1st disim workshop on engineering cyber-physical systems

Fon any information please contact Alessandro D’Innocenzo – [email protected] -

or Henry Muccini - [email protected]

1ST DISIM WORKSHOP ON

ENGINEERING CYBER-PHYSICAL SYSTEMS

TUESDAY 26, JANUARY 2016, 2:00 PM

MEETING ROOM 2.3, II FLOOR, COPPITO 1

UNIVERSITY OF L’AQUILA, ITALY

PROGRAM

14:00 - Alessandro D’Innocenzo & Henry Muccini - Welcome & Introduction to CPS

14:20 - Alessandro D’Innocenzo - Modeling and Co-design of Control Tasks over Wireless

Networking Protocols: State of the Art and Challenges

14:40 - Giordano Pola – Formal methods for analysis and control of CPS

15:00 - Elena De Santis - Safe Communication in Power Systems: application to a DC microgrid

control - Safe Human-Inspired Model for Vehicle Control

15:20 – Henry Muccini – Architecting (Self-Adaptive) Cyber-Physical Systems: a View on the State of

the Art

15:40 - Luigi Pomante: Electronic Design Automation & Embedded Systems Development

16:00 - Stefania Costantini - Agent-based hybrid architecture for Smart Cyber-Physical Systems and

applications to eHealth

16:20 - Discussion

Alessandro D’Innocenzo, Henry Muccini [email protected]

[email protected]

Dept. of Information Engineering, Computer Science and Mathematics

University of L’Aquila, Italy

DEWS

Centre of Excellence on Design Methodologies of Embedded Controllers,

Wireless Interconnect and Systems-on-chip - University of L’Aquila, Italy

SEA Group

The Next Computing Revolution

Mainframe computing (60’s – 70’s)

Large computers to execute big data processing applications

Desktop computing & Internet (80’s – 90’s)

One computer at every desk to do business/personal activities

Ubiquitous computing (00’s)

Numerous computing devices in every place/person

Millions for desktops vs. billions for embedded processors

Cyber Physical Systems (10’s)

SEA Group

What are Cyber Physical Systems?

Cyber-Physical Systems (CPS) as ``engineered systems that are

built from, and depend upon, the seamless integration of

computational and physical components” [NSF12]

Cyber-Physical Systems (CPS) are integrations of computation

with physical processes. Embedded computers and networks

monitor and control the physical processes, usually with

feedback loops where physical processes affect computations

and vice versa [Lee08]

A cyber-physical system (CPS) is a system of collaborating

computational elements controlling physical entities

[Wikipedia].

SEA Group

HW/SW

component

HW/SW

component

HW/SW

component

HW/SW

component

HW/SW

component

Monitor and

controlAffect

Feedback loop

Collaborate

SEA Group

Different names for same things…

Cyberphysical Systems (CPS),

Networked Embedded Systems,

SCADA,

Swarm Robotics,

Drone Sensor Networks,

Internet of Things (IoT),

Wireless Sensor Networks (WSN),

SEA Group

Main characteristics

- Networked embedded components

- Feedback loop

- Adaptable, re-configurable, dynamic

- Distributed control

SEA Group

CPS versus Embedded Systems

CPS represents an evolution of embedded systems,

where components are immersed in and interacting

with the physical world

CPS has to satisfy new requirements, such as

continuous evolution and adaptability, due to the

computational complexity, distribution and system

adaptability of those systems.

SEA Group

Example #1 (taken from Luca Mottola slides)

SEA Group

Example #2: self-driving cars

SEA Group

Example #3: smart buildings

INCIPICT SER2: Building automation systems:

Motivations

Physical modeling, automatic

control, communication:

Cyber-Physical Systems

Rule Based DR

Model Based DR

Data-Driven DR

Building automation systems: SoA

Courtesy of Madhur Behl

SEA Group

CPS versus Networked Systems

CPS represents an evolution of networked control

systems, where physical systems and controllers

interact via a communication system

CPS inherit from NCS challenges on distributed control

and dynamic reconfiguration

Networked Control Systems

Plantu y

x

• Let a plant model be given by input/output/internal variables and

differential/difference equations, e.g.:

� � + 1 = �� + �� , � = ��(�)


Plantu y

x



� � + 1 = �� + �� , � = ��(�)

• Let some specifications be given on the desired behavior of the variables,

e.g. stability or some temporal logic formula


PlantControlleru y

x



� � + 1 = �� + �� , � = ��(�)



• Design a controller such that the closed-loop interconnection satisfies the

specifications, e.g.

ℎ � + 1 = �ℎ � + � � , u � = �ℎ(�)


PlantController



� � + 1 = �� + �� , � = ��(�)



• Design a controller such that the closed-loop interconnection satisfies the

specifications, e.g.

ℎ � + 1 = �ℎ � + � � , u � = �ℎ �

• What if plant and controller exchange data via a communication network?R. Alur, A. D'Innocenzo, K.H. Johansson, G.J. Pappas, G. Weiss. Compositional Modeling and Analysis of Multi-Hop

Control Networks. IEEE Transactions on Automatic Control, Special Issue on Wireless Sensor and Actuator

Networks, full paper, 56(10):2345-2357, 2011.

u y

xComm.

Network

SEA Group

Bibliography

[NSF12] National Science Foundation, Cyber-Physical

Systems Program Solicitation NSF 13-502, October

2012

[Lee08] Edward A. Lee. Cyber Physical Systems: Design

Challenges.Technical Report No. UCB/EECS-2008-8,

January 23, 2008

SEA Group

ModelingandCo-designofControlTasksoverWirelessNetworkingProtocols:

StateoftheArtandChallenges

AlessandroD’Innocenzo

1st DISIMWorkshoponEngineeringCyberPhysicalSystemsJanuary26,2016– UniversityofL’Aquila

Objective1:Robust&securedesignofcontroltasksoverwirelesscommunicationprotocols

Objective2:Co-simulationandemulationofcontrolalgorithms,communicationprotocolsandphysicalsystems

• Formalcompositionalinterfacesbetweencontrolalgorithmsandwirelesscommunicationprotocols

• Quantifyimpactofwirelessnetworkingoncontrolperformance• Robustnesswithrespecttopacketlossesanddelays• Resiliencewithrespecttofailuresandmaliciousintrusions• Formalverificationtoolsandco-simulationenvironments

Goal:todevelopnovelmethodsforco-designofcontrolalgorithmsandcommunicationprotocolconfiguration

Method:Interdisciplinaryresearchacrossthe“3C”:controltheory,computerscienceandcommunicationtheory

Output:novelmethodsthatimproveperformanceandsecurityoftechnologicalsolutionsforwirelessautomationsystems

Contactinfo:[email protected]

Controltask

Plantu y

x

• Let aplantmodelbegiven byinput/output/internal variables anddifferential/difference equations,e.g.:

𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘)

Controltask

Plantu y

x


𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘)• Let somespecifications begiven onthedesired behavior ofthevariables,

e.g.stability orsometemporal logic formula

Controltask

PlantControlleru y

x



e.g.stability orsometemporal logic formula• Designacontrollersuch that theclosed-loop interconnectionsatisfies the

specifications,e.g.ℎ 𝑘 + 1 = 𝐸ℎ 𝑘 + 𝐹𝑦 𝑘 , u 𝑘 = 𝐺ℎ(𝑘)

Controltask

PlantController



e.g.stability orsometemporal logic formula• Designacontrollersuch that theclosed-loop interconnectionsatisfies the

specifications,e.g.ℎ 𝑘 + 1 = 𝐸ℎ 𝑘 + 𝐹𝑦 𝑘 , u 𝑘 = 𝐺ℎ 𝑘

• What if plant andcontrollerexchangedataviaawirelessnetwork?R.Alur,A.D'Innocenzo,K.H.Johansson, G.J.Pappas,G.Weiss. Compositional Modeling andAnalysis ofMulti-HopControlNetworks.IEEETransactions onAutomatic Control, SpecialIssue onWireless SensorandActuatorNetworks,fullpaper,56(10):2345-2357, 2011.

u y

xWirelessNetwork

ChallengeswithWiredControlNetworks

Wires areexpensive• Wires as well as installationcosts• Wire/connectorwear andtear

Lack offlexibility• Wires constrain sensor/actuatormobility• Limitedreconfigurationoptions

Restricted controlarchitectures• Centralizedcontrolparadigm

Paradigmshifttowardswirelesscontrolarchitectures

“Removing cables undoubtedly saves cost, but often the real cost gains lie in the radicallydifferent design approach that wireless solutions permit. […] In order to fully benefit fromwireless technologies, a rethink of existing automation concepts and the complete designand functionality of an application is required.” Jan-Erik Frey, R&D Manager ABB

WirelessControlNetworkA collection of cooperating algorithms (controllers) designed to achievea set of common goals, aided by interactions with the environmentthrough distributed measurements (sensors) and actions (actuators)exchanged via a wireless communication network

ApplicationsofWirelessControlNetworks

Industrialautomation

EnvironmentalMonitoring,Disaster Recovery andPreventiveConservation

SupplyChainandAssetManagement

PhysicalSecurityandControl

OpportunitiesvschallengeswithWirelessControlNetworksLowercosts,easierinstallation• SuitableforemergingmarketsBroadensscopeofsensingandcontrol• Easiertosense/monitor/actuate:opensnewapplicationdomainsCompositionality• Enablessystemevolutionviacomposable controlloopsRuntimeadaptationandreconfiguration• Controlcanbemaintainedinresponsetofailuresandmaliciousattacks

Complexity• Systemsdesignersandprogrammersneedsuitableabstractionstohidethe

complexityfromwirelessdevicesandcommunicationprotocolsReliability• Needforrobustandpredictablebehaviordespitewirelessnon-idealitiesSecurity• Wirelesstechnologyisvulnerable:securitymechanismsforcontrolloops

Takeintoaccountcommunicationprotocolbehavior!

ISO/OSImodelfor(wireless)communicationprotocols

Application

Session

Presentation

Transport

Network

Data/Link

Physical

Application

Session

Presentation

Transport

Network

Data/Link

PhysicalWirelesslink))) (((

• Opensystemsinterconnection(OSI)modelseparatesfunctionalelementsofanetworkintosevenlayers

HostA HostB


Application

Session

Presentation

Transport

Network

Data/Link

Physical

Application

Session

Presentation

Transport

Network

Data/Link


Interference,datalosses,delays,limited

energy,channelcapacity,failures,

maliciousintrusions

Coding,modulation,tx power

Scheduling,accesstothewireless

channel

Routingstrategy


• OSImodelhasallowedrefinementofeachlayerindependently

Skype,youTube…

TCP,UDP

HostA HostB


Application

Session

Presentation

Transport

Network

Data/Link

Physical

Application

Session

Presentation

Transport

Network

Data/Link



• OSImodelhasallowedrefinementofeachlayerindependently• Eachlayeronlytalkswiththecorrespondinglayer…byexchangingpacketswith

thelayersabove&below

HostA HostB

Classicalcontrolloop

𝑢 𝑘 = 𝑓(𝑦 𝑘 )

Application

Session

Presentation

Transport

Network

Data/Link

Physical

Application

Session

Presentation

Transport

Network

Data/Link


S1

• Communicationstackandmediumistransparenttothecontrolalgorithm

A1 A2

RobustandFault-tolerantControl

𝑢5 𝑘 𝑢6 𝑘y 𝑘

𝑢5 𝑘 𝑢6 𝑘 y 𝑘

Plantcontrollaw

Controlloopoverawirelessnetwork

𝐮 𝐤 = 𝐟(𝐲 𝐤 )

Session

Presentation

Transport

Network

Data/Link

Physical

Sensing/actuation

Session

Presentation

Transport

Network

Data/Link


• Sensingandactuationdataarerelayedviatheprotocolstacklayers

S1A1 A2

Controlloops overawirelessnetwork

A1 A2 S1

𝐮 𝐤 = 𝐟(𝐲 𝐤 )

Session

Presentation

Transport

Network

Data/Link

Physical

Sensing/actuation

Session

Presentation

Transport

Network

Data/Link


Plantcontrollaw

• Sensingandactuationdataarerelayedviatheprotocolstacklayers• Severalfeedbackcontrolmechanismswithinseparatecommunicationlayers

TCPcongestioncontrol

Routingcontrol

Mediumaccesscontrol

Power,coding&modulationcontrol

Intra-layer controlloops

Controlloops overareal wirelessnetwork

Wirelessnetwork


Wirelessnetwork

Borderlinebetweencontrolover networkandcontrolof networkdisappears

M.C.Escher,RelativityLithograph,1953


Wirelessnetwork

Borderlinebetweencontrolover networkandcontrolof networkdisappears

M.C.Escher,RelativityLithograph,1953

Differentperspectivesintermsof• Time-scales

• Mathematicalsetting• Performancemetrics

• Constraints&non-idealities

HandlecomplexityofCPSviahybridsystemstheoryJ.Lygeros,S.Sastry,C.J.Tomlin.Agametheoreticapproachtocontrollerdesignforhybridsystems.InProc.

OfIEEE88(7):949-970,July2000• DiscreteVariables:

– Heateroff:q0– Heateron:q1

• ContinuousVariables:– Roomtemperature:x

• Transitions:– TurnheaterONwhenthetemperatureissmallerthan70degrees:x≤70.– TurnheaterOFFwhenthetemperatureisgreaterthan80degrees:x≥80.

• Analysisandcontrolofhybridsystemsviaformalmethods:– Discretizestatespace:Pola etal.[…]– Discretizetrajectories:YiDeng,A.D'Innocenzo,M.D.DiBenedetto,S.DiGennaro,A.A.Julius.

VerificationofHybridAutomataDiagnosability withMeasurementUncertainty.IEEETransactionsonAutomaticControl

Challenge:Co-designthecontrolalgorithmandthecommunicationprotocol

Controller

Application

Session

Presentation

Transport

Network

Data/Link

Physical

HandlecomplexityofCPSviatailoredmodelinganddesign

Co-designovertime-triggeredcommunicationprotocols

Challenge:Co-designthecontrolalgorithmandthecommunicationprotocol(scheduling,routingandcontrol)

Controller

Application

Session

Presentation

Transport

Network

Data/Link

Physical

WirelessHART MAC(scheduling) andNetwork(routing) layers

§ Time-triggered access tothechannel§ Timedivided inperiodic frames§ Each framedivided inΠ timeslots ofduration Δ

27


§ Time-triggered access tothechannel§ Timedivided inperiodic frames§ Each framedivided inΠ timeslots ofduration Δ

28


§ Time-triggered access tothechannel§ Timedivided inperiodic frames§ Each framedivided inΠ timeslots ofduration Δ§ Enablesredundancyindatarouting

29



30



31


§ Time-triggered access tothechannel§ Timedivided inperiodic frames§ Each framedivided inΠ timeslots ofduration Δ§ Enablesredundancyindatarouting§ Schedulingmustguaranteerelayviamultiplepaths

32



33



34



Protocolsdesignedfor“slow”controltasks:exploitredundancytouseiton“fast”controltasks

Redundancyindatarouting…

§ …makessystemtoleranttolong-termlinkfailures

§ …enablesdetectionandisolationoffailuresandmaliciousattacks

§ …makessystemrobusttoshort-termlinkfailures(e.g.packetlosses)


Closeacontrolloopinvestigatingtworoutingstrategies:1. Single-pathdynamicrouting:switchingbehaviorduetodynamicrouting2. Multi-pathstaticrouting: algorithmstomergeredundantdata




Wirelesscontrolnetworksas switching systems

𝐾(𝑡)


𝑡

𝐾(𝑡)


t+1

𝐾(𝑡)


t+…

𝐾(𝑡)

Different paths are associatedwith different delays.


𝑡+…

𝐾(𝑡)

𝐴 =

𝐴I 𝐵I 00 0 𝐼⋮ ⋮ ⋮

⋯ 0 0⋯ 0 0⋱ ⋮ ⋮

0 0 00 0 00 0 0

⋯ 𝐼 0⋯ 0 𝐼⋯ 0 0

𝐵 𝜎 𝑡 =

𝐵𝛿Q R ,S𝐼𝛿Q R ,5

⋮𝐼𝛿Q R ,TU6𝐼𝛿Q R ,TU5𝐼𝛿Q R ,T

Different paths are associatedwith different delays.Mathematical model: 𝑥 𝑡 + 1 = 𝐴𝑥 𝑡 + 𝐵 𝜎 𝑡 𝑣 𝑡 , 𝑡 ∈ ℕ, where 𝑥 𝑡 is the plantand network state, 𝜎 𝑡 ∈ Σdepends on routing/scheduling. The switching signal isconsidered as a disturbance.


𝑡+…

Problem:Designacontroller𝐾(𝑡) s.t.theclosedloopsystemisasymptoticallystable.Given astate-feedbackstatic controller𝐾(𝑡),theclosed loop systems is asymptoticallystable iff theJointSpectral Radius of 𝐴 + 𝐵 𝜎 𝑡 𝐾 𝑡 Q R ∈Z is smaller than 1.

Insights:Switchingsystemsanalysisanddesignisacrowdedresearcharea:• Leveragespecialstructureofmatrices𝐴 and𝐵 𝜎 𝑡 toprovidetailoredresults

thatoutperformclassicalresultsongeneralswitchingsystems

𝐾(𝑡)

Different paths are associatedwith different delays.Mathematical model: 𝑥 𝑡 + 1 = 𝐴𝑥 𝑡 + 𝐵 𝜎 𝑡 𝑣 𝑡 , 𝑡 ∈ ℕ, where 𝑥 𝑡 is the plantand network state, 𝜎 𝑡 ∈ Σdepends on routing/scheduling. The switching signal isconsidered as a disturbance.


R. M. Jungers, A. D'Innocenzo, M. D. Di Benedetto. Modeling, analysis and design of linear systems withswitching delays. IEEE Transactions on Automatic Control, to appear.A. Cicone, A. D'Innocenzo, N. Guglielmi, L. Laglia. A sub-optimal solution for optimal control of linearsystems with unmeasurable switching delays. 54th IEEE Conference on Decision and Control, Osaka,Japan, December 15-18, 2015.R. M. Jungers, A. D'Innocenzo, M. D. Di Benedetto. Further results on controllability of linear systemswith switching delays. 9th IFAC World Congress, Cape Town, South Africa, August 24-29, 2014.R. M. Jungers, A. D'Innocenzo, M. D. Di Benedetto. How to control Linear Systems with switching delays.13th European Control Conference (ECC14), Strasbourg, France, June 24-27, 2014.R.M. Jungers, A. D'Innocenzo, M.D. Di Benedetto. Feedback stabilization of dynamical systems withswitched delays. 51st IEEE Conference on Decision and Control, Maui, Hawaii, December 10-13 2012.

𝐾(𝑡)


Closeacontrolloopinvestigatingtworoutingstrategies:1. Single-pathdynamicrouting: takeintoaccountswitchingbehaviordueto

dynamicrouting2. Multi-pathstaticrouting: takeintoaccountalgorithmstomergeredundantdata




Multi-pathstaticrouting




Investigatealgorithmstomergeredundantdata:• Objective:stabilizetheclosed-loopsystem• Beststrategy:keepmostrecentpacketvs.computecombination?• Differentpathsareassociatedwithdifferentdelays• Notatrivialquestion,beststrategyfromthepointofviewofstabilitystrongly

dependsonplantandnetwork:needforacontrol-theoreticapproach

Syntax:§ Linear plant

𝒫 = (𝐴, 𝐵, 𝐶)

MIMOMCNmodel

Syntax:§ Linear plant§ Weight function 𝑊 determines data processing through the network -

reminiscent of network coding

𝐺ℛ = 𝑉ℛ,𝐸ℛ,𝑊ℛ𝑊ℛ_: 𝐸ℛ → ℝ 𝑖 = 1,⋯ ,𝑚

𝒫 = (𝐴, 𝐵, 𝐶) 𝐺𝒪 = 𝑉𝒪,𝐸𝒪,𝑊𝒪𝑊𝒪_: 𝐸𝒪 → ℝ 𝑖 = 1,⋯ , 𝑙

MIMOMCNmodel


reminiscent of network coding§ Communication scheduling 𝜂 assigns transmission of nodes


𝜂ℛ_: 1, … , Π → 2jℛ

𝐺𝒪 = 𝑉𝒪,𝐸𝒪,𝑊𝒪𝑊𝒪_: 𝐸𝒪 → ℝ 𝑖 = 1,⋯ , 𝑙

𝜂𝒪_: 1, … , Π → 2j𝒪

𝒫 = (𝐴, 𝐵, 𝐶)

MIMOMCNmodel


reminiscent of network coding§ Communication scheduling 𝜂 assigns transmission of nodes§ Model at time scale of frames instead of time-slots (no switching behavior)

𝑇 = ΠΔ


𝜂ℛ_: 1, … , Π → 2jℛ

𝐺𝒪 = 𝑉𝒪,𝐸𝒪,𝑊𝒪𝑊𝒪_: 𝐸𝒪 → ℝ 𝑖 = 1,⋯ , 𝑙

𝜂𝒪_: 1, … , Π → 2j𝒪

𝒫 = (𝐴, 𝐵, 𝐶)

MIMOMCNmodel

Resilientcontrol

𝐹 setofall configurations oflinks subject toafailureoramalicious intrusion

Resilientcontrol

Mf


Resilientcontrol

Mf


§ Benefit: Do not reconfigure the whole network (i.e. scheduling and routing) whena failure occurs: instead, only reconfigure neighbors of faulty nodes

§ Benefit: Do not add complexity to local communication to detect faulty ormalicious nodes: instead, use plant dynamics and path redundancy

§ Technical challenge: Exploit graph theory and control-theoretic approaches formodel-based failure detection and isolation

BANK OF LUENBERGER OBSERVERS

𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 ,𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ]

Observer-based diagonalFDIproblem

BANK OF LUENBERGER OBSERVERS

𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 ,𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ]

Observer-based diagonalFDIproblem

Derive a common mathematical model for network topology (graph) and plant (LTI system):exploit structured systems theory that translates LTI system into a graph

Resilientcontrol

A. D'Innocenzo, F. Smarra, M.D. Di Benedetto. Fault Tolerant Control of MIMO Multi-Hop Control Networks.Automatica, full paper, to appear.A. D'Innocenzo, F. Smarra, M. D. Di Benedetto. Further results on fault detection and isolation of maliciousnodes in Multi-hop Control Networks. 14th European Control Conference (ECC 2015), Linz, Austria, July 15-17, 2015. Best application paper award.M.D. Di Benedetto, A. D'Innocenzo, F. Smarra. Fault-tolerant control of a wireless HVAC control system.ISCCSP2014, 2014A. D'Innocenzo, M.D. Di Benedetto, F. Smarra. Fault detection and isolation of malicious nodes in MIMOMulti-hop Control Networks. 52nd IEEE CDC, 2013F. Smarra, A. D'Innocenzo, M.D. Di Benedetto. Fault Tolerant Stabilizability of MIMO Multi-Hop ControlNetworks. 3rd IFAC NecSys 2012A. D'Innocenzo, M.D. Di Benedetto, E. Serra. Fault Tolerant Control of Multi-Hop Control Networks. IEEETransactions on Automatic Control, 58(6):1377-1389, 2013.

ChallengesinWirelessControlNetworks

Modeling• Formalinterfacesbetweencontrolalgorithmsandwirelesscommunicationprotocols• Compositional modelsforscalableanalysisanddesignofmultiplecontrol loopsAnalysis• Quantify impactofwirelessnetworking oncontrolperformanceDesign• Controllerdesignincorporating wirelessnetworkproperties• Control-networkco-designRobustness• Robustwithrespecttopacketlossesanddelays• Tolerantwithrespecttofailuresandmaliciousintrusions– CPSSecurity(SafeCOP)Tools• Formalverificationandautomatic(co-)designofsafe&secureWCN(SafeCOP)• Co-simulationofcontrolalgorithms,communicationprotocolsandphysicalsystemsExperimentalset-up• WirelessHART laboratory(INCIPICT+SafeCOP)• Buildingautomation laboratory(INCIPICT)

Formal Methods for the Analysis and Control

of Cyber-Physical Systems

Giordano Pola

Department of Information Engineering,

Computer Science and

Mathematics,

Center of Excellence DEWS,

University of L’ Aquila, Italy

[email protected]

mailto:[email protected]

At DISIM & DEWS:

Marika Di Benedetto

Pierdomenico Pepe

Elena De Santis

Costanzo Manes

Outside:

Paulo Tabuada (UCLA, USA)

Karl Henrik Johansson (KTH, Sweden)

Arjan J. van der Schaft (University of Groningen, The Netherlands)

Antoine Girard (Universite’ Joseph Fourier, France)

Alessandro Borri (IASI-CNR, Italy)

Majid Zamani (TU Munich, The Netherlands)

Manuel Mazo (TU Delft, The Netherlands)

Acknowledgments: 01/13

Collaborations

02/13

http://CyberPhysicalSystems.org

Cyber Physical Systems (CPS) – a concept map

03/13

Network of plants Pi and computing units Ci

communicating via

non-ideal communication infrastructures

Our model of CPS

P1 P2 PN

C1 C2 CN

Our model of CPS 04/13

P1 P2 PN

C1 C2 CN

Plants:

nonlinear control systems with possible disturbances and

time-varying (states and inputs) delays

dx(t) / dt = f (x(t),x(t-x(t)),u(t-u(t)),d(t))

: Pi


P1 P2 PN

C1 C2 CN

Computing Units:

Labelled transition systems

T = (Q, Q0, L, ,O,H)

: Ci


Non-idealities in communication infrastructures:

Quantization errors

Bounded time-varying network access times

Bounded time-varying communication delays

Limited bandwidth

Bounded number of packet losses

P1 P2 PN

C1 C2 CN

:

07/13

Goals:

Synthesis of correct-by-design embedded control software

enforcing complex specifications

Detection of faults and/or criticalities in safety-critical CPS

Our model of CPS

P1 P2 PN

C1 C2 CN

Approach based on a three phases process:

#1. construct the finite/symbolic model T of the plant system

#2. design a finite/symbolic controller C that solves the specification S for T

#3. design a controller C’ for on the basis of C

Advantages:

Integration of software and hardware constraints in the control design of purely

continuous processes

Use of computer science techniques to address complex logic specifications

Correct-by-design embedded control software

Symbolic domain

Physical domain

Plant: Continuous or Hybrid system

Symbolic model Finite controller Software & hardware

Hybrid controller

08/13

stable control systems

[Automatica-2008]

stable switched systems

[IEEE-TAC-2010]

stable time-delay systems

[SCL-2010]

stable time-varying

delay systems

[IJRNC-2014]

[IJC-2012]

unstable control

systems

[IEEE-TAC-2012]

efficient control

algorithms

[IEEE-TAC-2012]

approximate bisimulation

[Girard & Pappas,IEEE-TAC-2007] incremental stability

[Angeli,IEEE-TAC-2002]

networked

control systems

[HSCC-2012]

[IEEE-CDC-2012]

[ERCIM News ‘97]

[IEEE-TAC-2016 ?]

Research at DEWS (IAB meeting 2014)

PWA systems

[IEEE-TAC-2014]

networks of control

systems

[IEEE-ACC-2014]

[IEEE-TAC-2016 ?]

decentralized symbolic

control & application to

vehicle platooning

[NecSys 2013]

stable control systems

with disturbances

[SIAM-2009]

09/13

#1. Construct the symbolic model T of the plant system Done:

1.1 CPS with one plant and one computing unit communicating via nonideal communication infrastructure

[Borri et al; HSCC-2012], [Liu et al.; HSCC-2014], [Zamani et al; IEEE-CDC-2015],]

1.2 CPS with multiple plants and computing units communicating via ideal communication infrastructure

[Tazaki et al.; HSCC-2008], [Pola et al.; IEEE-TAC-2016 ?]

To be done: 1.1 + 1.2 = ?

Incremental stability notions for CPS

Symbolic models for CPS with multiple plants and computing units communicating via nonideal communication infrastructure

Correct-by-design embedded control software 10/13

#2. Design a symbolic controller C that solves the specification S for T Done: [Borri et al; HSCC-2012]

Model: CPS with one plant and one computing unit communicating via nonideal communication infrastructure

Specifications: non-deterministic transition systems

To be done:

Extension to symbolic control design with specifications in terms of Linear Temporal Logic

Extension to symbolic control design for CPS with multiple plants and computing units communicating via nonideal communication infrastructure

Correct-by-design embedded control software 11/13

Model: Networks of Finite State Machines

Assumptions:

no continuous and/or hybrid dynamics

ideal communication infrastructure

Done: [Pola et al.; Automatica-2016 ?]

Decentralized observers detecting instantaneously faults/criticalities in CPS

Model reduction via bisimulation theory

To be done:

Extension to CPS with continuous and/or hybrid dynamics and with nonideal communication infrastructure

Extension to opacity [Mazare et., WITS 2004], i.e. to keep secret a set of states of an FSM with respect to all possible measurements on the system

Detection of Faults and/or Criticalities in CPS 12/13

Additional expertise required:

From Telecommunication Engineering

to set up a comprehensive model of communication infrastructures

From Embedded Systems Engineering

to set up a comprehensive model of hardware/software infrastructures

From Computer Science

to design efficient algorithms for analysis and controllers’ synthesis

The need for an interdisciplinary approach 13/13

Autonomous Vehicleand MicroGrids as CPS

Elena De Santis

Introduction

Traffic ControlMotivations

Autonomous Vehicle

Power SystemsMotivations

DC Microgrid

Conclusions

Autonomous Vehicle and MicroGridsas CPS:

Challenges and Opportunities

Elena De Santis

L’Aquila UniversityCenter of Excellence DEWS

L’Aquila, Jenuary 26th 2016

1/17

Autonomous Vehicle and MicroGrids as CPS


Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Index

1 Introduction

2 Traffic ControlMotivationsAutonomous Vehicle

3 Power SystemsMotivationsDC Microgrid

4 Conclusions

2/172/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Presentation outline

Traffic Control: Development of an Adaptive Cruise Controlmodel able to imitate human driver behaviour

Power Systems Control: Analysis and control of a DirectCurrent microgrid connected to renewables, storage systemsand loads

3/173/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

CPS

4/174/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Traffic Control

Microscopic approach: each element is analyzed(ex: mechanical laws)

Macroscopic approach: the elements together are analyzed(ex: kinetic gas theory)

Mesoscopic approach: macroscopic quantities are introducedin the microscopic approach!

5/175/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Why Human-Inspired?

BREAKING NEWS!

6/176/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

State of the art

7/177/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Hybrid systemsH = (Q, X , f , Init, Dom, E , G, R)

Q = {q1, q2, ...} is the set of discrete states;X = Rn is the continuous state space;f = {fi , qi ∈ Q} is a vector field;Init ⊆ Q × X is the set of initial conditions;Dom(·) : Q → 2X ;E ⊆ Q × Q is the set of edges;G(·) : E → 2X is a map describing guard conditions;R(·, ·) : E × X → 2X is a reset.

8/178/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Discrete States and related Domains

ImportantControl based on information fromLEADER + ENVIRONMENT

9/179/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Power Systems Control

10/1710/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Change of paradigm

Energy Production

Energy Transportation

11/1711/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

DC Microgrid

DefinitionMicrogrid concept: a cluster of loads and microsourcesoperating as a single controllable system that providespower to its local area.

12/1712/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Framework

13/1713/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Compressed Sensing

Problem:Find a sparse solution to the under-determined set ofequations:

14/1714/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Why is interesting?

15/1715/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

References

Safe Human-Inspired Mesoscopic Hybrid Automaton forLongitudinal Vehicle Control, A. Iovine, F. Valentini, E. De Santis,M. Di Benedetto, M. Pratesi, 5th IFAC Conference on Analysis andDesign of Hybrid Systems (ADHS’15), Atlanta, 14-16 October 2015A Safe Human-Inspired Mesoscopic Hybrid Automaton forAutonomous Vehicles, A. Iovine, F. Valentini, E. De Santis, M. DiBenedetto, M. Pratesi, to be submitted to IFAC journal NonlinearAnalysis: Hybrid Systems (NAHS)Management of the Interconnection of Renewables and Storagesinto a DC Microgrid, A. Iovine, S. B. Siad, G. Damm, A. Benchaib,F. Lamnabhi-Lagarrigue, E. De Santis, M. D. Di Benedetto, draftSecure Estimation for Wireless Tracking Control underDenial-of-Service Attacks G.Fiore, Y.H. Chang, Q.Hu, C. Tomlin,M.D. Di Benedetto, draft

16/1716/17



Elena De Santis

Introduction


Autonomous Vehicle


DC Microgrid

Conclusions

Thanks for your attention!

Any Questions?17/1717/17


Henry [email protected]

DISIM

Dept. of Information Engineering, Computer Science and Mathematics

University of L’Aquila, Italy

DEWS

Centre of Excellence on Design Methodologies of Embedded Controllers,

Wireless Interconnect and Systems-on-chip - University of L’Aquila, Italy

SEA Group

Which architectural styles?

Objective 1: Discovering best practices in

Architecting Cyber-Physical Systems

Objective 2: Discovering self-adaptation

practices in Architecting Cyber-Physical Systems

Collaborating CPS components• Which architectural

style?

• How to describe the

architecture of a CPS?

• Which are the critical

architecture decisions?

• How to assess the

quality of such a

model?

SEA Group

Goal: to analyze the state-of-the art in architecting

(self-adaptive) CPS

Method: Systematic Literature Review

Output: a classification of the most frequent practices

used for architecting CPS

More info: http://dl.acm.org/citation.cfm?id=2797453

Contact info: [email protected]

SEA Group

Models

Interoperability

Multi View

Management

(DS)Language

Extensibility

Usable &

Analytic DSL

Group

Design

Decision

Resilience

SA-based

Testing and

MC

Needs and Challenges Domains

CPS

Mobile

any

Technical Foundations

Metamodel

Composition

ModelTransformation

Model

Weaving

Semantic

Wiki

DLSs

Editors

Megamodeling

Survey TSE 2013

+

Software Architecture

4

MDE

Architecting complex systems

Software and System Architecture

CompComp

Comp

Comp

Comp

Comp

Comp

SYSTEM

SEA Group

Architecting challenges

How to build an

architecture that satisfies

the functional and non

functional requirements

and constraints?

Which architectural

decisions to be made?

Which architectural style to

be used?

How to validate such a

design model?

SEA Group

Views and

Viewpoints

Distributed team

management

SA Styles

SA Languages

Components and

ConnectorsTechnologies

25 years of work on

Software Architectures

Problem Statement9

Q: How the Software Architecture community can

contribute to engineering CPSs?

Q: How our theories and methods can be adaptedto fruitfully design CPSs?

Q: What are the new design challenges in architecting CPS?

Architecting Cyber Physical Systems

More abstractionNew design processes

New middlw

components

Multiple levels of

abstractions

Still, the trends of research on architecting CPS is unclear!

Università degli Studi dell’Aquila

Architecting

Henry MucciniDISIM, University of L’Aquila, Italy

Joint work with Ivano Malavolta and Mohammad Sharaf

[email protected], @muccinihenry

How?11

4 Research Questions

Search and Selection Protocol

Keywording

Inclusion and Exclusion

Search on Scholar

Search on Conferences

RQ1 – What are the

application

domains in

which the activity of architecting CPSs has been used so

far?

RQ2 – What are the type of

quality

attributes

(challenges)encountered when architecting CPSs?

RQ3 – What are

the goals and

focus areas of

the activity of architecting CPSs?

RQ4 – What are the types of

solutions to

support the activity of

architecting CPSs?

4

9

13

14

20

23

24

26

44

51

52

0 10 20 30 40 50 60

TESTABILITY

SECURITY

MAINTAINABILITY

FLEXIBILITY

RELIABILITY

DEPENDABILITY

COMPATIBILITY

MODIFIABILITY

PORTABILITY

SURVIVABILITY

PERFORMANCE

RQ2: Quality Attributes (challenges) 12

PERFORMANCE

timing: 30

resource utilization: 8energy/power consumption: 8efficiency: 6

SURVIVABILITY

heterogeneity: 29

distribution: 7reconfigurability:7mobility: 4autonomy: 4

PORTABILITY

integrability: 20

adaptability: 19

portability: 3independency: 2

RQ4: Solutions13

10

10

13

13

18

22

22

24

26

72

76

77

0 10 20 30 40 50 60 70 80 90

MIDDLEWARE

RESOURCE

RECONFIGURATION

VIRTUALIZATION

SOFTWARE AGENTS

COMPONENT-BASED

COMMUNICATION INFRASTRUCTURE

MODELING AND VALIDATION FRAMEWORKS

MODELING LANGUAGES

DESIGN

ARCHITECTURE

PATTERNS

PATTERNS

SOA: 31

multi-tier : 15

event-driven: 11

cloud: 11

ARCHITECTURE

cloud architecture: 11

system architecture: 8

Integration architecture: 4

DESIGN

modeling: 34

quality driven for system design: 12

platform: 7

RQ1: domains and applications14

2

4

8

9

12

18

27

34

47

0 5 10 15 20 25 30 35 40 45 50

MILITARY

CONSUMER

INFRASTRUCTURE

ROBOTICS

HEALTH CARE

MANUFACTURING

COMMUNICATION

ENERGY

TRANSPORTATION

TRANSPORTATION

-vehicular CPS

-avionics and aerospace

-intelligent transportation (traffic control)

ENERGY

-smart grids

-building control systems (smart building and smart city)

-distributed energy systems

COMMUNICATION

-WSNs

-Mobile CPS

-IoT

RQ1 – What are the

application domains in which

the activity of architecting CPSs has

been used so far?

Case studies15

Military 1

2%Consumer 2

4%

Infrastructure 1

2%

Robotics 4

8%

Health Care 5

10%

Manufacturing:

11

23%

Communication 1

2%

Energy

7 papers

15%

Transportati

on: 16

33%

Architectural Methods and

Techniques

languages;

18; 4%middleware;

26; 6%tactics; 28;

6%reference

architecture;

31; 7%

Framework;

34; 8%

views; 55;

12%

models;

60; 14%

architect

ure; 94;

21%

style; 96;

22%

8

13

32

119

0 20 40 60 80 100 120 140

WORKSHOP PAPER

BOOK CHAPTER

JOURNAL PAPER

CONFERENCE PAPER

Publication Venue


Self-Adaptation

Henry Muccini, Mohammad Sharaf, Danny Weyns

DISIM, University of L’Aquila

KU Leuven, Sweden

SEA Group

RQ1: How is self-adaptation applied in cyber physical

systems?

• Concerns, technology stack, application domains

RQ2: How do existing approaches for self-adaptation in

cyber physical systems handle self-adaptation

concerns?

• feedback loops, models

RQ3: What type of evidence is provided by existing

approaches for self-adaptation in cyber physical

systems?

• Empirical methods, assurances

SEA Group

Feedback Loops

Technology stack

SEA Group

Main Findings

Application layer

Middleware layer

Communication layer

Service layer

.. layer

Feedback loop

Feedback loop

Feedback loop

performance and

reliability

Security and

interoperabiliy

Technolgy stack vs Feedback loop Concerns


Security

Henry Muccini, Mohammad Sharaf, Deepak Khrisna,

Vikas Kumar



A modellig platform

Ivano Malavolta, Henry MucciniGSSI, L’Aquila


SEA Group

Modeling

environment

Programming

Framework

Analysis

and

Code

Generati

on


Ivano Malavolta, Henry MucciniGSSI, L’Aquila


SEA Group

AMUSE

MUSEUM:

To mitigate waiting queues

To manage emergencies

To provide ICT services

SEA Group

References

Ivano Malavolta, Henry Muccini, Mohammad Sharaf:A Preliminary Study on Architecting Cyber-PhysicalSystems. ECSA Workshops 2015: 20:1-20:6

Ivica Crnkovic, Ivano Malavolta, Henry Muccini, Mohammad Sharaf: On the Use of Component-Based Principles and Practices for Architecting Cyber-Physical Systems. CBSE 2016 (to appear)

Henry Muccini, Mohammad Sharaf, Danny Weyns: Self-Adaptation for Cyber-Physical Systems: A SystematicLiterature Review. SEAMS 2016 (to appear)

Electronic Design Automation &Embedded Systems Development

Luigi Pomante

First DISIM Workshop on Engineering Cyber-Physical Systems,

L’Aquila, 26/01/2016

2

Overview

Cyber-Physical Systems

M3 research line: main research topics

Electronic System-Level HW/SW Co-Design

Networked Embedded Systems

Mixed-Criticality Systems

Smart monitoring systems for Embedded SoC architectures

Advanced Processing Architectures

M3 research line: main research projects

3

Cyber-physical systems

A cyber-physical system (CPS) is an integration of computation with

physical processes.

Embedded computers and networks monitor and control the physical

processes, usually with feedback loops where physical processes

affect computations and vice versa.

As an intellectual challenge, CPS is about the intersection, not the

union, of the physical and the cyber.

E. A. Lee, S. A. Seshia

Introduction to Embedded Systems, a Cyber-Physical Systems approach

LeeSeshia.org, 2011

4

Cyber-physical systems

CYBER

PHYSICAL

EMBEDDED

REAL

TIMENETWORKED

5

M3 Main Research Topics

Networked Embedded Systems

HW/SW Technologies for (Networked) Embedded Systems

Wireless Sensor Networks

Middleware, Localization/Tracking, Security, EDA tools for WSN


Hypervisor technologies for mixed-criticality multi-core platforms

Mixed-criticality Network-On-Chip

Electronic System-Level HW/SW Co-Design

HW/SW Co-Design of Heterogeneous Parallel Dedicated/Embedded

Systems

HEPSYCODE

6

M3 Main Research Topics

Smart monitoring systems for Embedded SoC architectures

Distributed HW Profiling System for Parallel Architectures on FPGA

Platforms

4-LOOP, A-LOOP


SDR Platforms

Many-core chips for TSR

Insights on

Research Topics

7

8

Networked Embedded Systems: Wireless Sensor Networks

Middleware for WSN

Heterogeneous HW/SW/radio platforms

Virtual Machines (support to cooperations and distributed SW development)

Services

Indoor Localization

Security (cryptography, intrusion detection system)

Remote Lab and Testbed (LabSMILING)

Up to 100 nodes remotely programmable and monitorable

WSN data collection and analysis

9

Technologies

Hardware

CrossBow/Memsic: Mica2, MicaZ, IRIS, Imote2, TelosB

Advanticsys: TelosB-like

Texas Instruments: CC2xxx, CC4xxx

IBM: Moterunner

Atmel: ZigBit

10

Technologies

Software

C + HAL

OS: TinyOS, FreeRTOS, Contiki

Middleware

Agilla/Agilla 2

Communication protocols

IEEE 802.15.4 (Atmel and TinyOS implementations)

Specific routing algorithms

Atmel, TinyOS and OpenZigBee implementations

11


In a mixed criticality system different functions with different

insurance levels are allocated on the same component

A mixed criticality system requires a rigorous temporal and spatial

partitioning

Robust hardware and software mechanisms to prevent interference

between the various functions

Multi-core and many-core devices have considerable advantages

A much higher computational capacity per footprint, allowing a

substantial reduction of energy consumption

Disadvantage: they are less predictable, given the heavy use of

shared resources by the various processing elements


Use of hypervisors on multi-processor architectures

Virtualization appears to be apromising technique toimplement robust softwarearchitectures in multi-coreavionics platforms

Analysis of paravirtualizationtools on a multi-processorLEON4 platform specificallydesigned for the aerospacedomain

FentISS XtratuM SYSGO PikeOS

Porting and analysis of hypervisorsolutions on FPGA based SoCs

12

PARTITION 1

HYPERCALL INTERFACE

KE

RN

EL

MO

DE

US

ER

MO

DE

PARTITION 2 PARTITION 3

XTRATUM

USER

PARTITIONS

SUPERVISOR

PARTITIONS

PIKEOS SYSTEM SOFTWARE

PARTITION 1 PARTITION 2 PARTITION 3

PIKEOS SEPARATION MICROKERNEL

ARCHITECTURE

SUPPORT PACKAGE

PLATFORM

SUPPORT PACKAGE

KE

RN

EL

MO

DE

US

ER

MO

DE

13


Picture: OpenSynergy/SYSGO - Mixed-Criticality: Hypervisors in networked cyber- physical systems


Hardware mechanisms to supportisolation in a network-on-a-chip

Isolation of different applicationclasses on NoC architectures

Hardware mechanisms supportingisolation to be introduced into existingnetwork interfaces

Support for the execution of multipleapplications with different criticalitylevels

Strategy: message exchange supervision

14

R1

T7(c1),

TM

NI4

R4

T1(c1),

T2(c2)

NI1

R2

T5(c1),

T6(c2)

NI3

R3

T3(c1),

T4(c1)

NI2

15

ESL HW/SW Co-Design: HEPSYCODE

A System-Level Methodology for HW/SW Co-Design ofHeterogeneous Parallel Dedicated Systems that, starting from amodel of the system behaviour, based on a Concurrent ProcessesMoC, leads to an heterogeneous parallel dedicated system able tosatisfy given F/NF requirements

In particular, the goal is to suggest to designer

How to partition processes between HW and SW

Which kind of heterogeneous parallel architecture to use

How to map processes to processor

16

ESL HW/SW Co-Design: HEPSYCODE

The Co-Design Flow

System

Behaviour

Model

Functional

Simulation

Reference

Inputs

Co-Analysis

Co-Estimation

- Affinity

- Timing

- Size

- Concurrency

- Load

- Bandwidth

Timing

Constraints

HW/SW Partitioning,

Mapping and

Architecture Definition

Timing

Co-Simulation

Design Space Exploration

Algorithm-Level

Flow

System-Level Flow

Hetrogeneous

Parallel

Dedicated

System

Technologies Library

-Processors

-Memories

-Interconnections

Scheduling

Directives

Architectural

Constraints

17

Smart monitoring systems for Embedded SoC

architectures

Concept of a monitoring system

Functional RequirementsNon-functional Requirements Execution Time

Power Dissipation

Area

…

How estimate parameters starting by measurements?

How to make measurements?

How to take measurements?

Global MonitorSystem under

examination

Identification of the monitoring system

18

Proposed framework

Library

of

elements

System

identification

Inputs

Monitoring

system

composition

Monitoring

system

implementation

New

monitored

system

OutputsF/NF

requirements

General system view

19

core core

Bridge

Cache

I/D

core

Cache

I/D

Cache

I/D

SDRAM

Controller

NetworkUART

SSS

S S

SSS

S

S S

SS

Global monitor

Adapter

Inte

rfa

ce

Time

measure

Event

Count

Filtering

Hardware sniffers

Nucleus

Current collaboration with UNIMORE to manage access to shared

resources and to monitor system activities

Platforms

20

21

Multicore platforms

4–LOOP - SMP system including:

A quad-core Leon 3 with Linux operating system, OpenMP library and

hardware profiling system

ML605 (Virtex 6) Development Board

Current collaboration with POLIMI to port the Barbeque framework

(http://bosp.dei.polimi.it) on 4-LOOP platform

http://bosp.dei.polimi.it/

22

Multicore platforms

A–LOOP - AMP system including:

a dual-core ARM Cortex A9 processor with Linux operating system

a quad-core Leon3 processor with Linux operating system, OpenMP

library and a hardware profiling system

HARDWARE ARCHITECTURETHE PLATFORM

ZedBoard (Zynq7000)

Development Board

Current collaboration with POLITO to evaluate reliability of an AMP

(i.e. dual-SMP) PikeOS mixed-critical system

23


SDR Platforms

Sundance HW/SW development kit for Software-Defined-Radio (Wi-FI, 802.15.4, Wi-Max)

Many-core accelerators for TSR

Development of Parallel SW for True Software Radio

Avionic/TLC algorithms for a 64 VLIW cores accelerator

Simulator for PRAM MoC

Projects & People

24

25

M3 Main Research Projects

VISION (ERC-2009-StG 240555) Video-oriented UWB-based Intelligent Ubiquitous Sensing

SMILING (RIDITT 2009, national project) SMart In home LIviNG

PRESTO (Artemis-JU ASP 2010-269362) ImProvements of industrial Real Time Embedded SysTems develOpment

process

CRAFTERS (Artemis-JU ASP 2011-295371) ConstRaint and Application-driven Framework for Tailoring Embedded Real-time

Systems

26

M3 Main Research Projects

EMC2 (Artemis-JU AIPP 2013-621429) Embedded Multi-Core systems for Mixed Criticality applications in dynamic and

changeable real-time environments

CASPER (H2020-MSCA-RISE-2014) User-centric MW Architecture for Advanced Service Provisioning in Future

Networks

SAFECOP (ECSEL-JU RIA-2015) [in negotiation] Safe Cooperating Cyber-Physical Systems using Wireless Communication

27

People

Post-doc Fabio Federici, Claudia Rinaldi, Marco Santic

PhD Students Vittoriano Muttillo, Giacomo Valente

Collaborators Ileana Cerasani, Walter Tiberti

From Ambient Intelligence From Ambient Intelligence to Cyber-Physical Systemsto Cyber-Physical Systems

Stefania CostantiniStefania CostantiniPasquale CaianielloPasquale Caianiello

Giovanni De GasperisGiovanni De Gasperis

DISIMDISIMUniversità degli Studi di L’AquilaUniversità degli Studi di L’Aquila

Vision

• Così

• Non così

• E non così (wearable computing?)

Ambient Intelligence• The term ‘Ambient Intelligence’ was

introduced by Emile Aarts della Philips (http://www.research.philips.com/

technologies/syst_softw/ami/index.html) • It was then adopted by the European

Community

http://www.research.philips.com/

Ambient Intelligence (AmI)

• Computers and networks will be integratedinto the everyday environment renderingaccessible a multitude of services andapplications through easy-to-use humaninterfaces. This vision of "ambient intelligence"places the user, the individual, at the centre offuture developments for an inclusive knowledgebased society for all

• Now: Fog Computing, Cyber-Physical Systems

Ambient Intelligence (AmI)

• The Environment will be integrated byintelligent interfaces supported bycomputing and networking technology which is everywhere, embedded ineveryday objects such as furniture,clothes, vehicles, roads and smartmaterials even particles of decorativesubstances like paint

Ambient Intelligence: Vision

• Radically rethink the human-computerinteractive experience: – Integrate digital world (information &

services) and physical world (physicalobjects/environment)

– Make interfaces more responsive andproactive (objects & environment monitoruser and (proactively) presentinformation & services relevant to user’scurrent needs/interests)

Componenti dell’AmbientIntelligence

• Ambient– Materiali innovativi, Wearable Computing,

Sensori, Attuatori, Interfacce utente,Infrastrutture di Comunicazione

• Intelligence– Elaborazione del Linguaggio Naturale, Interfacce

Utente, Gestione dei Contenuti (Basi diConoscenza), Computational intelligence(Intelligenza Artificiale,Agenti Intelligenti

Internet of Everything

• I dispositivi digitali sonointegrati negli oggetti ditutti i giorni e nell‘ambiente(ubiquità, pervasività)

• Essi comunicano tramite unainfrastruttura comuneinvisibile e apparentementenon intrusiva

• Non c‘è più un solo computerper utente ma i varidispositivi interagisconomediante intelligenzadistribuita.

Un Possibile Futuro? Ambient semantics or “enriching your every day experience”

– Book tells you about friends/famous people that lovedit

– Book tells you about particularly interesting passages – Touching 2 books makes their connections appear – Picking up book makes relevant music play

Un Possibile Futuro?

• Objects with memory – Leaving messages in objects (e.g. reminders, personal

stories) – Objects that can tell you their relevant

stories/memories – Objects record history, rhythms of time and events

Intelligenza Artificiale e Agenti Intelligenti

I droidi D-3BO e C1-P8 di“Star Wars”

L’Intelligenza Artificiale(AI, born 1956)

John McCarthy, 1927-2011

Marvin Minski, 1927-2016

Agenti (software)• Sono situati in un ambiente non

necessariamente del tutto noto apriori

• Sono autonomi• Percepiscono l’ambiente• Agiscono sull’ambiente• Comunicano con altri agenti • Possono avere obiettivi, svolgere

compiti

Agenti Intelligenti(software)

• Interagiscono in modo flessibile conl’ambiente

– Sopravvivono– Imparano– Si adattano– Perseguono obiettivi– Cooperano, competono, negoziano

26 gennaio 2016 S. Costantini - IntelligenzaArtificiale

15

Features • Reattività• Proattività• Capacità di ragionamento

– pianificazione +– common sense reasoning

• Abilità sociale• Memoria• Capacità di imparare e rivedere le proprie

conoscenze

Una funzione essenziale: Imparare (Learning)

• Imparare dall’utente • Imparare come si comporta l’utente• Imparare dagli altri agenti• Imparare dall’esperienza

26 gennaio 2016 S. Costantini - IntelligenzaArtificiale

17

Intelligenza come fenomenoemergente

• Un agente software è dotato di un insiemedi comportamenti e capacità

• Quello che farà dipende:– dall’interazione con l’ambiente– dalle capacità dell’agente– dalle scelte dell’agente

• Se l’agente è ben programmato e adattato,si comporterà in modo “intelligente”

DALI: un linguaggio logico per agenti

Stefania Costantini & Arianna Tocchio

• Definito e implementato nel LaboratorioAAAI@AQ,

Università degli Studi di L’Aquila

• Brevettato, usato in applicazioni reali (ades. CUSPIS)– Disponibile su

• https://github.com/AAAI-DISIM-UnivAQ/DALI

https://github.com/AAAI-DISIM-UnivAQ/DALI

A Scenario: Augmented Reality

• Augmented physical environments

– Objects around you can draw your attention(e.g. books on a bookshelf of specific interestto you)

– Walking around town, system points outbuildings/places of particular interest to a user(based on user’s interests)

Today’s Augmented Reality

• Google glasses or mobile apps

What we did:Turismo e Fruizione Beni Culturali

• Localizzazione utenti via satelliti GALILEO• Agenti Intelligenti per:

– Profilo utente– Informazioni personalizzate– Proposte correlate agli interessi

Fruizione Beni Culturali: scenario

Ruolo degli Agenti Intelligenti

• Interagire con l’utente per ottenere ilprofilo base

• Personalizzare informazioni e interazione• Capire gli interessi dell’utente, • Aggiornare il profilo

Progetto CUSPIS

CUSPIS Demonstrator : Villa Adriana

Domotica

• Si occupa dell'integrazione delle tecnologie checonsentono di automatizzare una serie dioperazioni all’interno della casa. – Integrazione dei dispositivi elettrici ed elettronici, degli

elettrodomestici, dei sistemi di comunicazione, dicontrollo e sorveglianza presenti nelle abitazioni.

Il termine domotica deriva dall’importazione delneologismo francese domotique = domos automatique

Domotic and Smart Cities

• Obiettivo: abitare in case più sicure econfortevoli, dotate di un sistema diautomazione semplice, affidabile, flessibileed economico

• Un sistema (teoricamente) alla portata ditutti.– Confort– Sicurezza– Risparmio energetico

Smart Buildings (EnergyProsumers/Consumers)

Intelligent DALI Agents forSmart Buildings

• Optimize personal confort according topreferences and health conditions whilerespecting overall objectives via a specialInterval Temporal Logic

• Objectives: keep comsumption/expense withinlimits, sell and buy energy at best prices

A Multiagent Saver for the Automatic Management of HVAC Systems Speaker: Giovanni De Gasperis,University of L'Aquila, Italy EEEIC 2015, Rome

Prosumer node model

– real-time predictivecontrol of airconditioning systemsin smart buildings inthe context of energymanagement.

In general, a PROSUMER NODE in a smart grid is:

– A smart building that can produce, accumulate and have autonomyof decision making about resource consumption, dealing with givencomfort constraints

A Predictive Model for the Automated Management of Conditioning Systems in Smart Buildings.Speaker: Giovanni De Gasperis, University of L'Aquila, Italy UkSIM 2015, 25-27 March 2015, Cambridge, UK

The predictive control needs agood estimate of near future powerdemand.

To achieve acceptable near futureestimates, we proposed a method basedon “Evidence combination”, measuringperformances of a bank of estimators overtime:

1. Simple Moving Average (SMA)2. Functional Regression (FR)3. Support Vector Regression (SVR)4. Gradient Tree Boosting (GTB)

SMA FR SVR GTBbank ofpowerdemandestimators

ActualPower

measures

performance assessment &evidence combination

power demandforecast

Cycling over 96 samples, 1 each quarter ofhour of the last 24

nextquarterofhour

A Multiagent Saver for the Automatic Management of HVAC Systems Speaker: Giovanni De Gasperis,University of L'Aquila, Italy EEEIC 2015, Rome

Multi Agent Energy Saver Supervisor SystemArchitecture

e-Healthapplications

What we intend to do: Sostegno ai Disabili

• La disabilità non è una malattia, ma un “condizioneattuale” di una persona (World HealthOrganization)

• Una persona disabile è temporaneamente odefinitivamente incapace di effettuaredeterminate attività in modo “corretto” o “normale”

• La disabilità è correlata a situazioni nelle quali unapersona non è capace di gestire in modo adeguatouna situazione– Per cause fisiche o cognitive– Per cause esterne che creano limitazioni

Tutti noi siamo occasionalmentedisabili!

Ambient Intelligence/CPSsper il Sostegno ai Disabili

• Localizzazione dell’utente nell’ambientecircostante

• Aiuto nel riconoscere luoghi e oggetti• Adattamento all’utente per aumentare

confidenza e garantire sicurezza• Fornire schemi per sequenze “corrette” di azioni• Riconoscere e correggere le sequenze “non

corrette” di azioni

Ambient Intelligence/CPSsper il Sostegno ai Disabili

• In casi estremi, prendere autonomamentealcune decisioni (ad esempio sul dove ecome spostarsi)

• Imparare ad interpretare autonomamentei pattern dei comportamenti quotidiani;

• Riconoscere segni di angoscia,disorientamento,confusione

Ambient Intelligence/CPSs

per il Sostegno ai Disabili

• Offrire un aiuto proattivo attraversodiversi tipi di interventi fisici e verbali– Effettuare azioni per conto dell’utente– Raccogliere e fornire informazioni utili

• Allertare altri in caso di pericolo.

Che cos'è il contesto?

“L’informazione di contesto può in generale esseredefinita come un insieme ordinato multilivello diinformazioni dichiarative riferite agli eventi che siverificano in un dato luogo e che coinvolgonooggetti animati ed inanimati” [J. Crowley]

Context-awareness

Context-Awareness• Rappresentare il contesto

– Ontologie (in Informatica): descrizioneformale delle tipologie che si assumeesistano in un dominio di interesse Ddalla prospettiva dell’individuo che usaun linguaggio L al fine di parlare di D”.

• Percepire il contesto allargando ladescrizione con le nuove percezioni.

Dall’informazione dicontesto alla comunicazione

personalizzata

• Obiettivi– adattività rispetto al contesto– adattività rispetto al terminale utente– personalizzazione rispetto al profilo

dell’utente

Dall’informazione di contesto allacomunicazione personalizzata

– Interazione multimodale: testo, voce,avatar

– Interazione controllata da un agenteintelligente

Big Picture(by Aielli, Ancona, Caianiello, Costantini,

De Gasperis, Di Marco, Mascardi)

What we intend to do: eF&K for eHealth

Thank you for yourThank you for yourAttention!Attention!