1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS

Fon any information please contact Alessandro D’Innocenzo – alessandro.dinnocenzo@univaq.it -
or Henry Muccini - henry.muccini@univaq.it
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
alessandro.dinnocenzo@univaq.it
henry.muccini@univaq.it
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
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
control
Affect
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
Plant
u y
x
• Let a plant model be given by input/output/internal variables and
differential/difference equations, e.g.:
‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬

Plant
u y
x
‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬
• Let some specifications be given on the desired behavior of the variables,
e.g. stability or some temporal logic formula

PlantController
u y
x
‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬
• Design a controller such that the closed-loop interconnection satisfies the
specifications, e.g.
ℎ ݇ + 1 = ‫ܧ‬ℎ ݇ + ‫ݕܨ‬ ݇ , u ݇ = ‫ܩ‬ℎ(݇)

PlantController
‫ݔ‬ ݇ + 1 = ‫ݔܣ‬ ݇ + ‫ݑܤ‬ ݇ , ‫ݕ‬ ݇ = ‫)݇(ݔܥ‬
• Design a controller such that the closed-loop interconnection satisfies the
ℎ ݇ + 1 = ‫ܧ‬ℎ ݇ + ‫ݕܨ‬ ݇ , u ݇ = ‫ܩ‬ℎ ݇
• 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

Modeling and Co-design of Control Tasks
over Wireless Networking Protocols:
State of the Art and Challenges
Alessandro D’Innocenzo
1st DISIM Workshop on Engineering Cyber Physical Systems
January 26, 2016 – University of L’Aquila

Objective 1: Robust & secure design of control tasks over
wireless communication protocols
Objective 2: Co-simulation and emulation of control
algorithms, communication protocols and physical systems
• Formal compositional interfaces between control algorithms and wireless
communication protocols
• Quantify impact of wireless networking on control performance
• Robustness with respect to packet losses and delays
• Resilience with respect to failures and malicious intrusions
• Formal verification tools and co-simulation environments

Goal: to develop novel methods for co-design of control
algorithms and communication protocol configuration
Method: Interdisciplinary research across the “3C”: control
theory, computer science and communication theory
Output: novel methods that improve performance and security
of technological solutions for wireless automation systems
Contact info: alessandro.dinnocenzo@univaq.it

Control task
Plant
u y
x
𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘)

Control task
Plant
u y
x
𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘)
e.g. stabilityor some temporal logicformula

Control task
PlantController
u y
x
𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘)
• Design a controller such that the closed-loop interconnectionsatisfies the
ℎ 𝑘 + 1 = 𝐸ℎ 𝑘 + 𝐹𝑦 𝑘 , u 𝑘 = 𝐺ℎ(𝑘)

Control task
PlantController
𝑥 𝑘 + 1 = 𝐴𝑥 𝑘 + 𝐵𝑢 𝑘 , 𝑦 𝑘 = 𝐶𝑥(𝑘)
• Design a controller such that the closed-loop interconnectionsatisfies the
ℎ 𝑘 + 1 = 𝐸ℎ 𝑘 + 𝐹𝑦 𝑘 , u 𝑘 = 𝐺ℎ 𝑘
• What if plant and controller exchange data via a wireless 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
xWireless
Network

Challenges with Wired Control Networks
Wires are expensive
• Wires as well as installationcosts
• Wire/connector wear and tear
Lack of flexibility
• Wires constrain sensor/actuator mobility
• Limited reconfigurationoptions
Restricted control architectures
• Centralizedcontrol paradigm

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

Wireless Control Network
A collection of cooperating algorithms (controllers) designed to achieve
a set of common goals, aided by interactions with the environment
through distributed measurements (sensors) and actions (actuators)
exchanged via a wireless communication network

Applications of Wireless Control Networks
Industrial automation
EnvironmentalMonitoring,
Disaster Recovery and
Preventive Conservation
Supply Chain and
Asset Management
Physical Security
and Control

Opportunities vs challenges with Wireless Control Networks
Lower costs, easier installation
• Suitable for emerging markets
Broadens scope of sensing and control
• Easier to sense/monitor/actuate: opens new application domains
Compositionality
• Enables system evolution via composable control loops
Runtime adaptation and reconfiguration
• Control can be maintained in response to failures and malicious attacks
Complexity
• Systems designers and programmers need suitable abstractions to hide the
complexity from wireless devices and communication protocols
Reliability
• Need for robust and predictable behavior despite wireless non-idealities
Security
• Wireless technology is vulnerable: security mechanisms for control loops
Take into account communication protocol behavior!

ISO/OSI model for (wireless) communication protocols
Application
Session
Presentation
Transport
Network
Data/Link
Physical
Application
Session
Presentation
Transport
Network
Data/Link
Physical
Wireless
link))) (((
• Open systems interconnection (OSI) model separates functional elements of a
network into seven layers
Host A Host B

Application
Session
Presentation
Transport
Network
Data/Link
Physical
Application
Session
Presentation
Transport
Network
Data/Link
Physical
Wireless
link))) (((
Interference, data
losses, delays, limited
energy, channel
capacity, failures,
malicious intrusions
Coding,
modulation,
tx power
Scheduling, access
to the wireless
channel
Routing strategy
• OSI model has allowed refinement of each layer independently
Skype, youTube…
TCP, UDP
Host A Host B

Application
Session
Presentation
Transport
Network
Data/Link
Physical
Application
Session
Presentation
Transport
Network
Data/Link
Physical
Wireless
link))) (((
• OSI model has allowed refinement of each layer independently
• Each layer only talks with the corresponding layer…by exchanging packets with
the layers above & below
Host A Host B

Classical control loop
𝑢 𝑘 = 𝑓(𝑦 𝑘 )
Application
Session
Presentation
Transport
Network
Data/Link
Physical
Application
Session
Presentation
Transport
Network
Data/Link
Physical
Wireless
link))) (((
S1
• Communication stack and medium is transparent to the control algorithm
A1 A2
Robust and Fault-tolerant Control
𝑢5 𝑘
𝑢6 𝑘
y 𝑘
𝑢5 𝑘 𝑢6 𝑘 y 𝑘

Plant control law
Control loop over a wireless network
𝐮 𝐤 = 𝐟(𝐲 𝐤 )
Session
Presentation
Transport
Network
Data/Link
Physical
Sensing/actuation
Session
Presentation
Transport
Network
Data/Link
Physical
Wireless
link))) (((
• Sensing and actuation data are relayed via the protocol stack layers
S1A1 A2

Control loops over a wireless network
A1 A2 S1
𝐮 𝐤 = 𝐟(𝐲 𝐤 )
Session
Presentation
Transport
Network
Data/Link
Physical
Sensing/actuation
Session
Presentation
Transport
Network
Data/Link
Physical
Wireless
link))) (((
Plant control law
• Sensing and actuation data are relayed via the protocol stack layers
• Several feedback control mechanisms within separate communication layers
TCP congestion control
Routing control
Medium access control
Power, coding &
modulation control
Intra-layer control loops

Control loops over a real wireless network
Wireless
network

Wireless
network
Borderline between control over network
and control of network disappears
M.C. Escher, Relativity
Lithograph, 1953

Wireless
network
Borderline between control over network
and control of network disappears
M.C. Escher, Relativity
Lithograph, 1953
Different perspectives in terms of
• Time-scales
• Mathematical setting
• Performance metrics
• Constraints & non-idealities

Handle complexity of CPS via hybrid systems theory
J.Lygeros,S.Sastry,C.J.Tomlin. A game theoretic approach to controller design for hybrid systems. In Proc.
Of IEEE 88(7):949-970, July 2000
• Discrete Variables:
– Heater off: q0
– Heater on: q1
• Continuous Variables:
– Room temperature : x
• Transitions:
– Turn heater ON when the temperature is smaller than 70 degrees: x≤70.
– Turn heater OFF when the temperature is greater than 80 degrees: x≥80.
• Analysis and control of hybrid systems via formal methods:
– Discretize state space: Pola et al. […]
– Discretize trajectories: Yi Deng, A. D'Innocenzo, M. D. Di Benedetto, S. Di Gennaro, A. A. Julius.
Verification of Hybrid Automata Diagnosability with Measurement Uncertainty. IEEE
Transactions on Automatic Control

Challenge: Co-design the control algorithm
and the communication protocol
Controller
Application
Session
Presentation
Transport
Network
Data/Link
Physical
Handle complexity of CPS via tailored modeling and design

Co-design over time-triggered communication protocols
Challenge: Co-design the control algorithm
and the communication protocol
(scheduling, routing and control)
Controller
Application
Session
Presentation
Transport
Network
Data/Link
Physical

WirelessHART MAC (scheduling) and Network (routing) layers
§ Time-triggered access to the channel
§ Time divided in periodic frames
§ Each frame divided in Π time slots of duration Δ

27

28
§ Enables redundancy in data routing

29

30

31
§ Scheduling must guarantee relay via multiple paths

32

33

34
Protocols designed for “slow” control tasks: exploit
redundancy to use it on “fast” control tasks

Redundancy in data routing…
§ …makes system
tolerant to long-
term link failures
§ …enables detection and
isolation of failures and
malicious attacks
§ …makes system robust to short-term
link failures (e.g. packet losses)

Close a control loop investigating two routing strategies:
1. Single-path dynamic routing: switching behavior due to dynamic routing
2. Multi-path static routing:algorithms to merge redundant data
§ …makes system
tolerant to long-
term link failures
malicious attacks

§ …makes system
tolerant to long-
term link failures
malicious attacks
1. Single-path dynamic routing: switching behavior due to dynamic routing
2. Multi-path static routing:algorithms to merge redundant data

Wireless control networks as switching systems
𝐾(𝑡)

𝑡
𝐾(𝑡)

t+1
𝐾(𝑡)

t+…
𝐾(𝑡)
Different paths are associated with different delays.

𝑡+…
𝐾(𝑡)
𝐴 =
𝐴I 𝐵I 0
0 0 𝐼
⋮ ⋮ ⋮
⋯ 0 0
⋯ 0 0
⋱ ⋮ ⋮
0 0 0
0 0 0
0 0 0
⋯ 𝐼 0
⋯ 0 𝐼
⋯ 0 0
𝐵 𝜎 𝑡 =
𝐵𝛿Q R ,S
𝐼𝛿Q R ,5
⋮
𝐼𝛿Q R ,TU6
𝐼𝛿Q R ,TU5
𝐼𝛿Q R ,T
Mathematical model: 𝑥 𝑡 + 1 = 𝐴𝑥 𝑡 + 𝐵 𝜎 𝑡 𝑣 𝑡 , 𝑡 ∈ ℕ, where 𝑥 𝑡 is the plant
and network state, 𝜎 𝑡 ∈ Σ depends on routing/scheduling. The switching signal is
considered as a disturbance.

𝑡+…
Problem: Design a controller 𝐾(𝑡) s.t. the closed loop system is asymptotically stable.
Given a state-feedback staticcontroller 𝐾(𝑡), the closed loop systems is asymptotically
stable iff the Joint Spectral Radius of 𝐴 + 𝐵 𝜎 𝑡 𝐾 𝑡 Q R ∈Z
is smaller than 1.
Insights: Switching systems analysis and design is a crowded research area:
• Leverage special structure of matrices 𝐴 and 𝐵 𝜎 𝑡 to provide tailored results
that outperform classical results on general switching systems
𝐾(𝑡)
Mathematical model: 𝑥 𝑡 + 1 = 𝐴𝑥 𝑡 + 𝐵 𝜎 𝑡 𝑣 𝑡 , 𝑡 ∈ ℕ, where 𝑥 𝑡 is the plant
and network state, 𝜎 𝑡 ∈ Σ depends on routing/scheduling. The switching signal is
considered as a disturbance.

R. M. Jungers, A. D'Innocenzo, M. D. Di Benedetto. Modeling, analysis and design of linear systems with
switching 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 linear
systems 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 systems
with 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 with
switched delays. 51st IEEE Conference on Decision and Control, Maui, Hawaii, December 10-13 2012.
𝐾(𝑡)

1. Single-path dynamic routing:take into account switching behavior due to
dynamic routing
2. Multi-path static routing:take into account algorithms to merge redundant data
§ …makes system
tolerant to long-
term link failures
malicious attacks

Multi-path static routing
§ …makes system
tolerant to long-
term link failures
malicious attacks
Investigate algorithms to merge redundant data:
• Objective: stabilize the closed-loop system
• Best strategy: keep most recent packet vs. compute combination?
• Different paths are associated with different delays
• Not a trivial question, best strategy from the point of view of stability strongly
depends on plant and network: need for a control-theoretic approach

Syntax:
§ Linear plant
𝒫 = (𝐴, 𝐵, 𝐶)
MIMO MCN model

Syntax:
§ Linear plant
§ Weight function 𝑊 determines data processing through the network -
reminiscent of network coding
𝐺ℛ = 𝑉ℛ, 𝐸ℛ, 𝑊ℛ
𝑊ℛ_
: 𝐸ℛ → ℝ 𝑖 = 1, ⋯, 𝑚
𝒫 = (𝐴, 𝐵, 𝐶)
𝐺 𝒪 = 𝑉𝒪, 𝐸 𝒪,𝑊𝒪
𝑊𝒪_
: 𝐸 𝒪 → ℝ 𝑖 = 1, ⋯, 𝑙
MIMO MCN model

Syntax:
§ Linear plant
§ Communication scheduling 𝜂 assigns transmission of nodes
𝑊ℛ_
: 𝐸ℛ → ℝ 𝑖 = 1, ⋯, 𝑚
𝜂ℛ_
: 1, … , Π → 2jℛ
𝑊𝒪_
: 𝐸 𝒪 → ℝ 𝑖 = 1, ⋯, 𝑙
𝜂 𝒪_
: 1, … , Π → 2j 𝒪
𝒫 = (𝐴, 𝐵, 𝐶)
MIMO MCN model

Syntax:
§ Linear plant
§ Communication scheduling 𝜂 assigns transmission of nodes
§ Model at time scale of frames instead of time-slots (no switching behavior)
𝑇 = ΠΔ
𝑊ℛ_
: 𝐸ℛ → ℝ 𝑖 = 1, ⋯, 𝑚
𝜂ℛ_
: 1, … , Π → 2jℛ
𝑊𝒪_
: 𝐸 𝒪 → ℝ 𝑖 = 1, ⋯, 𝑙
𝜂 𝒪_
: 1, … , Π → 2j 𝒪
𝒫 = (𝐴, 𝐵, 𝐶)
MIMO MCN model

Resilient control
𝐹 set of all configurations of links subject to a failureor a malicious intrusion

Resilient control
Mf

Resilient control
Mf
§ Benefit: Do not reconfigure the whole network (i.e. scheduling and routing) when
a failure occurs: instead,onlyreconfigure neighbors offaultynodes
§ Benefit: Do not add complexity to local communication to detect faulty or
malicious nodes:instead,use plant dynamics and path redundancy
§ Technical challenge: Exploit graph theory and control-theoretic approaches for
model-based failure detection and isolation

BANK OF
LUENBERGER
OBSERVERS
𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 , 𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ]
Observer-based diagonal FDI problem

BANK OF
LUENBERGER
OBSERVERS
𝑓l 𝑘𝑇 = [𝑓l5 𝑘𝑇 , 𝑓l6 𝑘𝑇 ,… , 𝑓l|o| 𝑘𝑇 ]
Observer-based diagonal FDI problem
Derive a common mathematical model for network topology (graph) and plant (LTI system):
exploit structured systems theory that translates LTI system into a graph

Resilient control
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 malicious
nodes 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, 2014
A. D'Innocenzo, M.D. Di Benedetto, F. Smarra. Fault detection and isolation of malicious nodes in MIMO
Multi-hop Control Networks. 52nd IEEE CDC, 2013
F. Smarra, A. D'Innocenzo, M.D. Di Benedetto. Fault Tolerant Stabilizability of MIMO Multi-Hop Control
Networks. 3rd IFAC NecSys 2012
A. D'Innocenzo, M.D. Di Benedetto, E. Serra. Fault Tolerant Control of Multi-Hop Control Networks. IEEE
Transactions on Automatic Control, 58(6):1377-1389, 2013.

Challenges in Wireless Control Networks
Modeling
• Formal interfaces between control algorithms and wireless communication protocols
• Compositional models for scalable analysis and design of multiple control loops
Analysis
• Quantify impact of wireless networking on control performance
Design
• Controller design incorporating wireless network properties
• Control-network co-design
Robustness
• Robust with respect to packet losses and delays
• Tolerant with respect to failures and malicious intrusions – CPS Security (SafeCOP)
Tools
• Formal verification and automatic (co-)design of safe & secure WCN (SafeCOP)
• Co-simulation of control algorithms, communication protocols and physical systems
Experimental set-up
• WirelessHART laboratory (INCIPICT + SafeCOP)
• Building automation 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
giordano.pola@univaq.it

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

Our model of CPS
05/13
P1 P2 PN
C1 C2 CN
Computing Units:
Labelled transition systems
T = (Q, Q0, L, ,O,H)
:Ci

Our model of CPS
06/13
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 controllerSoftware & 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
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
 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
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
 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 Vehicle
and MicroGrids as CPS
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Autonomous Vehicle and MicroGrids
as CPS:
Challenges and Opportunities
Elena De Santis
L’Aquila University
Center of Excellence DEWS
L’Aquila, Jenuary 26th 2016
1/17
Autonomous Vehicle and MicroGrids as CPS

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Index
1 Introduction
2 Traﬃc Control
Motivations
Autonomous Vehicle
3 Power Systems
Motivations
DC Microgrid
4 Conclusions
2/172/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Presentation outline
Traﬃc Control: Development of an Adaptive Cruise Control
model able to imitate human driver behaviour
Power Systems Control: Analysis and control of a Direct
Current microgrid connected to renewables, storage systems
and loads
3/173/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
CPS
4/174/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
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 introduced
in the microscopic approach!
5/175/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Why Human-Inspired?
BREAKING NEWS!
6/176/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
State of the art
7/177/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Hybrid systems
H = (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

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Discrete States and related Domains
Important
Control based on information from
LEADER + ENVIRONMENT
9/179/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Power Systems Control
10/1710/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Change of paradigm
Energy Production
Energy Transportation
11/1711/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
DC Microgrid
Deﬁnition
Microgrid concept: a cluster of loads and microsources
operating as a single controllable system that provides
power to its local area.
12/1712/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Framework
13/1713/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Compressed Sensing
Problem:
Find a sparse solution to the under-determined set of
equations:
14/1714/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Why is interesting?
15/1715/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
References
Safe Human-Inspired Mesoscopic Hybrid Automaton for
Longitudinal Vehicle Control, A. Iovine, F. Valentini, E. De Santis,
M. Di Benedetto, M. Pratesi, 5th IFAC Conference on Analysis and
Design of Hybrid Systems (ADHS’15), Atlanta, 14-16 October 2015
A Safe Human-Inspired Mesoscopic Hybrid Automaton for
Autonomous Vehicles, A. Iovine, F. Valentini, E. De Santis, M. Di
Benedetto, M. Pratesi, to be submitted to IFAC journal Nonlinear
Analysis: Hybrid Systems (NAHS)
Management of the Interconnection of Renewables and Storages
into a DC Microgrid, A. Iovine, S. B. Siad, G. Damm, A. Benchaib,
F. Lamnabhi-Lagarrigue, E. De Santis, M. D. Di Benedetto, draft
Secure Estimation for Wireless Tracking Control under
Denial-of-Service Attacks G.Fiore, Y.H. Chang, Q.Hu, C. Tomlin,
M.D. Di Benedetto, draft
16/1716/17

Autonomous Vehicle
Elena De Santis
Introduction
Traﬃc Control
Motivations
Autonomous Vehicle
Power Systems
Motivations
DC Microgrid
Conclusions
Thanks for your attention!
Any Questions?
17/1717/17

Henry Muccini
henry.muccini@univaq.it
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: henry.muccini@univaq.it

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
Model
Transformation
Model
Weaving
Semantic
Wiki
DLSs
Editors
Megamodeling
Survey TSE 2013
+
Software Architecture
4
MDE
Architecting complex systems

Software and System Architecture
Comp
Comp
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
Connectors Technologies
25 years of work on
Software Architectures

Problem Statement
9
Q: How the Software Architecture community can
contribute to engineering CPSs?
Q: How our theories and methods can be adapted
to fruitfully design CPSs?
Q: What are the new design challenges in
architecting CPS?
Architecting Cyber Physical
Systems
More abstraction
New 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 Muccini
DISIM, University of L’Aquila, Italy
Joint work with Ivano Malavolta and Mohammad Sharaf
henry.muccini@univaq.it, @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: 8
energy/power
consumption: 8
efficiency: 6
SURVIVABILITY
heterogeneity: 29
distribution: 7
reconfigurability:7
mobility: 4
autonomy: 4
PORTABILITY
integrability: 20
adaptability: 19
portability: 3
independency: 2

RQ4: Solutions
13
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 applications
14
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 Muccini
GSSI, L’Aquila

SEA Group
Modeling
environment
Programming
Framework
Analysis
and
Code
Generati
on

Ivano Malavolta, Henry Muccini
GSSI, 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-Physical
Systems. 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 Systematic
Literature 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
TIME
NETWORKED

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
 Mixed-Criticality Systems
 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
 Advanced Processing Architectures
 SDR Platforms
 Many-core chips for TSR

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
Mixed-Criticality Systems
 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 a
promising technique to
implement robust software
architectures in multi-core
avionics platforms
 Analysis of paravirtualization
tools on a multi-processor
LEON4 platform specifically
designed for the aerospace
domain
 FentISS XtratuM
 SYSGO PikeOS
 Porting and analysis of hypervisor
solutions on FPGA based SoCs
12
PARTITION 1
HYPERCALL INTERFACE
KERNEL
MODE
USERMODE
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
KERNEL
MODE
USER
MODE

13
Picture: OpenSynergy/SYSGO - Mixed-Criticality: Hypervisors in networked cyber- physical systems

 Hardware mechanisms to support
isolation in a network-on-a-chip
 Isolation of different application
classes on NoC architectures
 Hardware mechanisms supporting
isolation to be introduced into existing
network interfaces
 Support for the execution of multiple
applications with different criticality
levels
 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 of
Heterogeneous Parallel Dedicated Systems that, starting from a
model of the system behaviour, based on a Concurrent Processes
MoC, leads to an heterogeneous parallel dedicated system able to
satisfy 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 Requirements
Non-functional Requirements
 Execution Time
 Power Dissipation
 Area
 …
How estimate parameters starting by measurements?
How to make measurements?
How to take measurements?
Global Monitor
System 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
Outputs
F/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
Interface
Time
measure
Event
Count
Filtering
Hardware sniffers
Nucleus
Current collaboration with UNIMORE to manage access to shared
resources and to monitor system activities

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

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
Advanced Processing Architectures
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

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 IntelligenceFrom Ambient Intelligence
to Cyber-Physical Systemsto Cyber-Physical Systems
Stefania CostantiniStefania Costantini
Pasquale CaianielloPasquale Caianiello
Giovanni De GasperisGiovanni De Gasperis
DISIMDISIM
Università 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

Ambient Intelligence (AmI)
• Computers and networks will be integrated
into the everyday environment rendering
accessible a multitude of services and
applications through easy-to-use human
interfaces. This vision of "ambient intelligence"
places the user, the individual, at the centre of
future developments for an inclusive knowledge
based society for all
• Now: Fog Computing, Cyber-Physical Systems

Ambient Intelligence (AmI)
• The Environment will be integrated by
intelligent interfaces supported by
computing and networking technology
which is everywhere, embedded in
everyday objects such as furniture,
clothes, vehicles, roads and smart
materials even particles of decorative
substances like paint

Ambient Intelligence: Vision

• Radically rethink the human-computer
interactive experience:
– Integrate digital world (information &
services) and physical world (physical
objects/environment)
– Make interfaces more responsive and
proactive (objects & environment monitor
user and (proactively) present
information & services relevant to user’s
current needs/interests)

Componenti dell’Ambient
Intelligence
• Ambient
– Materiali innovativi, Wearable Computing,
Sensori, Attuatori, Interfacce utente,
Infrastrutture di Comunicazione
• Intelligence
– Elaborazione del Linguaggio Naturale, Interfacce
Utente, Gestione dei Contenuti (Basi di
Conoscenza), Computational intelligence
(Intelligenza Artificiale,Agenti Intelligenti

Internet of Everything
• I dispositivi digitali sono
integrati negli oggetti di
tutti i giorni e nell‘ambiente
(ubiquità, pervasività)
• Essi comunicano tramite una
infrastruttura comune
invisibile e apparentemente
non intrusiva
• Non c‘è più un solo computer
per utente ma i vari
dispositivi interagiscono
mediante intelligenza
distribuita.

Un Possibile Futuro?
Ambient semantics or “enriching your every day experience”
– Book tells you about friends/famous people that loved
it
– 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 a
priori
• Sono autonomi
• Percepiscono l’ambiente
• Agiscono sull’ambiente
• Comunicano con altri agenti
• Possono avere obiettivi, svolgere
compiti

Agenti Intelligenti
(software)
• Interagiscono in modo flessibile con
l’ambiente
– Sopravvivono
– Imparano
– Si adattano
– Perseguono obiettivi
– Cooperano, competono, negoziano

26 gennaio 2016 S. Costantini - Intelligenza
Artificiale
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 - Intelligenza
Artificiale
17
Intelligenza come fenomeno
emergente
• Un agente software è dotato di un insieme
di 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 Laboratorio
AAAI@AQ,
Università degli Studi di L’Aquila
• Brevettato, usato in applicazioni reali (ad
es. CUSPIS)
– Disponibile su
• 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 interest
to you)
– Walking around town, system points out
buildings/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 il
profilo base
• Personalizzare informazioni e interazione
• Capire gli interessi dell’utente,
• Aggiornare il profilo

CUSPIS Demonstrator :
Villa Adriana

Domotica
• Si occupa dell'integrazione delle tecnologie che
consentono di automatizzare una serie di
operazioni all’interno della casa.
– Integrazione dei dispositivi elettrici ed elettronici, degli
elettrodomestici, dei sistemi di comunicazione, di
controllo e sorveglianza presenti nelle abitazioni.
Il termine domotica deriva dall’importazione del
neologismo francese domotique = domos automatique

Domotic and Smart Cities
• Obiettivo: abitare in case più sicure e
confortevoli, dotate di un sistema di
automazione semplice, affidabile, flessibile
ed economico
• Un sistema (teoricamente) alla portata di
tutti.
– Confort
– Sicurezza
– Risparmio energetico

Smart Buildings (Energy
Prosumers/Consumers)

Intelligent DALI Agents for
Smart Buildings
• Optimize personal confort according to
preferences and health conditions while
respecting overall objectives via a special
Interval Temporal Logic
• Objectives: keep comsumption/expense within
limits, 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
Prosumer node model
– real-time predictive
control of air
conditioning systems
in smart buildings in
the context of energy
management.
In general,
a PROSUMER NODE
in a smart grid is:
– A smart building that can produce, accumulate and have autonomy
of decision making about resource consumption, dealing with given
comfort constraints

A Predictive Model for the Automated Management of Conditioning Systems in Smart Buildings.
Speaker: Giovanni De Gasperis, University of L'Aquila, Italy
The predictive control needs a
good estimate of near future power
demand.
To achieve acceptable near future
estimates, we proposed a method based
on “Evidence combination”, measuring
performances of a bank of estimators over
time:
1. Simple Moving Average (SMA)
2. Functional Regression (FR)
3. Support Vector Regression (SVR)
4. Gradient Tree Boosting (GTB)
SMA FR SVR GTB
bank of
power
demand
estimators
Actual
Power
measures
performance assessment &
evidence combination
power demand
forecast
Cycling over 96 samples, 1 each quarter of
hour of the last 24
next
quarter
of
hour

A Multiagent Saver for the Automatic Management of HVAC Systems Speaker: Giovanni De Gasperis,
University of L'Aquila, Italy
Multi Agent Energy Saver Supervisor System
Architecture

What we intend to do:
Sostegno ai Disabili
• La disabilità non è una malattia, ma un “condizione
attuale” di una persona (World Health
Organization)
• Una persona disabile è temporaneamente o
definitivamente incapace di effettuare
determinate attività in modo “corretto” o “normale”
• La disabilità è correlata a situazioni nelle quali una
persona non è capace di gestire in modo adeguato
una situazione
– Per cause fisiche o cognitive
– Per cause esterne che creano limitazioni
Tutti noi siamo occasionalmente
disabili!

Ambient Intelligence/CPSs
per il Sostegno ai Disabili
• Localizzazione dell’utente nell’ambiente
circostante
• 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

• In casi estremi, prendere autonomamente
alcune decisioni (ad esempio sul dove e
come spostarsi)
• Imparare ad interpretare autonomamente
i pattern dei comportamenti quotidiani;
• Riconoscere segni di angoscia,
disorientamento,confusione

• Offrire un aiuto proattivo attraverso
diversi 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 essere
definita come un insieme ordinato multilivello di
informazioni dichiarative riferite agli eventi che si
verificano in un dato luogo e che coinvolgono
oggetti animati ed inanimati” [J. Crowley]
Context-awareness

Context-Awareness
• Rappresentare il contesto
– Ontologie (in Informatica): descrizione
formale delle tipologie che si assume
esistano in un dominio di interesse D
dalla prospettiva dell’individuo che usa
un linguaggio L al fine di parlare di D”.
• Percepire il contesto allargando la
descrizione con le nuove percezioni.

Dall’informazione di
contesto alla comunicazione
personalizzata
• Obiettivi
– adattività rispetto al contesto
– adattività rispetto al terminale utente
– personalizzazione rispetto al profilo
dell’utente

Dall’informazione di contesto alla
comunicazione personalizzata
– Interazione multimodale: testo, voce,
avatar
– Interazione controllata da un agente
intelligente

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 your
Attention!Attention!

1ST DISIM WORKSHOP ON ENGINEERING CYBER-PHYSICAL SYSTEMS

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