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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732242”.
DEIS - DEPENDABILITY ENGINEERING
INNOVATION FOR CPS
Ran Wei1, Tim Kelly1, Richard Hawkins1, Eric Armenguad2
1University of York
2AVL List GmbH
 DEIS : Dependability Engineering Innovation for CPS
 Funding scheme: H2020 ICT-01-2016
 Total Budge: €4,889,290
 Start Date: January 1st, 2017
 Duration: 3 years
 Consortium: 10 partners
 Coordinator: AVL List
H2020 ICT-01-2016 DEIS
2
 Involves both computer and physical systems and are connected to Internet/local networks
 A mechanism controlled/monitored by computer-based algorithms, tightly integrated with the
internet and its users.
 Tendency: Open and adaptive
 Loosely connected (openness)
 Systems come together to form new system with temporary configuration, and provide service as a whole
(adaptivity)
 Number of configurations: unknown
 Challenge: assuring dependability of CPS
 How well they can function with one another
 How safe they are as a whole
CYBER-PHYSICAL SYSTEMS
3
Addresses assurance of dependability for CPS
 Important and unsolved challenge
Forming a “science of dependable system integration” for CPS
Core concept: Digital Dependability Identity (DDI)
 Composable and executable
 Contains attributes that describe the systems or components dependability
behavior: faults propagation, described using concepts from the theory of safety
contracts
 Contains requirements on how the component interacts with other entities in a
dependable way, described in terms of the level of trust and assurance.
 Produced during design, issued when the component is released, and
continuously maintained over the complete lifetime of a component or system.
DEIS
4
 Automotive: development of a stand-alone system for intelligent physiological
parameter monitoring
 Automotive: Evaluation of automated driving functions and the dependability
impact of connected powertrain on advanced driver simulator
 Railway: Plug-and-play environment for heterogeneous railway systems
enabling dependable exchange of information between components and
subsystems
 Healthcare enhancement of clinical decision app for oncology professional
targeting higher degree of dependability for ad-hoc systems
DEIS: DEPLOYMENT TARGETS
5
Improve Cyber-Physical Systems (CPS) by the
introduction of Digital Dependability Identity (DDI)
a. Efficient synthesis over the supply chain
b. Safe and secure composition of highly distributed
and autonomous CPS
Apply to four use industrial use cases from 3 different
domains
6
DEIS – THE USE CASES
Target: intelligent physiological parameter monitoring for automotive applications to monitor driver comfort
and capability to safely control the vehicle for transition scenarios
Challenge: secure transmission of driver health information
7
USE CASE 1: AUTOMOTIVE DOMAIN
Target: Evaluation of automated driving functions and the dependability impact of connected powertrain on
advanced driver simulator
Challenge: evaluation and integration of cyber-physical systems and systems of systems with a high degree of
autonomy
8
USE CASE 2: AUTOMOTIVE DOMAIN
Target: Dependability framework with heterogeneous (cross-industry) participants
Challenge: interoperability in the area of safety engineering across companies, railway operators as well as
safety authorities
9
USE CASE 3: RAILWAY DOMAIN
Target: Clinical decision support app for oncology professionals
Challenge: higher capability for ad-hoc integration in Electronic Health Records, taking into consideration
security issues
10
USE CASE 4: HEALTHCARE DOMAIN
 Universal exchange of dependability information
 Lack of common model for representing DDIs
 Expressive and machine executable
 Shield sensitive information to protect IP
 Efficient dependability assurance across industries and value chains
 Component providers should be able to generate DDIs based on the dependability
information of the components
 Include information for dependability assurance
 Dependability considered from the early stage: MDE is applicable for its advantages in
automation
 Dependable integration of systems in the field
 Dependability of CPS cannot be fully assured prior to deployment, thus need automated
evaluation of DDIs
DEIS: CHALLENGES FOR DDI
11
 Objective 1: An open model for specifying Digital Dependability
Identities enabling the efficient integration of modular
dependability assurance cases
 Objective 2: Semi-automated framework for the generation and
evaluation of DDIs
 Objective 3: A framework for the in-the-field dependability
assurance in CPS
 Objective 4: autonomous and connected CPS use cases
DEIS TECHNICAL APPROACH – THE DDI
12
13
PROJECT STRUCTURE
WP1 project
management
WP2 project requirements
WP3 model concept
WP4 dependability
collaboration workspace
WP6 Case studies and evaluations
WP7 impact
management –
clustering,
dissemination
and
exploitation
WP5
autonomous
and
connected
CPS
 Definition of a metamodel for DDI: Open Dependability Exchange (ODE)
metamodel
 A semi-automated framework for the generation and evaluation of DDIs
 A framework for the in-the-field dependability assurance in CPS
 Autonomous and connected CPS use cases and evaluations
DEIS: EXPECTED OUTCOMES
14
 Requirement elicitation
 Requirements for the exchange of dependability-related information and the specification of modular
DDIs
 Requirements for the tooling support needed to check the validity of the available dependability
information and to model DDIs
 Requirements for applying the DEIS approach within the automotive, healthcare and the railway domain
and evaluating the project results compared to the state-of-the-art and the state-of-practice
 DDI concept
 Engineering stories
 Progress from University of York
 Implementation of Structured Assurance Case Metamode (SACM) in EMF
 Implementation of Goal Structuring Notation (GSN) in EMF: metamodel, editor, framework
 Model to model transformation from GSN to SACM
 Tool support for SACM and GSN creations
DEIS: CURRENT STATUS
15
 Questions
THANK YOU
16

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DEIS: Dependability Engineering Innovation for Cypber-Physical Systems

  • 1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732242”. DEIS - DEPENDABILITY ENGINEERING INNOVATION FOR CPS Ran Wei1, Tim Kelly1, Richard Hawkins1, Eric Armenguad2 1University of York 2AVL List GmbH
  • 2.  DEIS : Dependability Engineering Innovation for CPS  Funding scheme: H2020 ICT-01-2016  Total Budge: €4,889,290  Start Date: January 1st, 2017  Duration: 3 years  Consortium: 10 partners  Coordinator: AVL List H2020 ICT-01-2016 DEIS 2
  • 3.  Involves both computer and physical systems and are connected to Internet/local networks  A mechanism controlled/monitored by computer-based algorithms, tightly integrated with the internet and its users.  Tendency: Open and adaptive  Loosely connected (openness)  Systems come together to form new system with temporary configuration, and provide service as a whole (adaptivity)  Number of configurations: unknown  Challenge: assuring dependability of CPS  How well they can function with one another  How safe they are as a whole CYBER-PHYSICAL SYSTEMS 3
  • 4. Addresses assurance of dependability for CPS  Important and unsolved challenge Forming a “science of dependable system integration” for CPS Core concept: Digital Dependability Identity (DDI)  Composable and executable  Contains attributes that describe the systems or components dependability behavior: faults propagation, described using concepts from the theory of safety contracts  Contains requirements on how the component interacts with other entities in a dependable way, described in terms of the level of trust and assurance.  Produced during design, issued when the component is released, and continuously maintained over the complete lifetime of a component or system. DEIS 4
  • 5.  Automotive: development of a stand-alone system for intelligent physiological parameter monitoring  Automotive: Evaluation of automated driving functions and the dependability impact of connected powertrain on advanced driver simulator  Railway: Plug-and-play environment for heterogeneous railway systems enabling dependable exchange of information between components and subsystems  Healthcare enhancement of clinical decision app for oncology professional targeting higher degree of dependability for ad-hoc systems DEIS: DEPLOYMENT TARGETS 5
  • 6. Improve Cyber-Physical Systems (CPS) by the introduction of Digital Dependability Identity (DDI) a. Efficient synthesis over the supply chain b. Safe and secure composition of highly distributed and autonomous CPS Apply to four use industrial use cases from 3 different domains 6 DEIS – THE USE CASES
  • 7. Target: intelligent physiological parameter monitoring for automotive applications to monitor driver comfort and capability to safely control the vehicle for transition scenarios Challenge: secure transmission of driver health information 7 USE CASE 1: AUTOMOTIVE DOMAIN
  • 8. Target: Evaluation of automated driving functions and the dependability impact of connected powertrain on advanced driver simulator Challenge: evaluation and integration of cyber-physical systems and systems of systems with a high degree of autonomy 8 USE CASE 2: AUTOMOTIVE DOMAIN
  • 9. Target: Dependability framework with heterogeneous (cross-industry) participants Challenge: interoperability in the area of safety engineering across companies, railway operators as well as safety authorities 9 USE CASE 3: RAILWAY DOMAIN
  • 10. Target: Clinical decision support app for oncology professionals Challenge: higher capability for ad-hoc integration in Electronic Health Records, taking into consideration security issues 10 USE CASE 4: HEALTHCARE DOMAIN
  • 11.  Universal exchange of dependability information  Lack of common model for representing DDIs  Expressive and machine executable  Shield sensitive information to protect IP  Efficient dependability assurance across industries and value chains  Component providers should be able to generate DDIs based on the dependability information of the components  Include information for dependability assurance  Dependability considered from the early stage: MDE is applicable for its advantages in automation  Dependable integration of systems in the field  Dependability of CPS cannot be fully assured prior to deployment, thus need automated evaluation of DDIs DEIS: CHALLENGES FOR DDI 11
  • 12.  Objective 1: An open model for specifying Digital Dependability Identities enabling the efficient integration of modular dependability assurance cases  Objective 2: Semi-automated framework for the generation and evaluation of DDIs  Objective 3: A framework for the in-the-field dependability assurance in CPS  Objective 4: autonomous and connected CPS use cases DEIS TECHNICAL APPROACH – THE DDI 12
  • 13. 13 PROJECT STRUCTURE WP1 project management WP2 project requirements WP3 model concept WP4 dependability collaboration workspace WP6 Case studies and evaluations WP7 impact management – clustering, dissemination and exploitation WP5 autonomous and connected CPS
  • 14.  Definition of a metamodel for DDI: Open Dependability Exchange (ODE) metamodel  A semi-automated framework for the generation and evaluation of DDIs  A framework for the in-the-field dependability assurance in CPS  Autonomous and connected CPS use cases and evaluations DEIS: EXPECTED OUTCOMES 14
  • 15.  Requirement elicitation  Requirements for the exchange of dependability-related information and the specification of modular DDIs  Requirements for the tooling support needed to check the validity of the available dependability information and to model DDIs  Requirements for applying the DEIS approach within the automotive, healthcare and the railway domain and evaluating the project results compared to the state-of-the-art and the state-of-practice  DDI concept  Engineering stories  Progress from University of York  Implementation of Structured Assurance Case Metamode (SACM) in EMF  Implementation of Goal Structuring Notation (GSN) in EMF: metamodel, editor, framework  Model to model transformation from GSN to SACM  Tool support for SACM and GSN creations DEIS: CURRENT STATUS 15