M2M and Semantic Sensor Web
KAIST KSE
Uichin 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
• M2M은 기계간의 통신 (machine-to-machine) 및 사람이
동작하는 디바이스와 기계간의 통신(man-to-machine)을
의미하며, 광의적으로는 통신 과 IT기술을 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, WiBro등)의 통합, 이종(ICT+비ICT)
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-City등 대규모 사물정보 서비스 제공에 적합
• 주로 단방향적인 지식/지능 정보 전달 서비스에 중점을 둠
M2M Architecture (ETSI)
M2M Application
M2M Area Network
Service
Capabilities
M2M
Core
M2M
Gateway
Client
Application
* 출처: ETSI M2M 소개
7
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.
Relationship with Other Standards
EPCGlobal
ISO/IEC JTC1
Metering
IUT-T
CEN
CENELEC
Smart Metering
Smart Metering
NGN
OASIS
GS1
ESMIG
UWSN
Access networks
Service Platform
WOSA
wireless
Wide Area
Network
IPV6
Hardware and
Protocols
KNX
M2M Gateway
IETF 6LowPAN
Phy-Mac Over IPV6
IP Network
W-Mbus
IETF ROLL
Routing over Low Power
Lossy Networks
wireline
IEEE
ZCL
802.xx.x
Application
ZigBee Alliance.
ZB Application Profiles
* 출처: ESTI M2M 소개
Metering
Home Gateway
Initiative
W3C
IPSO
Utilities
HGI
OMA
GSMA
3GPP
SCAG,…
SA1, SA3, ,…
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_C
hair_Numerex_CTO_Jeff_Smith.pdf
• Update of M2M Standard Work http://ftp.tiaonline.org/TR-50/TR50_MAIN/Public/20100310_Denver_CO/TR50-20100310005_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 Directions
Invited 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. Sheth
amit.sheth@wright.edu
Kno.e.sis Center, Comp. Sc & Engg
Wright State University, Dayton OH, USA
Thanks Kno.e.sis team and collaborators
Evolution
of
the
Web
Web as an oracle / assistant / partner
2007
- “ask the Web”: using semantics to
leverage text + data + services
- Powerset
Web of people
- social networks, user-created casual
content
- Twine, GeneRIF, Connotea
Web of resources
- data = service = data, mashups
- ubiquitous computing
1997
Web of databases
- dynamically generated pages
- web query interfaces
Web of pages
- text, manually created links
- extensive navigation
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
Deep semantics
Shallow semantics
a little bit about ontologies
Many Ontologies Available Today
Open Biomedical Ontologies
Open Biomedical Ontologies, http://obo.sourceforge.net/
Drug Ontology Hierarchy
(showing is-a relationships)
non_drug_
reactant
interaction_
property
formulary_
property
formulary
indication
monograph
_ix_class
prescription
_drug_
property
cpnum_
group
property
indication_
property
brandname_
individual
brandname_
undeclared
prescription
_drug_
brand_name
brandname_
composite
generic_
composite
prescription
_drug
prescription
_drug_
generic
generic_
individual
owl:thing
interaction
interaction_
with_prescri
ption_drug
interaction_
with_non_
drug_reactant
interaction_
with_mono
graph_ix_cl
ass
A little bit about semantic metadata
extractions and annotations
Extraction for Metadata Creation
WWW, Enterprise
Repositories
Nexis
UPI
AP
Feeds/
Documents
Digital Videos
...
...
Data Stores
Digital Maps
...
Digital Images
Create/extract as much (semantics)
metadata automatically as possible;
Use ontlogies to improve and enhance
extraction
Digital Audios
EXTRACTORS
METADATA
Web 2.0
Man Meets Machine
Putting the man back in Semantics
Semantic Web focuses on artificial agents
“Web 2.0 is made of people” (Ross Mayfield)
“Web 2.0 is about systems
that harness collective
intelligence.”
(Tim O’Reilly)
The relationship web combines the skills of humans and machines
Artificial Intelligence
Intelligent Agents
Personal
Assistants
Semantic
Webs
Semantic Web
Connects Knowledge
Taxonomies
Enterprise
Minds
Knowledge
Management
Knowledge
Bases
The Metaweb
Connects Intelligence
Group
Minds
Formal
Ontologies
The Global
Brain
Powerful
Lifelogs
Enterprise
Portals
Marketplaces
Auctions
Content Portals
Wikis
Weblogs
Web Sites
Implicit
The Web
Connects Information
Groupware
PIMs
P2P File-sharing
RSS
Social,
Social Software
Informal
Connects
People
eMail
“Push”
Pub-Sub
File Servers
The
“Relationship”
Web
Decentralised
Communities
Semantic
Weblogs
Search Engines
Databases
Smart
Marketplaces
USENET
Conferencing
IM
Community
Portals
Social
Networks
Semantic Sensor Web
Amit Sheth
LexisNexis 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 further
Can we use:
Who? Where?
What?
Why?
When?
How?
Use integrated
STT analysis
to explore
cause and effect
Scenario: Sensor Data Fusion and Analysis
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?
36
Data Pyramid
“An object by itself is intensely uninteresting”.
– Grady Booch, Object Oriented Design with Applications, 1991
Keywords
|
|
Search
(data)
Relationships,
Events
Entities
|
Integration
(information)
Ontology
Metadata
Analysis,
Insight
(knowledge)
Knowledge (Comprehension)
Entity Metadata
Information (Perception)
Feature Metadata
Raw Sensor (Phenomenological) Data
Data (World)
What is Sensor Web Enablement (SWE)?
http://www.opengeospatial.org/projects/groups/sensorweb
38
SWE Components - Languages
Information
Model for
Observations
and Sensing
Sensor and
Processing
Description
Language
Observations &
Measurements
(O&M)
GeographyML
(GML)
Common Model for
Geographical
Information
SensorML
(SML)
TransducerML
(TML)
Sam Bacharach, “GML by OGC to AIXM 5 UGM,”
OGC, Feb. 27, 2007.
Real Time
Streaming
Protocol
SWE Components – Web Services
Discover
Services
Sensors
Providers
Data
Sensor Planning Service:
Command and Task Sensor
Systems
Sensor
Observation
Service: Access
Sensor
Description and
Data
SOS
SPS
SAS
Sensor Alert
Service
Dispatch
Sensor Alerts
to registered
Users
Catalog
Service
Sam Bacharach,
“GML by OGC to AIXM 5 UGM,”
OGC, Feb. 27, 2007.
Clients
Accessible from various types
of clients from PDAs and Cell
Phones to high end
Workstations
Semantic Sensor Web
41
Data-to-Knowledge Architecture
Knowledge
• Object-Event Relations
• Spatiotemporal Associations
Semantic Analysis and Query
• Provenance/Context
Data Storage
(Raw Data, XML, RDF)
Information
• Entity Metadata
Feature Extraction and Entity Detection
• Feature Metadata
Semantic
Annotation
Data
• Raw Phenomenological Data
Sensor Data Collection
Ontologies
• Space Ontology
• Time Ontology
• Domain Ontology
42
Semantic Sensor Observation Service
S-SOS Client
BuckeyeTraffic.org
Collect Sensor Data
HTTP-GET
Request
O&M-S or SML-S
Response
Semantic Sensor Observation Service
Get Observation
Oracle
SensorDB
Describe Sensor
Get Capabilities
Ontology & Rules
SWE
Annotated SWE
• Weather
• Time
Semantic Annotation Service
• Space
43
SSW Standards Organizations
W3C Semantic Web
• SML-S
• O&M-S
• TML-S
OGC Sensor Web
Enablement
•
•
•
•
SensorML
O&M
TransducerML
GeographyML
Sensor
Ontology
• Resource Description
Framework
• RDF Schema
• Web Ontology Language
• Semantic Web Rule
Language
Sensor
Ontology
National Institute for Standards
and Technology
• Semantic Interoperability
Community of Practice
• Sensor Standards
Harmonization
• SAWSDL
• SA-REST
Web Services
• Web Services
Description
Language
• REST
Summary
• Wireless sensor network  ubiquitous sensor
network: M2M and Internet of Things
– Including participatory sensing & ubiquitous human
computation
• Semantic web, and semantic sensor web