cyber-physical system ensembles: unlocking opportunities when...
TRANSCRIPT
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Cyber-Physical System Ensembles:
Unlocking opportunities when machines collaborate
Pieter J. Mosterman
Chief Research Scientist, Director
MathWorks
Justyna Zander
Adjunct Professor
School of Computer Science, McGill University
MathWorks Research Fellow
Worcester Polytechnic Institute
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Axes of Classification
Configure online Collaborate Infrastructure
Needs, challenges, technological solutions, and the potential impact are classified along three axes: (i) dynamic online
configuration of these systems of systems, (ii) forms of collaboration by the machines, and (iii) infrastructural support for the
collaboration of dynamically configuring system ensembles
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Information
Physics
Electronics
Network
Physics
Information
Electronics
Network
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Configure online
Configure online
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Linearization, implementation model generation, etc.
Generation of models with necessary detail based on property
selection
Proper models in design ►
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Configure online
Virtual system integration
Need Technology
Impact
Challenge
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Linearization, implementation model generation, etc.
Generation of models with necessary detail based on property
selection
Model Building
Automation SystemCounterexample
guided abstraction
refinement
Proper models in design ►
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Virtual system integration
Configure online
![Page 6: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/6.jpg)
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Link for cosimulation,
simulation API, code
generation
Solver configurations for
continuous time, discrete
time, discrete event
Connecting, combining, and
integrating models represented in
different formalisms
Efficient simulation models to be
used across dynamic and execution
semantics
System-level design and analysis by using models ►
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Virtual system integration
Need Technology
Impact
Challenge
Configure online
![Page 7: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/7.jpg)
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Link for cosimulation,
simulation API, code
generation
Solver configurations for
continuous time, discrete
time, discrete event
Connecting, combining, and
integrating models represented in
different formalisms
Efficient simulation models to be
used across dynamic and execution
semantics
Hybrid dynamic
systems
Multiparadigm modeling
System-level design and analysis by using models ►
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Virtual system integration
Configure online
![Page 8: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/8.jpg)
8
Data streaming, target connectivity support, standardized
communication protocols (TCP, UDP), real-time simulation
Open tool platforms with trusted interfaces for communication
across synchronized and coordinated models, software, and
hardware devices
Connectivity among models, software, and hardware
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Virtual system integration
Need Technology
Impact
Challenge
Configure online
![Page 9: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/9.jpg)
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Data streaming, target connectivity support, standardized
communication protocols (TCP, UDP), real-time simulation
Open tool platforms with trusted interfaces for communication
across synchronized and coordinated models, software, and
hardware devices
Connectivity among models, software, and hardware
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Virtual system integration
Real-time simulation
Configure online
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Handling of ensemble
(in)consistency with
sufficient runtime
fidelity
Traceability (direct and
across
transformations)
Runtime variants,
middleware service
description
specification (.srv)
Runtime curve fitting
and design
optimization
Introspection of the
system state,
configuration, and
available services
Online model
calibration
Reasoning and planning adaptation of an ensemble of systems ►
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Runtime system adaptation
Need
Impact
Technology
tem
Challenge
Configure online
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Handling of ensemble
(in)consistency with
sufficient runtime
fidelity
Traceability (direct and
across
transformations)
Runtime variants,
middleware service
description
specification (.srv)
Runtime curve fitting
and design
optimization
Introspection of the
system state,
configuration, and
available services
Online model
calibration
Reasoning and planning adaptation of an ensemble of systems ►
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Runtime system adaptation
Models @ runtime Automated model
calibration
Configure online
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Regression modeling, model selection (artificial neural network,
support vector machine, rational model)
Environment models to enable surrogate interactions
Testing with functionality on deployed systems
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Runtime system adaptation
TechnologyChallengeNeed
Impact
Configure online
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Regression modeling, model selection (artificial neural network,
support vector machine, rational model)
Environment models to enable surrogate interactions
Testing with functionality on deployed systems
Confidently design systems as part of a reliable system
ensemble
Exploit exogeneous functionality for efficient, economical, and
resilient operation
Runtime system adaptation
Testing with surrogate
models
Configure online
![Page 14: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/14.jpg)
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Information
Physics
Electronics
Network
Physics
Information
Electronics
Network
Collaborate
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Collaborate
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Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Emerging behavior design
Planning and synthesis
of distributed control
functionality on
concurrent resources
Concurrency and
platform modeling,
functionality
decomposition, service
composition
Event-driven control,
discrete event
modeling and analysis,
uncertainty modeling
Concurrency
semantics, property
proving with
performance models
Analysis methods
across loosely coupled
architectures
Accessible formal
methods that apply to
collaborative problems
Reasoning and planning adaptation of an ensemble of systems
Need
Impact
Technology
tem
Challenge
Collaborate
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Planning and synthesis
of distributed control
functionality on
concurrent resources
Concurrency and
platform modeling,
functionality
decomposition, service
composition
Event-driven control,
discrete event
modeling and analysis,
uncertainty modeling
Concurrency
semantics, property
proving with
performance models
Analysis methods
across loosely coupled
architectures
Accessible formal
methods that apply to
collaborative problems
Service orchestrationService oriented sensor
programming
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Reasoning and planning adaptation of an ensemble of systemsEmerging behavior design
Collaborate
![Page 17: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/17.jpg)
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Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Data sharing
Communication modeling, double buffering analysis, timing
properties of software, clock recovery
Synchronization of data from incongruent
sources
Multirate architectures ►
TechnologyChallengeNeed
Impact
Collaborate
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Communication modeling, double buffering analysis, timing
properties of software, clock recovery
Synchronization of data from incongruent
sources
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Data sharing Multirate architectures ►
Technology challenges in
CPS
Collaborate
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Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Data sharing
Version based model import/export, metamodel generation, model
concepts sharing and comparing
Information represented as high-level models with well-defined
metamodels and ontologies
Extracting and deriving specific value from general information
TechnologyChallengeNeed
Impact
atio
Collaborate
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Version based model import/export, metamodel generation, model
concepts sharing and comparing
Information represented as high-level models with well-defined
metamodels and ontologies
Megamodeling and
metamodeling
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Data sharing Extracting and deriving specific value from general information
Collaborate
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Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Performance
characterization via
performance models
and measures
Critical path analysis,
code performance
report and advisor
Property based model
slicing, behavioral
analysis, functionality
mining
Adaptive filtering,
distortion modeling,
groundtruthing
(baselining)
Generation of models
for a task by property
identification and
model behavior
selection
Online calibration
based on objective and
performance criteria
Multi-use functionality post-deploymentFunctionality sharing ►
Collaborate
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Performance
characterization via
performance models
and measures
Critical path analysis,
code performance
report and advisor
Property based model
slicing, behavioral
analysis, functionality
mining
Adaptive filtering,
distortion modeling,
groundtruthing
(baselining)
Generation of models
for a task by property
identification and
model behavior
selection
Online calibration
based on objective and
performance criteria
Multi-use functionality post-deployment ►
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Functionality sharing
Data distribution
service (DDS)
Online calibrationRequirements mining
Collaborate
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Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Property and assumption based model slicing, trace to source and
destination, assumptions in functionality to behavior mapping
Assumption formalization and dependency effect analysis
Feature interactionFunctionality sharing
Collaborate
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Property and assumption based model slicing, trace to source and
destination, assumptions in functionality to behavior mapping
Assumption formalization and dependency effect analysis
Feature interaction
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Functionality sharing
Multi-rate double
buffering
Collaborate
![Page 25: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/25.jpg)
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Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Coverage based automatic test generation, variants-based
testing, closed-loop testing
Model-based test generation from requirements while preserving
the context of dynamic configuration
Systematic test suite generation and automated test evaluationCollaborative functionality testing ►
Collaborate
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Coverage based automatic test generation, variants-based
testing, closed-loop testing
Model-based test generation from requirements while preserving
the context of dynamic configuration
Systematic test suite generation and automated test evaluation ►
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Collaborative functionality testing
Testing of dynamic
variability
Collaborate
![Page 27: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/27.jpg)
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Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Collaborative functionality testing
System state restoration, stateless
services, test fixture generation
Time partition testing, functionality
extraction
Setting of initial conditions and
injecting fault data
Temporal and spatial partitioning to
isolate functionality for a specific
system architecture under
investigation
Reproducible test results under minimum uncertainty
Collaborate
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System state restoration, stateless
services, test fixture generation
Time partition testing, functionality
extraction
Setting of initial conditions and
injecting fault data
Temporal and spatial partitioning to
isolate functionality for a specific
system architecture under
investigation
Reproducible test results under minimum uncertainty
Systematically design
systems that are part of a
system ensemble to optimally
realize desired ensemble
behavior
Effectively exploit distributed
information resources for
exclusive system features
Create novel system features
post deployment
Assure the collaboration
quality on shared resources.
Identify and automatically
mitigate root causes of failure
in a distributed environment.
Collaborative functionality testing
Service oriented
architecture testing
Collaborate
![Page 29: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/29.jpg)
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Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Information
Physics
Electronics
Network
Physics
Information
Electronics
NetworkInfr
astr
uctu
re
Infrastructure
![Page 30: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/30.jpg)
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Relations (across abstractions,
formalisms, transformations)
service API, change notification API
Protected models (obfuscated,
encrypted), trusted compiler
Traceability across semantic and
technology adaptation, and
intellectual property protection
Information extraction from
obfuscated intellectual property
Tool coupling among disparate organizations ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Design artifact sharing
Infrastructure
![Page 31: Cyber-Physical System Ensembles: Unlocking opportunities when …msdl.cs.mcgill.ca/people/mosterman/presentations/cps2016/... · 2018. 4. 30. · Cyber-Physical System Ensembles:](https://reader036.vdocuments.us/reader036/viewer/2022071107/5fe1cfcb5e3ad46b0f35b134/html5/thumbnails/31.jpg)
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Relations (across abstractions,
formalisms, transformations)
service API, change notification API
Protected models (obfuscated,
encrypted), trusted compiler
Traceability across semantic and
technology adaptation, and
intellectual property protection
Information extraction from
obfuscated intellectual property
Tool coupling among disparate organizations ►Design artifact sharing
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Open services for
lifecycle collaboration
Infrastructure
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Model generation, pattern (control)
extraction, XML interexchange
Modeling numerical mathematics
(integration, root finding) as
dynamic system
Configurable view projections that
are tool specific
Consistent semantics across tools
by modeling the execution engines
Support manifold views and tools in design
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Design artifact sharing
Infrastructure
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33
Model generation, pattern (control)
extraction, XML interexchange
Modeling numerical mathematics
(integration, root finding) as
dynamic system
Configurable view projections that
are tool specific
Consistent semantics across tools
by modeling the execution engines
Support manifold views and tools in design
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Graph transformationsSingle underlying model
Design artifact sharing
Infrastructure
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34
Communication protocol (building block) modeling, performance
modeling across target hardware
Real-time services of graded quality with a low footprint and a
configurable protocol stack that includes time and location
information
Physically aware configurable protocol stack that is IP compatible►Wireless communication
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Infrastructure
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35
Communication protocol (building block) modeling, performance
modeling across target hardware
Real-time services of graded quality with a low footprint and a
configurable protocol stack that includes time and location
information
Physically aware configurable protocol stack that is IP compatible►Wireless communication
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
IEEE 802.15.4e low cost
communication
Infrastructure
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36
Wireless communication
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Physical layer (RF) modeling,
antenna modeling
Scheduler configuration, dynamic
scheduling with guarantees, mixed
synchronous and asynchronous
behavior
Physical layer based timing and
synchronization architectures
Scheduling of periodic and
aperiodic events with reliable
execution times
Precise timing and synchronization in a distributed environment
Infrastructure
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37
Physical layer (RF) modeling,
antenna modeling
Scheduler configuration, dynamic
scheduling with guarantees, mixed
synchronous and asynchronous
behavior
Physical layer based timing and
synchronization architectures
Scheduling of periodic and
aperiodic events with reliable
execution times
Precise timing and synchronization in a distributed environmentWireless communication
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
IEEE 1588 precise timing Distributed real-time
systems task scheduling
Processor and
network scheduling
Infrastructure
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38
Virtual machine (LLVM, JVM, Docker) with real-time capabilities,
serialized intermediate representation of functionality
Standardized and configurable real-time execution stack
Flexible and transferable embedded functionality dispatch ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
Infrastructure
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39
Virtual machine (LLVM, JVM, Docker) with real-time capabilities,
serialized intermediate representation of functionality
Standardized and configurable real-time execution stack
Flexible and transferable embedded functionality dispatch ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
Real-time virtualizationOpen Services Gateway
Initiative (OSGi)
Infrastructure
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40
Combine analytic and experimental target profiling (processor,
hardware, FPGA -in-the-loop), interpolation estimates from
historical data
Platform-based modeling of execution behavior functionality
Performance characterization from abstract functionality ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
Infrastructure
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41
Combine analytic and experimental target profiling (processor,
hardware, FPGA -in-the-loop), interpolation estimates from
historical data
Platform-based modeling of execution behavior functionality
Performance characterization from abstract functionality ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
Platform-based design
Infrastructure
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42
Static analysis methods, automatic test generation, hardware
architecture behavior implementation
Characterization of computational architectures
Determination of key test cases for different implementations ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
Infrastructure
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43
Static analysis methods, automatic test generation, hardware
architecture behavior implementation
Characterization of computational architectures
Determination of key test cases for different implementations ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
IEEE 754 floating point
denormals
Infrastructure
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Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
Harmonic periods (integer),
synchronous (single clock, discrete
time), simultaneous, dense
(variable step, continuous)
Certification kit for mixed Safety-
Integrity Levels (SiL) of
components, matching software
with hardware
Modeling the semantics of timeDynamically mixing safety integrity
levels
Safety of heterogeneous system ensembles
Infrastructure
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45
Harmonic periods (integer),
synchronous (single clock, discrete
time), simultaneous, dense
(variable step, continuous)
Certification kit for mixed Safety-
Integrity Levels (SiL) of
components, matching software
with hardware
Modeling the semantics of timeDynamically mixing safety integrity
levels
Safety of heterogeneous system ensembles
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Hardware resource sharing
Safety analysis at an
architectural level
Infrastructure
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46
Service discovery response time (latency, averages, time-out),
modal service request behavior
Real-time middleware and service oriented architectures with
physical capabilities
Real-time embedded services operating in a physical environment►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Service utilization
Infrastructure
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47
Service discovery response time (latency, averages, time-out),
modal service request behavior
Real-time middleware and service oriented architectures with
physical capabilities
Real-time embedded services operating in a physical environment►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Service utilization
Real-time discovery
services
Real-time middleware
Infrastructure
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48
Type similarity checking and conversion, semantics definition
Service ontologies with taxonomies for similarity and
transformability matching
Smart services discovery ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Service utilization
Infrastructure
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49
Type similarity checking and conversion, semantics definition
Service ontologies with taxonomies for similarity and
transformability matching
Smart services discovery ►
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Service utilization
Semantic middleware
Infrastructure
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Reliable model and code generation
Language and ontology infrastructure to support translation and
transformation
Information sharing in a heterogeneous system ensemble
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Service utilization
Infrastructure
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51
Reliable model and code generation
Language and ontology infrastructure to support translation and
transformation
Information sharing in a heterogeneous system ensemble
Conveniently, efficiently, and
consistently collaborate
between stakeholders
throughout the system life
cycle
Reliably configure flexible
system configurations for
features with varying quality
of service
Contract out system
resources and balance use of
external resources for
resiliency and runtime cost
optimization
Dynamically assemble
systems post-deployment
and purpose available
functionality to serve singular
needs
Service utilization
Semantic web
ontology language
Semantic anchoring
Infrastructure
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52
Pieter J. Mosterman and Justyna Zander, “Cyber-physical
systems challenges: a needs analysis for collaborating
embedded software systems,” in Software & Systems
Modeling, Springer Berlin/Heidelberg, ISSN 1619-1366, vol.
15, nr. 1, pp. 5-16, 2016
https://dl.acm.org/citation.cfm?id=2890224 https://www.youtube.com/watch?v=oofHMaEWwP8
The Smart Emergency Response System
Using MATLAB and Simulink
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Environment models to enable surrogate interactions
Open tool platforms with trusted interfaces for communication
across synchronized and coordinated models, software,
and hardware devices
Efficient simulation models to be used across dynamic and execution semantics
Online model calibration
Maintenance of consistent information and management of inconsistencies regarding the ensemble of systems with sufficient fidelity at runtime
Introspection of the system state, configuration, and services it makes available
Generation of models with necessary detail based on property selection
Connecting, combining, and integrating models represented in different formalisms
Needs Challenges Technology ImpactD
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Model-based test generation from requirements while preserving the context of dynamic configurations
Setting of initial conditions and injecting fault data
Temporal and spatial partitioning to isolate
functionality for a specific system architecture under
investigation
Information represented as high-level models with well-defined
metamodels and ontologies
Synchronization of data from incongruent sources
Assumption formalization and dependency effect analysis
Online calibration based on objective and performance criteria
Performance characterization via performance models and measures
Generation of models for a desired task perspective by property identification and model behavior selection
Accessible formal methods that apply to collaborative problems
Planning and synthesis of distributed control functionality on concurrent resources
Analysis methods across loosely coupled architectures
Needs Challenges Technology Impact
Pie
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J. M
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Real-time middleware and service oriented architectures with physical capabilities
Service ontologies with taxonomies for similarity and transformability
matching
Language and ontology infrastructure to support
translation and transformation
Scheduling of periodic and aperiodic events with reliable
execution times
Physical layer based timing and synchronization architectures
Real-time services of graded quality with a low footprint and a configurable protocol stack that
includes time and location information
Characterization of computational architectures
Dynamically mixing safety integrity levels
Modeling the semantics of time
Platform-based modeling of execution behavior functionality
Standardized and configurable real-time execution stack
Traceability across semantic and technology adaptation, and intellectual property protection
Information extraction from obfuscated intellectual property
Configurable view projections that are tool specific
Consistent semantics across tools by modeling execution engines
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