The Internet of Things – A Revolution not to...

The Internet of Things –A Revolution not to Miss

Joseph SifakisRiSD Lab, EPFL

18th Panhellenic Conference on Informatics

Harokopio UniversityAthens

October 4, 2014

The ICT Revolution – Overwhelmingly Exploding

Evolution driven by exponential progress in technology and explosion of applications

1936 1945 1960 1970 1980 1990 2000 2010

Foundations -Alan Turing, Kurt Gödel

Systems Everywhere – Vital for Modern Societies

From SW to Systems – Significant Differences

Resources HealthBuildings Transport Communications

System(SW+HW)

Shift of focus from SW to SystemsSystems are hard to design due to unpredictable and subtle interactions

with their environment, rather than to complex data and algorithms

From SW to Systems – New Trends

5

New trends break with traditional Computing Systems Engineering. It is hard to jointly meet technical requirements such as:

Reactivity: responding within known and guaranteed delaye.g. flight controller

Autonomy: provide continuous service without human intervention e.g. no manual start, optimal power management

Dependability: guaranteed minimal service in any case e.g. resilience to attacks, hardware failures, software execution errors

Scalability: at runtime or evolutionary growth (linear performance increase with resources)e.g. reconfiguration, scalable services

Technological challenge: Capacity to design systems of guaranteed functionality and quality, at acceptable costs.

...and also take into account economic requirements for optimal cost/quality

OVERVIEW

6

Technological Convergence

The Internet of Things

The Vision

Requirements

Challenges

System Design

Linking Physicality and Computation

Components

Intelligent Systems

Discussion

Convergence


7

Install and maintain a single converged network infrastructure – towards an all-IP network?

Integration of the traditional service-specific devices into devices providing converged services

Integration of services into packages which are sold to the consumers

Convergence of inter-sector policies in the context of global development: harmonize standards, regulate content and QoS, to enable pervasiveness of technologies

Technological Convergence – For a Smarter Planet

IBM’s initiative for a smarter planet

INSTRUMENTED: We now have the ability to measure, sense and see the exact condition of practically everything.

INTERCONNECTED: People, systems and objects can communicateand interact with each other in entirely new ways

INTELLIGENT: We can respond to changes quickly and accurately, bypredicting events and optimizing resources

Technological Convergence – Mobiles Services

Technological Convergence – The Google Universe

OVERVIEW

13



The Vision

Requirements

Challenges

System Design


Components

Intelligent Systems

Discussion

The Internet of Things – The Vision

14

We see the IoT as billions of smart, connected “things” – a sort of “universal global neural network” in the cloud– that will encompass every aspect of our lives and its foundation is the intelligence that embedded processing provides

A quick Internet search highlighted the following example use cases and applications under consideration:

Machine-to-machine communication Machine-to-infrastructure communication Telehealth: remote or real-time pervasive monitoring of patients,

diagnosis and drug delivery Asset tracking of goods on the move Automatic traffic management Remote security and control Environmental monitoring and control Home and industrial building automation “Smart” applications, including cities, water, agriculture, buildings, grid,

meters, broadband, cars, appliances, tags, animal farming, etc.

The Internet of Things – The Vision

The Internet of Things – Behavioral Segmentation

The Internet of Things – Industrial Internet

The Internet of Things – Industrial Internet Data Loop

OVERVIEW

22



The Vision

Requirements

Challenges

System Design


Components

Intelligent Systems

Discussion

The Internet of Things – Requirements

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1. Local Sensor/Actuator Nodes

carrying a unique ID and controlled separately via a remote command and control topology e.g. through a smartphone with RFID and/or NFC and GPS functionality

including cameras; water or gas flow meters; thermostats; radar vision; RFID readers; doors and locks with open/close circuits

2. Layers of Local Embedded Processing Nodes

providing the “real-time” embedded processing that is a key requirement of most IoT applications, e.g. microcontrollers and microprocessors

connected to hierarchically structured architectures with a master device that can communicate via the Internet with a variety of “clients,” e.g. service providers that can give the user access to remotely control all of these connected “things.”


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3. Wired and Wireless Communication Capability


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3. Wired and Wireless Communication Capability

We need an integrated network infrastructure covering a large variety of characteristics (few kilobytes to high-bandwidth, timed or asynchronous, any distance). to transfer information gathered by the sensing nodes and processed

by local embedded processing nodes to various to transfer back processed information and commands to the local

embedded processing nodes to execute a task.

The emergence of this infrastructure is likely not to happen before 10 years. It will be the result of a battle between existing technologies, revision of standards and new standards new technologies e.g. Wireless Sensor and Actuator Networks for

environment control


26


27

Energy efficiencyOften, the sensing nodes are battery-operated, so a low-power spec is a basic requirement.

Cost-effectiveness As with any other market, mass adoption will not take place until a certain

price point for the solutions is reached. The overall cost is the sum of the parts of the system plus the cost of the

services required for the system

Reliability Stringent requirements and harsh environmental conditions must be

supported. Product life cycles in the industrial market are at least 10-15 years, in

contrast to mobile phones, laptops or other electronic devices that may change every two years

.


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Full Security Across the Entire Signal Path

Timely Information Availability The assurance that the services and their underlying infrastructure

can process, store and deliver the data when and where it’s needed is the first aspect of a secure system

Data Confidentiality Safeguarding the information obtained by IoT services is critical,

otherwise those services will lose the users’ trust We need tradeoffs between data availability for data mining/push

services and data confidentiality.

Data Integrity If data cannot be trusted the entire service paradigm around that

data will break down. New type of malware has targeted electronic process control

systems for the first time Siemens process control systems at nuclear plants during the summer 2010

OVERVIEW

29



The Vision

Requirements

Challenges

System Design


Components

Intelligent Systems

Discussion

System Design – The Significant Difference

Mature systems engineering disciplines are based on solid theory for building artefacts with predictable behavior over their life-time.

Computing systems engineering is at the same stage of development as was Mechanics in the Middle Ages

Science is lagging behind technology – complex systems are built empirically

Current theoretical approaches are inoperable – correctness cannot be theoretically guaranteed

System Design – Two Main Gaps

Req

uire

men

ts(d

ecla

rativ

e)

App

licat

ion

SW

(exe

cuta

ble)

Sys

tem

(HW

+SW

)

Correctness? Correctness?

Pro

gram

min

g

Impl

emen

tatio

n

System Design – Requirements

32

Trustworthiness requirements express assurance that the designed system can be trusted that it will perform as expected despite

HW failures Design Errors Environment Disturbances

Malevolent Actions

Optimization requirements are quantitative constraints on resources such as time, memory and energy characterizing

1) performance e.g. throughput, jitter and latency; 2) cost e.g. storage efficiency, processor utilizability3) tradeoffs between performance and cost

System Design – Trustworthiness vs. Optimization

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Trustworthiness requirements characterize qualitative correctness – a state is either trustworthy or not

Non Trustworthy States

Optimization requirements characterize execution sequences

Trustworthiness vs. Optimization The two types of requirements are often conflicting System design should determine tradeoffs driven by cost-effectiveness

and technical criteria

System Design – Levels of Criticality

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Safety critical: a failure may be a catastrophic threat to human lives

Security critical: harmful unauthorized access

Mission critical: system availability is essential for the proper running of an organization or of a larger system

Best-effort: optimized use of resources for an acceptable level of trustworthiness

System Design – Reported Failures

System Design – The Cost of Trustworthiness

System Design – The Cost of Trustworthiness

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System Design – Verification

Verification Method

Requirements

YES, NO, DON’T KNOW

Should be: faithful e.g. whatever

property is satisfied for the model holds for the real system

generated automatically from system descriptions

Should be: consistent

e.g. there exists some model satisfying them

complete e.g. they tightly characterize the system’s behavior

Present systems are not trustworthy!

$1,000 per line of code for “high-assurance” software!

Model

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System Design – Limitations of Formal Verification

Requirements formalization We lack adequate languages e.g. to describe security requirements

and optimization requirements, in general We need theory and tools for checking consistency and completeness

Faithful system modeling We need theory for building models that faithfully represent the

behavior of an application software running on a given platform We need theory for building hybrid system models representing the

interaction between a system and its environment

Establishing correctness verification is limited to small or medium size monolithic software or

hardware and to formalizable requirements Attempts to apply compositional verification to complex systems

have failed

OVERVIEW

40



The Vision

Requirements

Challenges

System Design


Components

Intelligent Systems

Discussion

41


SYSTEMSoftware: application SW middleware OS

Environment: deadlines jitter throughput

HW Platform: CPU speed memory power failure rates temperature

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Software: application SW middleware OS

SYSTEM



SW Design cannot ignore HW design

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SW Design cannot ignore control design

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System Design coherently integrates all these

We need to revisit and revise computing to integrate methods from EE and Control

Cyber-physical systems refer to the next generation of engineered systems requiring tight

integration of computing, communication, and control technologies

tightly combine the continuous dynamics (systems of differential equations) with the discrete dynamics of cyber systems (SW+HW).

are important in overcoming many challenges in energy, environment, transportation, and health care e.g., to achieve stability, performance, reliability, robustness, and efficiency

Linking Physicality and Computation – Cyber-physical Systems

The emergence of Cyber-physical systems has been enabled by micro-scale and nano-scale design and fabrication technologies e.g.,

sensors, actuators, and processors that are small, cheap, fast, and energy efficient

advances in system software, from high performance computing systems to real-time embedded systems

wireless networks making feasible connectivity of mobile nodes.


Building smart systems as the composition of components whose cyber and physical parts are concurrently designed

OVERVIEW

48



The Vision

Requirements

Challenges

System Design


Components

Intelligent Systems

Discussion

Components

Building complex systems by composing a small number of types of components is essential for any engineering discipline.

This confers numerous advantages such as mastering complexity, enhanced productivity and correctness through reuse

Component composition orchestrates interactions between components. It lies at the heart of the system integration challenge.

No Common Component Model for Computing Systems Engineering!

Components – The Babel of Languages

System designers deal with a large variety of components, with different characteristics, from a large variety of viewpoints, each highlighting different dimensions of a system

Verilog VHDL SystemC

Statecharts

SysML

Matlab/Simulink,

AADL

BPELJavaTSpaces Concurrent Fortran

NesC

MPICorba

Javabeans.NET

SWbus

Softbench

TLM

C

SES/Workbench

Fractal

Consequences: Using semantically unrelated formalisms e.g. for programming, HW

description and simulation, breaks continuity of the design flow and jeopardizes its coherency

Costly system development decoupled from validation and evaluation.

Components – Component Heterogeneity

InteractionThere exists a large variety of mechanisms used to express interaction between components e.g. semaphores, monitors, locks, function call, asynchronous message passing, rendezvous, broadcast ….

Execution mode Synchronous components (HW, Multimedia application SW) Asynchronous components (General purpose application SW) GALS

Programming styles Thread-based or Actor-based programming Imperative or Functional programming

We need a unified component composition paradigm for describing and analyzing the coordination between components in terms of tangible, well-founded and organized concepts

Components – Correctness-by-Construction

Compositionality rules guarantee that composite components inherit essential properties of constituent components


Compositionality rules guarantee that composite components inherit essential properties of constituent components

We need compositionality results for progress properties such as. deadlock-freedom and liveness


Composability rules guarantee that adding new components does not jeopardize essential properties of integratedcomponents

Feature interferencein OS, middleware,telecommunicationsystems and web services are all due to lack of composability!

OVERVIEW

55



The Vision

Requirements

Challenges

System Design


Components

Intelligent Systems

Discussion

Intelligent Systems

IBM “WATSON” (2011)The Jeopardy!-playing

question answering system

Supercomputers may be used explore a (large) predefined space of solutions or to combine predefined knowledge

IBM Deep Blue (1997)

Intelligence is the ability to create knowledge by applying rules of reasoning: deduction, abduction (inference to the best explanation), induction (generalization from a finite number of facts)

Intelligent Systems – Knowledge Engineering

Knowledge is truthful information that can be embedded into the right network of conceptual interrelations that can “provide reason” for them e.g. a logical proof, some reasonable support (dialectically) an explanation (e.g., causally) a clarification (e.g., through an analogy)

Challenges analyzing semantic information of the cyberspace in terms of well-formed,

meaningful and veridical data. How does semantic information upgrade to knowledge?

quest for a theory of truth. What does it mean for semantic information to be truthful?

integrating knowledge into computing systems in order to solve complex problems normally requiring a high level of human expertise (Knowledge-based engineering)

Intelligent Systems – Knowledge Engineering

.Food for Thought Unlimited

Information Facts Data Software

Formalized Knowledge Mathematics Physics Biology Computing Social Sciences

The Internet

Models of the labor market?

What is the

probability that…..

next week?

Online design tools?

The Semantic WEB

Intelligent Systems – Adaptivity

Security ThreatsHW

failures

Varying ET Varying Load

Mitigation

SY

STE

MC

ON

TRO

LLE

R Learning

Objective Management

Planning

System state

Parametersteering

Systems must provide services meeting given requirements in interaction with intrinsically uncertain (non-deterministic) environments

Adaptivityconsists in using control-based techniques to ensure correctness despite uncertainty

Intelligent Systems – Adaptivity

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Planning

Learning

Management of objectives

Movie would have been better …

Go to: 1) Stadium 2) Movie 3) Restaurant

OVERVIEW

61



The Vision

Requirements

Challenges

System Design


Components

Intelligent Systems

Discussion

Discussion – Over-hyped Technology?Gartner 12/08/2014: “The IoT is the most over-hyped technology in development today”“lack of standardization in the area, as well as the changing nature of the technology itself, as part of the reason why widespread adoption is further than its promoters think”

Discussion – Do My Washer and Grill Have to Talk?

Connectivity must enhance the user experience, not simply be included because it is possible to do so.

benefit provided by connectivity is arguably minimal when its cost is potentially compromised network security.

“I always hear about the refrigerator that figures out what you are out of and orders more for you. Except this is easily accomplished by opening the door and taking a look, which you are going to get around to sooner or later if you eat food. And I don't buy the same food every week, so I certainly wouldn't want the refrigerator ordering food for me.”

An internet fridgein 2002

Discussion – Security is a main Concern

Discussion – A Revolution not to Miss

Europe had traditionally held strong positions in industrial sectors where embedded systems are key to their development e.g. mobile communications, automotive, avionics and space, rail, energy, consumer electronics, medical devices – The main competitors used to be Asian players

Europe is losing strong positions in industrial sectors where embedded systems are key to their development e.g. mobile communications, automotive, energy, consumer electronics, medical devices.

Some examples: Rise of Samsung and Apple - decline of Nokia (acquired by

Microsoft), Bankruptcy of BenQ Mobile ( formerly Siemens Mobile) Exit of Ericsson from the Sony-Ericsson joint venture Continued decline of Alcatel-Lucent

Discussion – A Revolution not to Miss

Increasing integration and technologic convergence is blurring the boundaries between sectors opens the way for new interoperability standards is breaking down the walls of niche markets and opening global

markets

This revolution, led once again by US software companies, confirms the strategic role of software technologies

Thank you

The Internet of Things – A Revolution not to...

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