45
M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Embed Size (px)

Citation preview

Page 1: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M and Semantic Sensor Web

KAIST KSEUichin Lee

Page 5: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M Definition• M2M 은 기계간의 통신 (machine-to-machine) 및 사람이

동작하는 디바이스와 기계간의 통신 (man-to-machine) 을 의미하며 , 광의적으로는 통신 과 IT 기술을 결합하여 원격지의 사물 , 차량 , 사람의 상태 / 위치정보 등을 확인 가능한 제반 솔루션 의미

* 출처 : KT M2M 사업추진 방향

Page 6: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M Definition• 사람 , 사물 및 환경에 대한 정보를 감지 , 저장 , 가공 , 통합 할 수 있고

언제 어디서나 안전하고 편리하게 원하는 맞춤형 지식 / 지능 정보서비스를 제공할 수 있는 차세대 방송통신 융합 ICT 인프라 (방송통신위원회 )

– 통합 / 융합 : 다양한 방송통신망 (2G, 3G, WiBro 등 ) 의 통합 , 이종 (ICT+ 비 ICT) 융합 서비스 제공이 가능한 지능기반 네트워크

– 광대역 / 모빌리티 / 글로벌화 : 수천억개의 사물 간 정보교환을 위해 광대역 /이동성이 보장 , 인터넷 기반으로 세계 어느 곳에서도 사물정보의 상호 교환이 가능

– 보안 / 품질 보장화 : 공공 / 민간의 중요한 사물 정보 및 서비스에 대한 차별화 된 보안 및 고품질 보장이 가능

– 고기능화 : IPv6 기반으로 u-City 등 대규모 사물정보 서비스 제공에 적합

• 주로 단방향적인 지식 / 지능 정보 전달 서비스에 중점을 둠

Page 7: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2MGateway

ClientApplication

M2M ApplicationM2M Area Network

M2M Architecture (ETSI)

7

ApplicationDomain

Network Domain

M2M Device Domain

Service Capabilities

M2MCore

* 출처 : ETSI M2M 소개

Page 8: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M Device Domain• M2M Device

– A device that runs application(s) using M2M capabilities and network domain functions. An M2M Device is either connected straight to an Access Network or interfaced to M2M Gateways via an M2M Area Network.

• M2M Area Network– A M2M Area Network provides connectivity between M2M Devices and

M2M Gateways. Examples of M2M Area Networks include: Personal Area Network technologies such as IEEE 802.15, SRD, UWB, Zigbee, Bluetooth, etc or local networks such as PLC, M-BUS, Wireless M-BUS.

• M2M Gateways– Equipments using M2M Capabilities to ensure M2M Devices

interworking and interconnection to the Network and Application Domain. The M2M Gateway may also run M2M applications.

Page 9: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M Network/App Domain

• Network Service Capabilities– Provide functions that are shared by different applications – Expose functionalities through a set of open interfaces – Use Core Network functionalities and simplify and optimize

applications development and deployment whilst hiding network specificities to applications

– Examples include: data storage and aggregation, unicast and multicast message delivery, etc.

• M2M Applications (Server)– Applications that run the service logic and use service

capabilities accessible via open interfaces.

Page 10: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M Market Characteristics

• Initial investment is difficult (e.g., license fees)• Complex supply chain: from chipset to

network to mobile operators• Long-tail business• Low ARPU (<$10) compared to voice (<$30) • Lagging standards

Page 11: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M Standard Trends

• So far heterogeneous M2M devices/platforms– SKT/KT/LG M2M platforms– Orange M2M Connect– Nokia M2M Gateway– Sprint Business Mobility Framework

• M2M standard activities for interoperability– Access networks: UMTS/GSM (3GPP, ETSI), CDMA

(3GPP2), WiFi/WiMAX/ZigBee (IEEE)– App and middleware: TIA TR-50.1 Smart Device

Communications (SDC), ESTI TC M2M

Page 12: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

M2M Standard Areas

• ETSI formed a TC to focus on describing the scenarios of applications:– Smart Grid/Smart Meters– eHealth– Automotive Applications– City Automations– Connected Consumers

• 3GPP work is under the name of Machine Type Communications (MTC)

• 3GPP2 (and CDG) has just started looking into the potential impacts

* 출처 : TIA TR-50.1

Page 13: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

ETSI M2M Standards

• M2M Service Requirements (Draft: ETSI TS 102 689 V0.5.1, Jan. 2010)– General requirements on M2M communications ranging from Device

initiation, authentication, to noninterference of electro-medical devices.

– Managements: fault handling, configuration, accounting– Functional requirements: data collection and reporting, remote

control, QoS support, etc.– Security: authentication, authorization, data integrity, trust

management– Naming/numbering/addressing: IP, URL, SIP

• M2M Functional Architecture (Draft ETSI TS 102 690 V0.1.2, Jan. 2010)

Page 14: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

ETSI M2M Standards

• M2M apps under development including:– Smart Meters Draft ETSI TR 102 691 V0.3.2, Jan. 2010

– eHealth Draft ETSI TR 102 732 V0.2.1, Sep. 2009

– Connected Consumers Draft ETSI TR 102 857 V0.0.1, Dec. 2009

– City Automation Draft ETSI TR 102 897 V0.0.2, Jan. 2010

– Automotive Apps Draft ETSI TR 102 898 V0.1.0, Jan. 2010

– Car Charging, Fleet Management, Anti-Theft

Page 15: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

3GPP’s M2M Standards

• “System Improvement for Machine Type Communications (MTC)” (3GPP TR 23.888 V0.21, Jan. 2010, Release 10)

• Heavy discussions in SA1 and the doc listed 11 issues:– Group based optimization,– TC Devices communicating with one or multiple servers,– Device communicated with each other,– Online, off-line small data transmissions,– Low mobility,– MTC subscriptions,– Device trigger, time control,– MTC monitoring and decoupling MTC server from 3GPP

architecture.

Page 16: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Access networks

Application

Service Platform

IP Network

Wide Area Network

M2M Gateway

wireless

wireline

IPSOIPV6

Hardware and Protocols

ZigBee Alliance.ZB Application Profiles 3GPP

SA1, SA3, ,…

IETF 6LowPANPhy-Mac Over IPV6

OMA GSMASCAG,…

IETF ROLLRouting over Low Power

Lossy Networks

IUT-TNGN CENELEC

Smart MeteringCEN

Smart Metering

ISO/IEC JTC1UWSN

IEEE802.xx.x

ESMIGMetering

WOSA

KNX

ZCL

HGIHome Gateway

Initiative

EPCGlobalGS1

UtilitiesMetering

OASIS

W3C

W-Mbus

Relationship with Other Standards

* 출처 : ESTI M2M 소개

Page 17: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

References

• KT M2M 사업추진 방향 http://plum.hufs.ac.kr/hsn2010/pdf/Session6-3.pdf

• SKT 사물통신 서비스 소개 http://blog.daum.net/nia-m2m/74

• M2M Activities in ETSI http://docbox.etsi.org/M2M/Open/Information/M2M_presentation.ppt

• Connected World Conference http://www.tiaonline.org/news_events/documents/CWPresentation_TR50_Chair_Numerex_CTO_Jeff_Smith.pdf

• Update of M2M Standard Work

http://ftp.tiaonline.org/TR-50/TR-50_MAIN/Public/20100310_Denver_CO/TR50-20100310-005_Update%20of%20M2M%20Standard%20work%20v3%28Mitch%20Tseng%29.pdf

• Overview of M2M http://sites.google.com/site/hridayankit/M2M_overview_paper.pdf

Page 18: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Semantic Web: Promising Technologies, Current Applications

& Future DirectionsInvited and Colloquia talks at: Swinburne Institute of Technology –Melbourne (July 18),

University of Adelaide-Adelaide (July 23), University of Melbourne- Melbourne (July 31), Victoria University- Melbourne

Australia, 2008

Amit P. [email protected]

Kno.e.sis Center, Comp. Sc & EnggWright State University, Dayton OH, USA

Thanks Kno.e.sis team and collaborators

Page 19: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Evolution of the Web

Web of pages - text, manually created links - extensive navigation

2007

1997

Web of databases - dynamically generated pages - web query interfaces

Web of resources - data = service = data, mashups - ubiquitous computing

Web of people - social networks, user-created casual content - Twine, GeneRIF, Connotea

Web as an oracle / assistant / partner - “ask the Web”: using semantics to leverage text + data + services - Powerset

Sem

antic

Tec

hnol

ogy

Use

d

Page 20: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Semantic Web: Key Components

• Ontology: Agreement with a common vocabulary/nomenclature, conceptual models and domain Knowledge

• Schema + Knowledge base • Agreement is what enables interoperability• Formal description - Machine processability is

what leads to automation

Page 21: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Semantic Web: Key Components

• Semantic Annotation (Metadata Extraction): Associating meaning with data, or labeling data so it is more meaningful to the system and people.

• Can be manual, semi-automatic (automatic with human verification), automatic.

Page 22: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Semantic Web: Key Components

• Reasoning/Computation: semantics enabled search, integration, answering complex queries, connections and analyses (paths, sub graphs), pattern finding, mining, hypothesis validation, discovery, visualization

Page 23: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

SW Stack: Architecture, Standards

Page 24: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

From Syntax to Semantics

Shallow semantics

Deep semantics

Expressiveness,

Reasoning

Page 25: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

a little bit about ontologies

Page 26: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Open Biomedical Ontologies

Open Biomedical Ontologies, http://obo.sourceforge.net/

Many Ontologies Available Today

Page 27: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Drug Ontology Hierarchy (showing is-a relationships)

owl:thing

prescription_drug

_ brand_na

me

brandname_unde

clared

brandname_comp

osite

prescription_drug

monograph_ix_cla

ss

cpnum_ group

prescription_drug

_ property

indication_

property

formulary_

property

non_drug_

reactant

interaction_proper

ty

property

formulary

brandname_indivi

dual

interaction_with_prescriptio

n_drug

interaction

indication

generic_ individua

l

prescription_drug_ generic

generic_ composit

e

interaction_ with_non_ drug_react

ant

interaction_with_monograph_ix_class

Page 28: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

A little bit about semantic metadata extractions and annotations

Page 29: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

WWW, EnterpriseRepositories

METADATA

EXTRACTORS

Digital Maps

NexisUPIAP

Feeds/Documents

Digital Audios

Data Stores

Digital Videos

Digital Images. . .

. . . . . .

Create/extract as much (semantics)metadata automatically as possible;

Use ontlogies to improve and enhanceextraction

Extraction for Metadata Creation

Page 30: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Web 2.0

Man Meets Machine

Page 31: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Putting the man back in Semantics

“Web 2.0 is made of people” (Ross Mayfield)

“Web 2.0 is about systems that harness collective

intelligence.”(Tim O’Reilly)

Semantic Web focuses on artificial agents

The relationship web combines the skills of humans and machines

Page 32: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Semantic WebConnects Knowledge

The MetawebConnects Intelligence

The WebConnects Information

Social SoftwareConnects People

Artificial Intelligence

Personal Assistants

Ontologies

Taxonomies

KnowledgeBases

KnowledgeManagement

SemanticWebs

Intelligent Agents

EnterpriseMinds

GroupMinds

Lifelogs

SemanticWeblogs

The“Relationship”

WebDecentralisedCommunities

SmartMarketplaces

The GlobalBrain

Search Engines

Content Portals

Databases

File Servers

“Push”

PIMs

Web Sites

EnterprisePortals

Pub-Sub

MarketplacesAuctions

Groupware

Weblogs

Wikis

RSSCommunity

Portals

eMail

P2P File-sharing Conferencing

IM

USENET

SocialNetworks

Deg

ree

of In

form

ation

Con

necti

vity

Formal

Social,InformalImplicit

Powerful

Degree of Social Connectivity

Web 3.0

Web 1.0

Web 4.0

Web 2.0

Page 33: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Semantic Sensor Web

Amit ShethLexisNexis Ohio Eminent Scholar

Kno.e.sis Center, Wright State University

Page 34: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Events – Spatial, Temporal and Thematic

Spatial

Temporal

Thematic

Page 35: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Events and STT Dimensions

Powerful mechanism to integrate content– Describes Real-World occurrences– Can have video, images, text, audio (same

event)– Search and Index based on events and STT

relations

Many relationship types– Spatial:

• What events happened near this event? • What entities/organizations are located

nearby?– Temporal:

• What events happened before/after/during this event?

– Thematic:• What is happening?• Who is involved?

Going furtherCan we use:

Who? Where? What?

Why? When?

How?

Use integrated STT analysisto explore

cause and effect

Page 36: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

36

High-level Sensor

Low-level Sensor

How do we determine if the three images depict …

• the same time and same place?• the same entity?• a serious threat?

Scenario: Sensor Data Fusion and Analysis

Page 37: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Raw Sensor (Phenomenological) Data

Feature Metadata

Entity Metadata

Ontology Metadata

Expr

essi

vene

ss

Data (World)

Information (Perception)

Knowledge (Comprehension)

Data Pyramid

“An object by itself is intensely uninteresting”. – Grady Booch, Object Oriented Design with Applications, 1991

Keywords

|

Search(data)

Entities

|

Integration(information)

Relationships,Events

|

Analysis,Insight(knowledge)

Page 38: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

38

What is Sensor Web Enablement (SWE)?

http://www.opengeospatial.org/projects/groups/sensorweb

Page 39: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

GeographyML (GML)

TransducerML (TML)

Observations &

Measurements (O&M)

Information Model for

Observations and Sensing

Sensor and Processing Description Language

Real Time Streaming Protocol

Common Model for Geographical

Information

SensorML (SML)

Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.

SWE Components - Languages

Page 40: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

CatalogService

SOS SPS

Clients

Sensor Observation Service: Access Sensor

Description and Data

Sensor Planning Service: Command and Task

Sensor SystemsDiscover Services Sensors

Providers Data

Accessible from various types of clients from PDAs

and Cell Phones to high end Workstations

Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.

SWE Components – Web Services

SAS

Sensor Alert Service

Dispatch Sensor

Alerts to registered

Users

Page 41: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

41

Semantic Sensor Web

Page 42: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

42

Data

• Raw Phenomenological Data

Data-to-Knowledge Architecture

Information

• Entity Metadata

• Feature Metadata

Knowledge

• Object-Event Relations

• Spatiotemporal Associations

• Provenance/Context

Feature Extraction and Entity Detection

Data Storage(Raw Data, XML, RDF)

Semantic Analysis and Query

Sensor Data Collection

Ontologies

• Space Ontology

• Time Ontology

• Domain Ontology

SemanticAnnotation

Page 43: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

43

Ontology & Rules

• Weather

• Time

• Space

OracleSensorDB

Get Observation

Describe Sensor

Semantic Sensor Observation Service

Collect Sensor Data

BuckeyeTraffic.org

Get Capabilities

Semantic Annotation Service

S-SOS Client

SWE Annotated SWE

HTTP-GET Request

O&M-S or SML-S Response

Semantic Sensor Observation Service

Page 44: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

SSW Standards Organizations

OGC Sensor Web Enablement

• SensorML• O&M• TransducerML• GeographyML

Web Services

• Web Services Description Language

• REST

National Institute for Standards and Technology

• Semantic Interoperability Community of Practice

• Sensor Standards Harmonization

W3C Semantic Web• Resource Description

Framework• RDF Schema• Web Ontology Language• Semantic Web Rule

Language

• SAWSDL

• SA-REST

• SML-S

• O&M-S

• TML-S

Sensor Ontology

Sensor Ontology

Page 45: M2M and Semantic Sensor Web KAIST KSE Uichin Lee

Summary• Wireless sensor network ubiquitous sensor network:

M2M and Internet of Things– Including participatory sensing & ubiquitous human

computation

• Semantic web, and semantic sensor web