cyber physical systemsjohanl/educ/ocps/ocps 01 intro.pdf · communication core. ... v2v v2v...

15-Sep-16

Johan J. Lukkien, [email protected]

TU/e Informatica, System Architecture and Networking 1

an introduction and a perspective

Johan Lukkien

Cyber Physical Systems

What is CPS about?

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CPS: Definitions

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• UC Berkeley (May 2015). “Cyber-

Physical Systems (CPS) are integrations

of computation, networking, and physical

processes.” (looks a bit outdated)

• “Cyber-physical systems (CPS) are

next-generation embedded systems

featuring a tight integration of

computational and physical elements.” Annotated concept map from

http://cyberphysicalsystems.org/

ICCPS @ April 2013

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• “Cyber-physical systems

(CPS) are physical and

engineered systems whose

operations are monitored,

coordinated, controlled and

integrated by a computing and

communication core.

• This intimate coupling between the

cyber and physical will be manifested

from the nano-world to large-scale

wide-area systems of systems.

• And at multiple time-scales.”

ICCPS @ May2016

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• “Cyber-physical systems

(CPS) are physical and

engineered systems whose

operations are monitored,

coordinated, controlled and

integrated by (such) computing and communication.

• Broad deployment of cyber-physical systems is

transforming how we interact with the physical world as

profoundly as the world wide web transformed how we

interact with one another, and harnessing their

capabilities holds the possibility of enormous societal and

economic impact.

Some more…

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• “A cyber-physical application is a computer system that processes and

reacts to data from external stimuli from the physical world and make

decisions that also impact the physical world” – J. Sztipanovits. Composition of Cyber-Physical Systems. In: Engineering of Computer-

Based Systems, 2007. ECBS’07

(cited from White et.al;, R&D challenges and solutions for mobile cyber-physical

applications and supporting Internet services, Journal of Internet Services and

Applications, May 2010, Volume 1, Issue 1, pp 45-56)

• “A cyber-physical system (CPS) is a system featuring a tight

combination of, and coordination between, the system’s computational

and physical elements.” – Wikipedia, May 2013

• A CPS is therefore a system composed of physical entities such as

mechanisms controlled or monitored by computer-based algorithms. – Wikipedia, May 2016

So, what is CPS about?

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• Eq. 1: CPS = Computing + Control

– (“Embedded 2.0”)

• Eq. 2: CPS = Embedded Systems + Internet of Things

– (“Embedded meets Ubiquitous Sensing and Communication”)

• Eq. 3: CPS = Embedded Systems + Internet Services

– (“Embedded meets Big Data”, “Virtual embedding”)

• Eq. 4: CPS =

Physical world + Sense/Act. + Control + Computation + Communication + Data

• The novelty is to consider them all together

– a theory of CPS, in which 6 aspects influence each other, are collectively

optimized, enable new applications

– (some) interactions go through the physical world

embedded systems hybrid systems control theory

Using Data 1. Improve subsystem operation

– increased knowledge of the system context from external sources

• weather, environment, …

– evaluate effects of actuation and learn to improve

2. Supply detailed, local data to enable new applications (or improve

existing ones), e.g.,

– internal details of production processes

– wear and tear of internal components

3. Take (increasingly larger) data processing in control loops

– increases in sensor numbers, sampling frequency and sample size

4. (Some) control in the cloud, e.g. cloud based robotics

5. In the end, a CPS ‘knows’ the world beyond sensing and actuating

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Can you give an example?

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Intelligent Transportation Systems

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Vehicles operate using networked ICT

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Local

Control

Local

Control

Local

Control

In-car network

Driver

Control

In-vehicle networks

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• Networks of ECUs

– 40-80 in a modern car

• Designed for

– cooperative behavior

– specialist (remote) management /

diagnostics

• Gateway support for isolation

K-CAN

System

MOST K-CAN

Periphery

SI-BUS

(Byteflight)

PT-CAN

Diagnose

Gateway

BMW 7 series infrastructure

Vehicles become parts of a larger whole

• Vehicle to Vehicle

(V2V) communication

– ad-hoc

– collaborative

applications

• Applications are

distributed

• Actuation may pass

by driver control

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Driver

Control

Driver

Control

Driver

Control

V2V network

Accident prevention

Multiple technologies

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Access Network

GW

RSU RSU

Node Server

OBU

AU

OBU

AU

OBU

AU

HS

Internet

AU Application Unit

OBU On Board Unit

GW Gateway

RSU Road Side Unit

HS Hot spot

In-Vehicle

Domain

Infrastructure

Domain

Ad-Hoc Domain

C2C-CC (Car2Car Communication Consortium initiated by six European car manufacturers) architecture (~2010)

IEEE 802.11p IEEE 802.11a/b/g Other wireless technology (full coverage)

A conceptual view

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15

V2V V2V

Internet, V2I

Congestion control Road maintenance Environment control End-user applications Car sharing Event management

Driver Control

Driver Control

V2V network

Accident prevention

Local Control

Local Control

Local Control

In-car network

Driver Control

(2)

(1)

(4)

(3) • Example data flows:

– (1) gather detailed driving data to

determine

• local weather

• road condition

– (2) accident prevention by direct

intervention

– (3),(4) informing driver

about upcoming road conditions

Other examples (from Dutch rCPS project)

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medical

imaging

chip

fabrication

professional printing

platoonin

g

server farms

subcutaneous sensing

electron

microsco

py

How did CPS evolve?

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CPS is a convergence

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1. High-performance (embedded) real-time control systems

• Integration of control, computation, communication

• Focus on dependability, robustness,

strict (timing) guarantees

2. Deep penetration of sensing and actuation into

the physical world

• Internet of Things – unified protocol and addressing scheme between

any pair of (electronically enriched) objects

– large-scale heterogeneous data processing

• Focus on scalability, connectivity

• Uncertainty as essential ingredient (wireless, unreliable components)

From: Network QoS Management in

Cyber-Physical Systems, Feng Xia et. al

picture by Maurice Heemels

High Performance (embedded) real-time control

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19

applications

platform

network

Reservations, Budgets

Admission test

(temporal) Isolation

Real-time interfaces

Local

Control

Local

Control

Local

Control

In-vehicle network


TU/e Informatica, System Architecture and Networking

Internet of things

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• A unified protocol and naming

scheme between every pair of

devices

• Pervasive, extending network

communication to billions of

endpoints

• Reaching into the physical

world, reaching deep into

(production) systems, gathering

large amounts of detailed

information about states and

events

Single function devices,

sensors, actuators, M2M, 1-many

electronics, ~100B

Laptops, desktops,

phones, 1-1 electronics

~B

Internet ‘Core’,

Web, Data,

Compute Servers

~M

Two worlds

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• Pervasive connectivity moves into the

safety critical domain

– by including actuation

– by penetrating safety critical systems

– uncertainty and concerns of connectivity

and scalability are complemented with

timeliness and dependability

• Safety critical systems (CPS) are

becoming connected

– by including open (and wireless) networks

– robustness and (timing) guarantees are

complemented with scalability and

uncertainty

How are these systems integrated?

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System of systems

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applications

platform

network

Connected domains

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• Applications run on top of

connected (managerial)

domains

– an application takes

resources from that domain

and possibly runs code

– within a domain a single

behavioral policy may be

assumed

– the overall application is

emergent

• The perimeter is not that clear

– the domain can be more

logical than physical

• IoT example: smartphone apps

– take resources from phone,

network, back office cloud

– crossing of managerial domains

by user consent

• sometimes policy, sometimes

just yes/no

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Intelligent Transportation

Systems

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• Vehicle: integrated system components

– systems by themselves, but not

independent

• V2V: system of systems (vehicles) with

V2V applications

– e.g. accident prevention, parking spot

finding, collaborative driving

• V2I: ‘slower’ (higher latency)

applications, global applications

• ITS is a SoS and a typical Cyber

Physical system

Smart grid

Water supply

Industry

Mobility management

Public services

Smart City

Emergency

Well-being

Navigation

Safety

Applic

ations Management of

carbon release

Energy load balancing

Street Lighting

Connected light poles

Outdoor sensing

Surveillance infra

structure

Smart Mobility

Traffic lights

Road side units

Connected vehicles

Telecom

Cellular network

Data services

Smart Building

IT network

Building management infrastructure

Connected luminaires

Wireless sensors

Smart Energy

Smart meter

Renewable energy sources

Another

typical SoS

SoS characteristics: literature

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• Operational Independence

• autonomous behavior and goal

of subsystems

• Managerial Independence

• subsystems managed by

different authorities

• Evolutionary Independence

• Geographic distribution

• Emergent behavior

• properties deriving from the

combination of subsystems

• properties difficult or impossible

to deduce from subsystems

• Heterogeneity

• Networks as integration point

Consequences of composition

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competition of

control - local versus global

architectural

diversity uncorrelated

requirements

Sharing leads to mixed criticality

• Platforms run applications (components) with different criticality

• Components may have an essential and an optional part

• High demands –worst case – might be rare

• Mixed Criticality

– a mixed-critical system is an integrated suite of hardware, operating

system and middleware services and application software that supports

the execution of safety-critical, mission-critical, and non-critical software

within a single, secure compute platform (from http://www.cse.wustl.edu, research agenda for Mixed-Criticality Systems)

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http://www.cse.wustl.edu/

How to engineer (compose) SoS applications?

• E.g. a Smart City application

• Lewis et al.: three step life cycle

1. list all software services in the

concerned subsystems

2. build the integrated SoS

application from these

3. examine and realize the

consequences for the original

subsystems

• e.g. access to data, using

computation and data resources

• Some thing like this has to be

done but

– this assumes a stable

software ‘base’ (independent

evolution!)

– it requires subsystem

managers to be involved

– it invites adjustments of

subsystems to the applications

at hand leading to

maintenance problems • …. reducing dependencies is key

– it is difficult to involve third

party application developers

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Some requirement for a composition

architecture • Separate the following

– development API for application developers (“North side”)

– integration API for subsystems (“South side”)

– adaptation layer for subsystems

• Interfaces include

– reservation of resources (network and others)

– policies, and negotiation thereof

– coordination of SoS applications

• Models are required for

– reasoning about compositions, optimization

– simulations, at varying levels of detail

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INTEGRATION AND COORDINATION

subsystem 2

Adaptation

Policies / Reservation

Control / Data / Code

Application 1 Application 2

subsystem 1

Local App

Service Discovery

Adaptation

Local App

legacy collaboration

Discovery (subsystems, services)

Coordination / Reservation (policies, intra- subsystem)

Control / Data / Code

NORTH API

SOUTH API

What are CPS research challenges and directions?

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Some literature

• Cyber-Physical Systems: A New Frontier – Lui Sha, Sathish Gopalakrishnan, Xue Liu, and Qixin Wang

2008 IEEE International Conference on Sensor Networks, Ubiquitous,

and Trustworthy Computing

• Cyber Physical Systems: Design Challenges – Edward Lee, 2008 11th IEEE International Symposium on Object Oriented Real-

Time Distributed Computing (ISORC)

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Advances: CPS Systems Science

• A robust design flow

– … combining computational and physical aspects

– … with multi-scale dynamics

– … and integrated wired and wireless networking

• Management of systems’ resources, including e.g. mass, energy

and information

– the system ‘knows’ about itself and about aspects of the real world in

some representation

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Advances: Ubiquitous, trustworthy computing

• Adequate abstractions:

– Distributed, real-time computing

– Real-time group communication

– Dynamic topology management in mobile systems

• Programming models

– that include timing and deal with events in time and space

– have real-time and concurrency abstractions

• e.g. bands of simultaneity

– configurable / tunable software components

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Advances: Robustness, safety and security

• Coherent set of metrics that capture uncertainty, faults / error /

failures and security

– …to be included in the software / system design process

– …and in algorithms (e.g. anytime algorithms, event-based control)

– …to admit optimization, both design and run-time

• A philosophy of a small, correct kernel

– formally specified and verified against a sound and complete

environment model

– against which guarantees for (safety-)critical services are given

• Self monitoring and feedback control within software systems

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Anytime algorithms • The highest quality outcome

of subsystems is not always

required

– (response-)time versus

quality trade-off

• An “any-time” algorithm

– provides a valid solution any

time when

it is interrupted

– increases quality over time

• Examples:

– Newton-Raphson

– Trajectory estimation

– Video decoders

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Advances: composition

• Composition of (software) components includes service quality,

dependencies and resource use

– Rich interfaces, including resource specification, dependencies on

environment conditions

– Resource aware and optimized behavior

• Derive correctness and QoS properties from:

– architecture (logical, physical)

– protocols, mappings

– component properties

• Theory of composition

– language support, model-driven (DSL)

– integrating time-triggered and event-triggered

– systems of systems

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Advances: trust, trustworthiness

• (tools to) Visualize and analyze a CPS within its broader context

(social, technical)

• Transparent privacy protection

– concepts, analytical and engineering framework

• Cohesive, conceptual, predictable, transparent from user’s

perspective

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For all this

better models, better tools

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How does CPS relate to networks?

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Use of data needs networks

1. Improve subsystem operation

– increased knowledge of the system context from external sources

• weather, environment, …

– evaluate effects of actuation and learn to improve

2. Supply detailed, local data to enable new applications (or

improve existing ones), e.g.,

– internal details of production processes

– wear and tear of internal components

– sensing of ‘the real world’

3. Take (increasingly larger) data processing in control loops

– increases in sensor numbers, sampling frequency and sample size

4. (Some) control in the cloud, e.g. cloud based robotics

15-Sep-16



Networks CPS • Networks

– form the prime place of integrating subsystems

– are essential ingredients of applications

– are heterogeneous

– connect the ‘physical’ to the ‘Cyber’

• CPS applications are distributed, and there are multiple, e.g.,

– sensors, actuators, control connected by shared networks

– data and management (supervisory control) in the cloud

• Openness, and security for safety and privacy

• Concerns of energy, power, throughput, latency, jitter

– management

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Discussion

15-Sep-16



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