Das Internet der Dinge

Im Rahmen der Ringvorlesung Industrie 4.0

SS 2016, Universität zu Köln

Prof. Dr. Gregor Schiele FG Eingebettete Systeme der Informatik Uni Duisburg-Essen

Contents

1. What is the Internet of Things

2. Connected Devices Perspective

3. Connected Data Perspective

4. Conclusion

2

History

Term introduced 1999 by Kevin Ashton at Auto-ID Labs

Originally used to refer to a global network of RFID enabled objects

Many related terms: M2M Comm., Ubiquitous / Pervasive Computing, Cyber Physical Systems, Wireless sensor networks, IIoT, IoE, …

3

Definition: Internet of Things

“A global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies.” [ITU 2012]

4

[ITU 2012] International Telecommunication Unit: “Overview of the Internet of things”, Recommendation Y.2060, June, 2012

In Other Words…

Things connected to the Internet

That enable ‘advanced services’

5

‘Classical’ computer

‘Classical’ computer

‘Classical’ computer

Thing

Thing Thing

Thing

Thing

Thing

Thing

Thing Thing

The Internet

Internet Connected Things

An (IoT) thing is a physical object that is augmented with embedded electronics

Most basic thing: identifiable object (e.g. based on embedded passive RFID tags)

More advanced: sensing / smart object (e.g. based on embedded microcontroller, sensors / actuators and comm. interface)

6

7

8 http://www.tekreport.net/post/2016/02/26/smart-appliances

Chair of Information Systems II

Distributed Virtual Environments 9

Livescribe Echo Smartpen (2010)

10 Samsung NFC suit (2015)

Sensoria Fitness smart socks (2015)

Athos smart training clothes (2015)

fitb

it c

har

ge H

R (

20

15

)

UC Berkeley’s Smart Dust (2001)

Experimental system for autonomous sensing and

communication in cubic mm

Optical comm.

Using corner cube

retro-reflector and

steered laser

No commercial

solution (so far)

11

Networked Processors in Cars

12

http://www.heise.de/ct/03/14/170/

IoT System Overview

13

Smart Things IoT Gateways Online Services

Internet

Important Technology Trends

1. Computing

Miniaturization

Affordability

2. Sensing

Variety

Availability

3. (Wireless) Transmission

Communication

Energy transmission

14

One computer

(mainframe)

for many people

SIZE NUMBER

One computer

(PC) for

everyone

Many computers

for everyone

GPS receiver ZigBee transceiver

type: book name: DCOM author: Don Box

type: person name: Schiele gender: male

type: car name: XY-365 speed: 30 mph

25 °C

type: car name: GN-WK-1 speed: 30 mph

type: truck

name: AB-444 destination: Redmond load: 60%

type: person name: Jones gender: male

type: person name: Smith gender: male type: sensor

name: 144377 kind: temperature

type: book name: DCOM author: Don Box

type: person name: Jones gender: male

type: person name: Jones gender: male

Expected Business Impact of the IoT

Everybody agrees that the IoT will be huge...

McKinsey Global Institute: $2.7-$6.2 trillion

annually by 2025

General Electric: $32.3 trillion (46% of today‘s

global economy today) total worth

Cisco: $14.4 trillion net profit over next

decade

17

[source: M. Fell: Roadmap for The Emerging Internet of Things, carré & strauss, 2014, http://carre-strauss.com/documents/IoT_Roadmap.pdf]

IoT Market Opportunities

1. Data and process integration across full value chain and product lifecycle

2. New business models and services based on (seemingly) unrelated data

18

Hype Cycle for Emerging Technologies, 2015

19 http://www.gartner.com/newsroom/id/3114217

Two Views on IoT

Connected Things

Connected Data

20

vs

Contents

1. What is the Internet of Things

2. Connected Devices Perspective

3. Connected Data Perspective

4. Conclusion

21

Connected Devices

Everything becomes a smart object / thing

Things networked using Internet technologies (e.g. IP)

Things use Internet services to cooperate, i.e. the majority of users of services are devices and services instead of humans

22

[source: Cisco]

2003 2010

2015

2020

2020: 50 billion 2008: more devices than users in the Internet

users

devices

IoT: Connected Devices

IoT: Connected Devices

Topics include e.g.

Low power (machine-to-machine) networking

Ad hoc (system) auto configuration and self-organisation

Scalable Internet Infrastructure (bandwidth, number of devices, networks)

24

A Typical IoT Protocol Stack

25

CoAP / MQTT / XMPP

(TCP) UDP ICMP

IPv6 with 6LoWPAN

IEEE 802.15.4

HTTP / RTP

UDP TCP ICMP

IPv4 / IPv6

Ethernet / WLAN

TCP/IP Internet Stack: IoT Stack:

Hundreds / thousands of bytes Tens of bytes

Internet = IoT?

Established Internet protocols have been very successful

Why do we need new / modified ones?

26

A Typical Internet Device

High performance multicore 64bit CPUs

Gigabytes of RAM

Terabytes of HDD

Multithreading OS with standard TCP/IP stack

27

A Typical IoT Device

28 Adafruit FLORA (Arduino compatible) smart textile board

Low power 8bit microcontroller

Kilobytes of RAM & ROM

No / restricted OS support

Challenges

Data item sizes

Inefficient content encoding

Huge overhead, difficult parsing

Not optimised for energy saving

(Relatively) large buffers needed

Dedicated end-point addressing

Pull vs. push

29

A Typical IoT Protocol Stack

30

CoAP / MQTT / XMPP

(TCP) UDP ICMP

IPv6 with 6LoWPAN

IEEE 802.15.4

IoT Stack: CoAP: (binary) redesign of HTTP with push ability

MQTT: (loosely coupled) message queues

TCP typically too much overhead

6LoWPAN: adaptation layer for IPv6

802.15.4: low-power, low-cost, low speed, low range WLAN alternative

IEEE 802.15.4

Low range: 10 m

Low speed: up to 250 kbit/s

Low cost & power: ~10 times less than WLAN

Optimised for (infrequent) sending of small

(sensor) messages

31

802.15.4 Network Topologies

32

PAN coordinator Reduced function device

Communication flow

Adapted from: E. Callaway, P. Gorday, L. Hester, J. A. Gutierrez, M. Naeve, B. Heile: “Home Networking with IEEE 802.15.4: Developing Standard for Low-Rate Wireless Personal Area Networks”, IEEE Communications Magazine, August 2002.

Full function device

Star network Comm. only with PAN coord.

Peer-to-peer network Comm. with everyone in range

802.15.4 Channel Frequencies

33

Per Lab

Channel frequencies

(20 Kbps) (40 Kbps)

(250 Kbps)

Europe USA

From IEEE 802.15.4 to IEEE 802.15.4e 9

Ed Callaway, Paul Gorday, Lance Hester, Jose A. Gutierrez, Marco Naeve, Bob Heile, Home Networking with IEEE 802.15.4:

Developing Standard for Low-Rate Wireless Personal Area Networks, IEEE Communications Magazine, August 2002.

Since April 2009: 314–316 MHz, 430–434 MHz, 779–787 MHz bands in China (802.15.4c); 950–956 MHz band in Japan (802.15.4d); since August 2007 (IEEE 802.15.4a): ultra-wideband (UWB) PHY in 3 ranges: below 1 GHz, between 3 & 5 GHz, between 6 & 10 GHz.

Adapted from: E. Callaway, P. Gorday, L. Hester, J. A. Gutierrez, M. Naeve, B. Heile: “Home Networking with IEEE 802.15.4: Developing Standard for Low-Rate Wireless Personal Area Networks”, IEEE Communications Magazine, August 2002.

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Worldwide

Contents

1. What is the Internet of Things

2. Connected Devices Perspective

3. Connected Data Perspective

4. Conclusion

34

Connected Data

Things (continuously) create data about their

physical surroundings

Data is made available on the Internet, i.e. the

majority of data is created automatically by

smart things instead of humans

Aka: big data

35

IoT: Connected Data

Topics include e.g.

Interoperable data models (e.g. RDF)

Data management and querying (e.g. CQELS)

Data analytics (big data)

36

Connected Data: Today

New content: 300h of video per minute

432,000h of video per day

236 TByte per day

164 GByte per minute 37

http://500px.com/photo/19080871

Problem: Data Size 43.9 Million PKW (Germany, 1.1. 2014)

3-4 GByte / min per PKW

154 PByte / min

0

20000

40000

60000

80000

100000

120000

140000

160000

Pkw D Youtube

0.164 TB/min

Tera

Byt

e /

min

(source: Kraftfahrt-Bundesamt)

Problem: Data Heterogeneity

39

http://www.flickr.com/photos/69730904@N03/8813574238/

Linked Data common

data models

(A) Solution: Linked Data

40

http://www.flickr.com/photos/69730904@N03/8813574238/

Linked Data

As of September 2011

MusicBrainz

(zitgist)

P20

Turismo

de

Zaragoza

yovisto

Yahoo! Geo

Planet

YAGO

World

Fact-

book

El ViajeroTourism

WordNet

(W3C)

WordNet

(VUA)

VIVO UF

VIVO Indiana

VIVO Cornell

VIAF

URIBurner

Sussex

Reading

Lists

Plymouth

Reading

Lists

UniRef

UniProt

UMBEL

UK Post-codes

legislationdata.gov.uk

Uberblic

UB Mann-heim

TWC LOGD

Twarql

transportdata.gov.

uk

Traffic

Scotland

theses.

fr

Thesau-

rus W

totl.net

Tele-

graphis

TCM

Gene

DIT

TaxonConcept

Open Library (Talis)

tags2con

delicious

t4gm

info

Swedish

Open

Cultural Heritage

Surge

Radio

Sudoc

STW

RAMEAU SH

statistics

data.gov.

uk

St.

Andrews

Resource

Lists

ECS

South-

ampton

EPrints

SSW

Thesaur

us

Smart

Link

Slideshare

2RDF

semantic

web.org

Semantic

Tweet

Semantic XBRL

SW

Dog

Food

Source Code Ecosystem Linked Data

US SEC (rdfabout)

Sears

Scotland

Geo-

graphy

Scotland

Pupils &

Exams

Scholaro-

meter

WordNet (RKB

Explorer)

Wiki

UN/

LOCODE

Ulm

ECS (RKB

Explorer)

Roma

RISKS

RESEX

RAE2001

Pisa

OS

OAI

NSF

New-

castle

LAAS

KISTI

JISC

IRIT

IEEE

IBM

Eurécom

ERA

ePrints dotAC

DEPLOY

DBLP (RKB

Explorer)

Crime Reports

UK

Course-

ware

CORDIS (RKB

Explorer)CiteSeer

Budapest

ACM

riese

Revyu

research

data.gov.

ukRen.

Energy

Genera-tors

reference

data.gov.

uk

Recht-spraak.

nl

RDFohloh

Last.FM (rdfize)

RDF Book

Mashup

Rådata nå!

PSH

Product Types

Ontology

ProductDB

PBAC

Poké-

pédia

patents

data.go

v.uk

Ox

Points

Ord-nance Survey

Openly Local

Open Library

OpenCyc

Open Corpo-rates

OpenCalais

OpenEI

Open

Election

Data

Project

Open

Data

Thesau-

rus

Ontos News Portal

OGOLOD

JanusAMP

Ocean Drilling Codices

New

York

Times

NVD

ntnusc

NTU

Resource

Lists

Norwe-

gian

MeSH

NDL subjects

ndlna

myExperi-ment

Italian

Museums

medu-

cator

MARC

Codes

List

Man-

chester

Reading

Lists

Lotico

Weather

Stations

London Gazette

LOIUS

Linked Open Colors

lobidResources

lobidOrgani-sations

LEM

LinkedMDB

LinkedLCCN

LinkedGeoData

LinkedCT

Linked

User

Feedback

LOV

Linked Open

Numbers

LODE

Eurostat (Ontology

Central)

Linked

EDGAR (Ontology

Central)

Linked

Crunch-

base

lingvoj

Lichfield

Spen-

ding

LIBRIS

Lexvo

LCSH

DBLP (L3S)

Linked Sensor Data (Kno.e.sis)

Klapp-

stuhl-

club

Good-

win

Family

National

Radio-

activity

JP

Jamendo

(DBtune)

Italian

public

schools

ISTAT Immi-gration

iServe

IdRef Sudoc

NSZL Catalog

Hellenic

PD

Hellenic FBD

Piedmont

Accomo-

dations

GovTrack

GovWILD

Google

Art

wrapper

gnoss

GESIS

GeoWordNet

GeoSpecies

GeoNames

GeoLinkedData

GEMET

GTAA

STITCH

SIDER

Project

Guten-

berg

Medi

Care

Euro-

stat

(FUB)

EURES

DrugBank

Disea-

some

DBLP (FU

Berlin)

Daily

Med

CORDIS(FUB)

Freebase

flickr wrappr

Fishes of Texas

Finnish

Munici-

palities

ChEMBL

FanHubz

EventMedia

EUTC

Produc-

tions

Eurostat

Europeana

EUNIS

EU

Insti-

tutions

ESD

stan-

dards

EARTh

Enipedia

Popula-

tion (En-

AKTing)

NHS(En-

AKTing) Mortality

(En-

AKTing)

Energy

(En-

AKTing)

Crime

(En-

AKTing)

CO2

Emission

(En-

AKTing)

EEA

SISVU

education.data.g

ov.uk

ECS

South-

ampton

ECCO-

TCP

GND

Didactalia

DDC Deutsche

Bio-

graphie

data

dcs

MusicBrainz

(DBTune)

Magna-

tune

John

Peel (DBTune)

Classical

(DB

Tune)

AudioScrobbler (DBTune)

Last.FM artists

(DBTune)

DBTropes

Portu-guese

DBpedia

dbpedia lite

Greek DBpedia

DBpedia

data-open-ac-uk

SMC

Journals

Pokedex

Airports

NASA

(Data

Incu-

bator)

MusicBrainz(Data

Incubator)

Moseley

Folk

Metoffice Weather Forecasts

Discogs (Data

Incubator)

Climbing

data.gov.uk intervals

Data Gov.ie

databnf.fr

Cornetto

reegle

Chronic-ling

America

Chem2Bio2RDF

Calames

business

data.gov.

uk

Bricklink

Brazilian Poli-

ticians

BNB

UniSTS

UniPath

way

UniParc

Taxonomy

UniProt(Bio2RDF)

SGD

Reactome

PubMedPub

Chem

PRO-SITE

ProDom

Pfam

PDB

OMIMMGI

KEGG

Reaction

KEGG Pathway

KEGG

Glycan

KEGG Enzyme

KEGG

Drug

KEGG

Com-

pound

InterPro

HomoloGene

HGNC

Gene

Ontology

GeneID

Affy-metrix

bible

ontology

BibBase

FTS

BBC

Wildlife

Finder

BBC Program

mes BBC Music

Alpine

Ski

Austria

LOCAH

Amster-dam

Museum

AGROVOC

AEMET

US Census (rdfabout)

Media

Geographic

Publications

Government

Cross-domain

Life sciences

User-generated content

The Linked Open Data Cloud

Over 200 open data sets with more than 25 billion facts,

interlinked by 400 million typed links, doubling every 10 month!

see: http://lod-cloud.net/

Linking Open Data cloud diagram, by M. Schmachtenberg, C. Bizer, A. Jentzsch, Richard. Cyganiak

Media

Government

Geo

Publications

User-generated

Life sciences

Cross-domain

US government UK government

BBC New York Times

LinkedGeoData

BestBuy Overstock.com Facebook

Two Key Ingredients

1. RDF – Resource Description Framework Graph based Data – nodes and arcs

• Identifies objects (URIs)

• Interlink information (relationships)

2. Vocabularies (Ontologies)

• provide shared understanding of a domain

• organise knowledge in machine-comprehensible way

• give an exploitable meaning to the data

42

Cities:Dublin

84421km2

Geo:IslandOfIreland

EU:RepublicOfIreland

Geo:locatedOn

Geo:area

Geo:hasCapital

Geo:hasLargestCity

Wikipedia.org

Gov.ie

EU:RepublicOfIreland

Person:EndaKenny

Gov:hasTaoiseach

Gov:hasDepartment

IE:DepartmentOfFinance

Why Graphs and Ontologies?

43

Goal: Integrating IoT in LOD Cloud

44

http://www.flickr.com/photos/69730904@N03/8813574238/

Linked Data

Advantages of IoT Linked Data

IoT becomes part of the WWW

Open, independent standards and platforms

Global, established developer base

Extensible, open world

… but there is a problem

45

Linked Data and IoT

46

http://www.flickr.com/photos/69730904@N03/8813574238/

Linked Data

Challenges

47

‘classical LD’ IoT LD

Dynamism static, offline analysis Highly dynamic, online analysis

System resources

HPC, multicore CPUs, GBs RAM, TBs/PBs storage, fixed electrical

power grid connection

Embedded processors, kBs RAM, storage?, battery operated

Security and access control

manual, coarse grained access control

automated, fine grained access control

From Self-describing Data & Devices

Self-describing sensors (RDF)

Extended W3C SSN Ontology

Automatic discovery & integration

iSense nodes <http://www.example.org/s1> a ssn:Sensor;

ssn:onPlatform <http://www.example.org/

smartphone1>;

spt:obs ld4s:temperature;

spt:uom ld4s:centigrade;

corelf:bt "12-08-28T19:03Z";

spt:temporal ld4s:temporalprop3>;

spt:archive <http://www.example.org/

archive/historical_archive_a>.

ld4s:temporalprop3> a

spt:SensorTemporalProperty;

ssn:featureOfInterest ld4s:water;

dul:hasLocation ld4s:galway.

RDF storage

Semi-automatic annotation

with additional metadata

Self-sufficient sensor nodes

Central environment model

Export as standard

SPARQL end point

tim

e

sen

sors

en

viro

nm

en

t m

od

el

LOD

Clo

ud

…

To Self-describing Systems

To Semantic Event Streams

50

IoT Systems

LOD Cloud

Client

Client

...

Semantic Event

Processing

Contents

1. What is the Internet of Things

2. Connected Devices Perspective

3. Connected Data Perspective

4. Conclusion

51

What is the Internet of Things?

Imagine a World Were…

Every physical object is enhanced with a

computer

The Internet integrates all these computers

– wherever they are

Computers detect their physical surroundings

– including their users

… 52

What is the Internet of Things?

…

All this data is integrated, analysed and made

available globally – in (near) real-time

Results are send back to control / adapt the

physical world

The world becomes observable and

programmable in real-time 53

What will Become Possible?

Never forgetting anything you see or hear – forever?

Knowing what someone feels?

Knowing who has touched an object you have just acquired?

Changing your environment by waving a hand or thinking?

Detecting and influencing thinking?

Predicting the future?

…

54

Questions

55

ES

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GTE

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ET T E T E

der Informatik

Bismarckstr. 90 Gebäude BC 1. Etage 47057 Duisburg https://www.uni-due.de/es/ gregor.schiele@uni-due.de Tel: +49 203 379 3620

Linked Data