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M2M and Semantic Sensor Web
KAIST KSEUichin Lee
Ubiquitous Sensor Network (USN)
Figures from http://www.rfidglobal.eu/userfiles/documents/white%20papers%204.pdf
USN Services
Figures from http://www.rfidglobal.eu/userfiles/documents/white%20papers%204.pdf
Internet of Things: Ubiquitous Networking
Figures from http://www.rfidglobal.eu/userfiles/documents/white%20papers%204.pdf
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
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
M2MGateway
ClientApplication
M2M ApplicationM2M Area Network
M2M Architecture (ETSI)
7
ApplicationDomain
Network Domain
M2M Device Domain
Service Capabilities
M2MCore
* 출처 : ETSI M2M 소개
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.
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.
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
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
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
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)
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
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.
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
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
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
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
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
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.
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
SW Stack: Architecture, Standards
From Syntax to Semantics
Shallow semantics
Deep semantics
Expressiveness,
Reasoning
a little bit about ontologies
Open Biomedical Ontologies
Open Biomedical Ontologies, http://obo.sourceforge.net/
Many Ontologies Available Today
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
A little bit about semantic metadata extractions and annotations
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
Web 2.0
Man Meets Machine
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
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
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
Semantic Sensor Web
Amit ShethLexisNexis Ohio Eminent Scholar
Kno.e.sis Center, Wright State University
Events – Spatial, Temporal and Thematic
Spatial
Temporal
Thematic
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
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
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)
38
What is Sensor Web Enablement (SWE)?
http://www.opengeospatial.org/projects/groups/sensorweb
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
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
41
Semantic Sensor Web
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
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
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
Summary• Wireless sensor network ubiquitous sensor network:
M2M and Internet of Things– Including participatory sensing & ubiquitous human
computation
• Semantic web, and semantic sensor web