inno-
vation addresses [7], tools for collection and analysis of customer data are critical for cus-
tomer-driven innovation. The ability to innovate is determined by skill sets in three pillars [8]:
Speed: Fast feedback cycles have the potential to outperform any other efficiency im-
provements. Speed in this context refers to the ability to convert an identified customer
need into a new service or product feature and thus addresses the entire chain: from the
identification of the customer needs, to design, implementation and maintenance.
7
https://www.ecsel.eu/
8
http://www.smart-systems-integration.org/
9
https://artemis-ia.eu/
10
https://www.photonics21.org/
11
https://eur-lex.europa.eu/eli/reg/2016/679/oj
12
https://5g-ppp.eu/
13
http://www.bdva.eu/
14
http://www.etp4hpc.eu/
This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
Data: Effective use of data from customers and products in the field is the next area to
exploit and monetize. However, collecting huge amount of data is not particularly useful.
Rather it is the information that can be extracted from such a data lake that ultimately
has value. Extracting information from large and heterogenous volumes of data requires
the use of cutting-edge software technologies, such as data mining and data analytics.
Innovation comes from combining these technologies together with domain- and market
knowledge to create new or deeper insights into value creation opportunities.
Eco-systems: Strategic use of the ecosystem around the system/service is critical as this
allows for agility, risk sharing and to focus on key differentiating functionality. Strategic
collaboration with partners helps to offload commodity functions to eco-system partners
and in identifying differentiating services or product features.
Mastering skills related to these three pillars offers numerous opportunities to optimize de-
velopment and maintenance processes (e.g., a digital-twin for a powertrain) and for the pro-
vision of new services (e.g., multimodal mobility services). However, to provide homogenous
end-to-end solutions, the automotive industry needs to address the fragmentation on various
levels: at the edge (CPS), at the level of IoT and data analytics as well as at the service- and
application level.
Regarding automotive applications, European roadmaps are related to the European Road
Transport Research Advisory Council ERTRAC
15
[2,3] for road transport evolution, and the
European Factories of the Future Research Association (EFFRA
16
) for smart production.
3. The SCOTT project
Project SCOTT boldly aims to "build trust in the Internet of Things". Started 2017 as a
H2020/ECSEL Innovation Action (IA) by an European consortium of more than 50 industry
and research partners, SCOTT's acronym is a good summary of the main objectives: "Se-
cure Connected Trustable Things". SCOTT takes advantage of results like the Bubble con-
cept, high-level architecture and several individual technology building blocks developed of
the predecessor project DEWI
17
(Dependable Wireless Infrastructure, 2013-2017).
Many innovative applications of ICT in different aspects of society have been made possible
by embedded computing resources together with innovative sensor and actuator systems
locally into "things" (e.g., Smart Speakers). However, these systems often provide even
more value when networked appropriately with other "things" in its vicinity. Arguably the big-
gest breakthrough however came by teaming up locality (embedded cyber-physical systems)
with the close-to-infinite computation and storage resources of cloud computing. It is this
combination which may make good on the promise of "real" smart things, providing disrup-
tive innovations in almost all aspects of life (e.g., smart home, smart health and of course
smart mobility).
In many contexts, networking smart things essentially means wireless connectivity, providing
full mobility, and the means to establish connectivity anywhere, anytime and autonomously
(no user intervention required). Wireless connectivity is therefore key enabler for the vast
majority of such applications. However, wireless also brings challenges like varying quality of
service, concerns regarding dependability (e.g., domains requiring certain levels of availabil-
ity like industrial production lines; domains with for safety-relevant applications having regu-
lations defining mandatory reliability). Aforementioned project DEWI focused exactly on this
concern, defining the local DEWI bubble with dependability measures from physical all the
way to application protocol layer, and appropriate access interfaces. Multiple demonstrators
in automotive, aeronautics and rail domain clearly showed, that highly reliable wireless con-
nectivity is achievable. Even so when sticking to tight energy budgets, which is another im-
15
https://www.ertrac.org/
16
https://www.effra.eu/
17
http://www.dewiproject.eu/
This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
portant factor for smart things (typically battery-powered and/or powered by energy harvest-
ing from the environment).
DEWI indicated the way to dependable wireless connectivity. However, there was another
major challenge to overcome in order to enable ICT everywhere: user acceptance. Users
need to have trust in smart systems, and the services provided. Project SCOTT envisions
"trustable things", which is addressed in several dimensions in parallel: from providing ener-
gy-efficient cyber security (confidentiality, integrity, and availability) both in the wireless
communication bubble and beyond; to raising technology readiness level of the components
of such smart things (including energy harvesting, efficiency and management). An im-
portant part of SCOTT is dedicated to analyze the way we humans establish trust in (com-
puterized) systems. This includes both security and other user-interaction based issues in-
cluding contextual, organizational, and individual aspects (e.g., to inform users about the
currently established level of system integrity and dependability)
18
.
As envisioned by Mörtel et al [9], the trust framework developed and applied in SCOTT shall
guide early design decisions and efforts to allow systems better trusted and accepted by its
end users or acquirers. It will be applied and demonstrated by AVL and other industrial part-
ners with 15 use cases in domains like automotive (connected vehicle, smart testing factory),
health, production systems, aeronautics and train management.
SCOTT also suggests appropriate privacy labeling and trust indicating metrics for connected
systems, to allow users to better judge the value and risks of using. This includes methodol-
ogy to quantify privacy aspects like [10], [11]. As an example, AVL cooperates with other
partners on connected vehicles providing cloud-based services to travelers optimizing their
mobility choices and monitoring health of their vehicles, including smart and predictive
maintenance and software updates as required. Trust indicators and configurability support-
ing privacy labeling was part of the system design from the start. Other trust factors like sys-
tem availability are addressed in technology building blocks balancing sustainable energy
management and dependable connectivity with appropriate new protocol approaches like
[12]. Project SCOTT will display and report results in a multitude of demonstrators at its clo-
sure event in 2020; activities and progress are reported frequently via several channels
19
.
4. The IOT4CPS project
IoT4CPS is a project run by a consortium of 16 Austrian scientific and industrial partners. It
is partially funded by the “ICT of the Future” Program of the FFG (Austrian Research Promo-
tion Agency) and the BMVIT (Austrian Ministry for Transport, Innovation and Technology).
The lighthouse nature of the project is reflected in intention to not only deliver technical re-
sults, but also to provide guidance and inspiration for sustainable implementation of project
findings in the post-project phase. Hence, the partners are also enhancing created impact by
identifying and coordinating the interfaces to related ecosystems. One such example results
from exploitation of the project’s security nature, which makes it complementary to Auto-
Drive
20
an important lighthouse project for automated driving.
IoT4CPS focuses on development, production and operation of highly trustable components
and applications for Connected and Autonomous Driving. The Smart Production is causally
implicated into the project, as realization of the necessary components demands high levels
of integration and information exchange along the life cycle. However, rather than diving
deep into the full aspect range of Industry 4.0, the project’s attention is turned towards the
value chain of Autonomous Driving.
Many core technologies that form the set of complex mechanisms required for autonomous
driving have already achieved levels of maturity needed for integration into commercially
available vehicles. However, due to the rising complexity of the involved CPS, the utilization
of the computing at the edge is also increasing. The dependability aspect of the proposed
18
https://scottproject.eu/home-3/trustability/
19
https://www.researchgate.net/project/SCOTT-Secure-Connected-Trustable-Things
20
https://autodrive-project.eu/
This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
CPS solutions is a focal point of many discussions, as the safety is a critical factor in driving
applications in general. Therefore, IoT4CPS is highly motivated to highlight the security and
safety aspect of technologies capable of meeting the challenges related to automated and
context-aware vehicles. The project aims to address safety and security aspects in a holistic
approach along the value chain and the product life cycles. Hence, IoT4CPS is considering
the entire technology stack from semi-conductors (sub-component level), through control
systems (component level), to applications (system level). As the autonomous driving of the
future is also very much likely to rely on information about specific aspects of external envi-
ronment, the communication between the vehicle and infrastructure is also considered. The
project partners are also estimating the potential development trajectories of various infra-
structures and are modelling possible constructive interoperability of IoT platforms. Simulta-
neously, IoT4CPS addresses the product life cycle from development, via production to op-
erational use
The slowly emerging business models that utilize the technological advancements in con-
nected and autonomous driving heavily rely on data collection, analysis and visualization.
The key question is concerned with privacy, security and safety in terms of access to the raw
data and to the results of the data processing. The concerns increase, if one considers that it
seems nearly impossible to provide a true “end-to-end” IoT security solution from MCU level
to Internet/Cloud. However, the efforts in terms of cryptography development are aiming at
protecting the privacy of the users. IoT4CPS is seeking to implement efficient and secure
cryptographic solutions, without introduction of undesired latency. The aim is to accommo-
date for the usage of dynamic number of IoT devices by enabling scalable and usable public
key approaches. The analysis of communicated IoT data is proposed using security enabled
tools that support identification of anomalies in a continuously changing IoT-enabled CPS
systems.
The project is aiming to provide two types of industrial demonstrators. The autonomous driv-
ing demonstrator is going to be used for showcasing the implementation and usage of se-
cure and safe IoT platforms and V2X communication. The Industry 4.0 demonstrator will
showcase trustworthy radio connectivity solutions, life-cycle traceability solutions and securi-
ty testing along the product lifecycle.
Figure 3: Project flow SCOTT (left) and IOT4CPS (right)
Figure 3 illustrates the project flow for the SCOTT and IOT4CPS projects. In both projects,
the core value creation (trusted IOT technology bricks) is part of the ICT technology stack. At
the same time, both projects are strongly driven by industrial use cases from different appli-
cation domains. This organization very well highlights the different levels of value creation as
presented in Section 2: the technology providersdevelop new technology bricks providing
new capabilities. At the same time, end-to-end solution providers”, combining domain and
ICT expertise, are required to finally integrate and tailor the technology bricks for a specific
application domain. This cascaded value creation is probably one of the most important chal-
lenge for efficient opportunity identification, industrialization and exploitation of ICT solutions
in industrial domains.
This is a pre-print of a contribution published in Electronics Components and Systems for Automotive
Applications (Editor: J. Langheim) published by Springer, DOI 10.1007/978-3-030-14156-1.
5. Summary
ICT is a game changer in the automotive domain. Through digitalization, the value creation
is shifted toward more customized services around the vehicle, the user and the environ-
ment, leading to the emergence of new business models such as mobility-as-a-service. At
the same time, ICT will require evolution in the value creation process. ICT is an individual
journey for each institution. The European Commission has recognized the importance of
this journey and is providing incentives and supports (e.g., through the PPPs and related
roadmaps) to coordinate and accelerate its take-off, finally supporting the strengthening of
the European industry toward digitalization. Still, a strong integration in the company strategy
is required to go through this journey.
Acknowledgement
This work has been supported in part by research from the SCOTT and IOT4CPS projects.
SCOTT has received funding from the Electronic Component Systems for European Leader-
ship Joint Undertaking under grant agreement No 737422. This Joint Undertaking receives
support from the European Union’s Horizon 2020 research and innovation programme and
Austria, Spain, Finland, Ireland, Sweden, Germany, Poland, Portugal, Netherlands, Belgium,
Norway. The IoT4CPS project is partially funded by the "ICT of the Future" Program of the
FFG and the BMVIT.
References
[1] ETPs, 2017. “European Roadmap Electrification of Road Transport” jointly published by the three
ETPs published in 2017. Available at https://www.ertrac.org/index.php?page=ertrac-roadmap
[2] ERTRAC, Automated Driving Roadmap, 7th version, May 2017, available at
https://www.ertrac.org/index.php?page=ertrac-roadmap
[3] McKinsey & Company, Monetizing car data - New service business opportunities to create new
customer benefits, Advanced industries, Sept 2016
[4] ERTRAC, Urban mobility Roadmap, final version, February 2017, available at
https://www.ertrac.org/index.php?page=ertrac-roadmap
[5] Ebert, C., & Jones, C. 2009. Embedded software: Facts, figures, and future. Computer, (4), 42-52.
[6] Hatler, M., Gurganious, D. & Chi, C. 2012. Industrial wireless sensor networks. A market dynamics
report. ON World.
[7] BoschSijtsema, P., & Bosch, J. (2015). User involvement throughout the innovation process in
hightech industries. Journal of Product Innovation Management, 32(5), 793-807.
[8] Bosch, Jan. Speed, data, and ecosystems: Excelling in a software-driven world. CRC Press, 2017.
[9] SCOTT public deliverable D28.1: "Foundations for Building Trusted Systems", V2.1 (2017), acces-
sible via https://scottproject.eu/media/deliverables/
[10] Miche, Y., Ren, W., Oliver, I., Holtmanns, S., & Lendasse, A. (2018). A Framework for Privacy
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[11] Fiaschetti, A., Noll, J., Azzoni, P., & Uribeetxeberria, R. (Eds.). (2017). Measurable and Composa-
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[12] Bernhard, H. P., Springer, A., Priller, P., & Hörmann, L. B. (2018, June). Energy balanced routing
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ResearchGate has not been able to resolve any citations for this publication.
Article
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In this paper, we propose to investigate how the effects of privacy techniques can be practically assessed in the specific context of data anonymization, and present some possible tools for measuring the effects of such anonymization. We develop an approach using mutual information for measuring the information content in any dataset, including over non-Euclidean data spaces, by means of mapping non-Euclidean distances to a Euclidean space. We further evaluate the proposed approach over toy datasets composed of timestamped GPS traces, and attempt to quantify the information content loss created by three state-of-the-art anonymization approaches. The results allow for an objective quantification of the effects of the k-anonymity and differential privacy algorithms, and illustrate on the toy data used, that such privacy techniques have very non-linear effects on the information content of the data.
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We are at the beginning of a new age of business, where dynamic interaction is the driving force for whatever kind of business. To draw from a known analogy, “bring your own device” (BYOD) exemplifes the trends of devices accessing processes and information on enterprises. In the upcoming years, not only phones, tablets, and computers will demand access, but also sensors and embedded systems will deliver and request information. In the traditional way of handling dynamic interaction, the attempt was to secure the whole infrastructure of a company. To follow the analogy, BYOD is often seen as a threat, and answered in the classical way by preventing employees from using their devices, as security cannot be ensured. A second variant of counteracting classic threats such as insuf cient authentication and loss of devices is addressed through an approach of integrating, managing, and securing mobile devices. But these strategies cannot be applied to sensors and other kinds of cyber-physical systems. Companies cannot stop integrating embedded systems into their infrastructures, as their businesses and processes need them to remain competitive. So, they need to be able to assess the dynamic interaction impact of integrating a new system into their infrastructure in a manageable way, which conventionally suffers from two aspects: i. Secure interaction issues in current systems are described through an integrated approach, and do not open for scalability. ii. Measurable security in terms of quantiable results is not industry. A paradigm shift in handling dynamic interaction is required, addressing the need for securing information instead of securing infrastructure. The paradigm shift includes the need for a security methodology definition first, and for the consequent measurability. SHIELD addresses both these shortcomings, providing the methodology and the means of integrating new infrastructures, new ways of communication, and new devices. It thereby answers the upcoming trends of wireless sensors, sensor networks, and automated processes. Though the focus of SHIELD is on introducing security for cyber-physical systems, we see that these security measures need to be the basis for running automated processes. Consequently, the solution proposed in this book addresses a metrics-based approach for a quantitative assessment of both the potential attack scenario and the security measures of the information, and outlines the methodology of measurable security for systems of cyber-physical systems. Measurable security is often misinterpreted as a good risk analysis. The SHIELD approach works toward measuring security in terms of cardinal numbers, representing the application of special security methods as compared to the specific threat scenario. The approach is based on the semantic description of a potential attack scenario, the security-related aspects of sensors/systems, and security policies that should be applied irrespective of the scenario. Through SHIELD, we address measurable security and introduce countable numbers for the security components of systems. We also address the scalability aspect by using composition techniques that are able to build a security representation of the composed system (system of systems) based on the individual security representations of each individual element. This simplifies the process of measuring the security of the composed system, and opens up the opportunity to build the system in an incremental way. This approach is particularly indicated to manage all the security aspects of cyber-physical systems, embedded systems that are interconnected, interdependent, collaborative, and smart. They provide computing and communication, monitoring, and control of physical components and processes in various applications. Many of the products and services that we use in our daily lives are increasingly determined by cyber-physical systems and, the software that is built into them is the connection between the real physical world and the built-in intelligence. The SHIELD approach also represents an answer to dependability aspects. Dependability is a key aspect of cyber-physical systems, in particular in safety-critical environments that may often require 24/7 reliability, 100% availability, and 100% connectivity, in addition to real-time response. Moreover, security and privacy are both important criteria that affect the dependability of a system; therefore, this book focuses on security, privacy, and dependability issues within the context of embedded cyber-physical systems, considering security, privacy, and dependability both as distinct properties of a cyber-physical system and as a single property by composition. Increasing security, privacy, and dependability requirements introduce new challenges in emerging Internet of Things and Machine to Machine scenarios, where heterogeneous cyber-physical systems are massively deployed to pervasively collect, store, process, and transmit data of a sensitive nature. Industry demands solutions to these challenges—solutions that will provide measurable security, privacy, and dependability, risk assessment of security critical products, and configurable/composable security. Security is frequently misconstrued as the hardware or software implementation of cryptographic algorithms and security protocols. On the contrary, security, privacy, and dependability represent a new and challenging set of requirements that should be considered in the design process, along with cost, performance, power, and so on. The SHIELD methodology addresses security, privacy, and dependability in the context of cyber-physical systems as “built in” rather than as “addon” functionalities, proposing and perceiving with this strategy the first step toward security, privacy, and dependability certi cation for future cyberphysical systems. The SHIELD general framework consists of a four-layered system architecture and an application layer in which four scenarios are considered: (1) airborne domain, (2) railways, (3) biometric-based surveillance, and (4) smart environments. Starting from the current security, privacy, and dependability solutions in cyber-physical systems, new technologies have been developed and the existing ones have been consolidated in a solid basement that is expected to become the reference milestone for a new generation of “security, privacy, and dependability-ready” cyber-physical systems. SHIELD approaches security, privacy, and dependability at four different levels: node, network, middleware, and overlay. For each level, the state of the art in security, privacy, and dependability of individual technologies and solutions has been improved and integrated (hardware and communication technologies, cryptography, middleware, smart security, privacy, and dependability applications). The leading concept has been the demonstration of the composability of security, privacy, and dependability technologies and the composition of security, depending on the application need or the attack surrounding. To achieve these challenging goals, we developed and evaluated an innovative, modular, composable, expandable, and highly dependable architectural framework, concrete tools, and common security, privacy, and dependability metrics capable of improving the overall security, privacy, and dependability level in any specific application domain, with minimum engineering effort. Through SHIELD, we have (i) achieved a de facto standard for measurable security, privacy, and dependability; (ii) developed, implemented, and tested roughly 40 security-enhancing prototypes in response to specific industrial requests; and (iii) applied the methodology in four different domains, proving how generic the approach is. The book’s main objective is to provide an innovative, modular, composable, expandable and high-dependable architectural framework conceived and designed with the SHIELD methodology, which allows to achieve the desired security, privacy, and dependability level in the context of integrated and interoperating heterogeneous services, applications, systems, and devices; and to develop concrete solutions capable of achieving this objective in specific application scenarios with minimum engineering effort. The book is organized in two parts: Section I: SHIELD Technologies and Methodology for Security, Privacy, and Dependability is dedicated to the SHIELD methodology, to technical aspects of new and innovative security, privacy, and dependability technologies and solutions, and to the SHIELD framework. Section II: SHIELD Application Scenarios, New Domains, and Perspectives covers four different application scenarios for SHIELD in the airborne domain, railway domain, biometric security, and smart environments security (smart grid, smart vehicles, smart cities, etc.). This section also describes some domain-independent technology demonstrators and provides an overview of the industrial perspectives of security, privacy, and dependability and of the results obtained by adopting the SHIELD methodology in other European research projects. This book is foreseen for system integrators, software engineers, security engineers, electronics engineers, and many other engineering disciplines involved in the extremely rapidly digitalizing world. But also, managers and policy makers in industry and public administration can make use of it to get awareness on the security challenges of this massive digitalization. The book is intended to be written in a language as plain as possible to reach a wide audience. The goal is to raise awareness on security aspects of the cyber-physical systems that are increasingly being connected to the rest of the world. Systems are often responsible for critical infrastructures that provide the foundations of our modern society. It provides the shortcomings of current approaches, indicates the advances coming from the distributed approach as suggested by SHIELD, and addresses the state of the art in security in various market segments. Finally, it must be acknowledged that Measurable and Composable Security, Privacy, and Dependability for Cyberphysical Systems: The SHIELD Methodology is the result of the two SHIELD projects co-funded by the ARTEMIS Joint Undertaking (https://www.artemis-ju.eu/). Several institutions of different European countries have participated in SHIELD and this book would not have been possible without all the work carried out during all those years by this team of highly professional researchers. The participation by major European industry players in embedded systems security, privacy, and dependability, also made possible the commercial exploitation of the results developed in the SHIELD projects.
Book
As software R & D investment increases, the benefits from short feedback cycles using technologies such as continuous deployment, experimentation-based development, and multidisciplinary teams require a fundamentally different strategy and process. This book will cover the three overall challenges that companies are grappling with: speed, data and ecosystems. Speed deals with shortening the cycle time in R&D. Data deals with increasing the use of and benefit from the massive amounts of data that companies collect. Ecosystems address the transition of companies from being internally focused to being ecosystem oriented by analyzing what the company is uniquely good at and where it adds value.
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The feedback and input of users have been an important part of product innovation in recent years. User input has been studied from different approaches and is applied through different methods in particular phases of the innovation process. However, these methods are not integrated into the whole innovation process and are used only in particular phases or on an ad hoc basis. New developments in technology, social media, and new ways of working closer with customers have opened up new possibilities for firms to gain user input throughout the whole innovation process. However, the impact that these new developments in technology offer for user input innovation in high-tech firms is unclear. Therefore, we study how high-tech firms collect and apply user feedback throughout the whole innovation process. The paper is based on a comparative case study of eight cases in the high-tech industry, in which qualitative data collection was applied. The key contribution of the paper is a conceptual framework on user data-driven innovation throughout the innovation cycle. This framework gives insight into user involvement types and approaches to collect and apply user feedback throughout the innovation process.
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Due to the complex system context of embedded-software applications, defects can cause life-threatening situations, delays can create huge costs, and insufficient productivity can impact entire economies. Providing better estimates, setting objectives, and identifying critical hot spots in embedded-software engineering requires adequate benchmarking data.
European Roadmap Electrification of Road Transport" jointly published by the three ETPs
  • Etps
ETPs, 2017. "European Roadmap Electrification of Road Transport" jointly published by the three ETPs published in 2017. Available at https://www.ertrac.org/index.php?page=ertrac-roadmap
Automated Driving Roadmap, 7 th version
ERTRAC, Automated Driving Roadmap, 7 th version, May 2017, available at https://www.ertrac.org/index.php?page=ertrac-roadmap
Urban mobility Roadmap, final version
ERTRAC, Urban mobility Roadmap, final version, February 2017, available at https://www.ertrac.org/index.php?page=ertrac-roadmap