1. M2M and Semantic Sensor Web
KAIST KSE
Uichin Lee

2. Ubiquitous Sensor Network (USN)
Figures from

3. USN Services
Figures from

4. Internet of Things: Ubiquitous Networking
Figures from

5. 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

6. 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

7. M2M Architecture (ETSI)
M2M Application
M2M Area Network
M2M Core
Service
Capabilities
M2M Gateway
Client
Application
Application
Domain
Network Domain
M2M Device Domain
* 출처: ETSI M2M 소개

8. M2M Device Domain M2M Device M2M Area Network M2M Gateways
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 , 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.

9. 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.

10. 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

11. 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

12. 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

13. ETSI M2M Standards
M2M Service Requirements (Draft: ETSI TS 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 V0.1.2, Jan. 2010)

14. ETSI M2M Standards M2M apps under development including:
Smart Meters Draft ETSI TR V0.3.2, Jan. 2010
eHealth Draft ETSI TR V0.2.1, Sep. 2009
Connected Consumers Draft ETSI TR V0.0.1, Dec. 2009
City Automation Draft ETSI TR V0.0.2, Jan. 2010
Automotive Apps Draft ETSI TR V0.1.0, Jan. 2010
Car Charging, Fleet Management, Anti-Theft

15. 3GPP’s M2M Standards
“System Improvement for Machine Type Communications (MTC)” (3GPP TR 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.

16. Relationship with Other Standards
EPCGlobal
GS1
ISO/IEC JTC1
UWSN
ESMIG
Metering
IUT-T
NGN
Utilities
Metering
HGI
Home Gateway
Initiative
Access networks
Application
Service Platform
IP Network
Wide Area Network
M2M Gateway
wireless
wireline
CEN
Smart Metering
CENELEC
Smart Metering
OASIS
WOSA
W3C
IETF 6LowPAN
Phy-Mac Over IPV6
KNX
IPSO
IPV6
Hardware and
Protocols
W-Mbus
IETF ROLL
Routing over Low Power
Lossy Networks
IEEE
802.xx.x
ZCL
ZigBee Alliance.
ZB Application Profiles
3GPP
SA1, SA3, ,…
OMA
GSMA
SCAG,…
* 출처: ESTI M2M 소개

17. References
KT M2M 사업추진 방향
SKT 사물통신 서비스 소개
M2M Activities in ETSI
Connected World Conference
Update of M2M Standard Work
Overview of M2M

18. 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
Kno.e.sis Center, Comp. Sc & Engg
Wright State University, Dayton OH, USA
Thanks Kno.e.sis team and collaborators

19. Semantic Technology Used
Evolution of the Web
Semantic Technology Used
Web as an oracle / assistant / partner
- “ask the Web”: using semantics to leverage text + data + services
- Powerset
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 of pages
- text, manually created links
- extensive navigation
2007
We see a change of paradigm on the Web. Researchers once had to extensively navigate through pages to obtain the answer to a question.
We are getting closer to the time where one can pose a question to the Web and have the solution computed by integrated sources.
Some key areas of work include:
How to integrate pages, databases, services and human contributions on the Web
How to detect and propagate changes, control authorship and trust
How to ask questions and visualize the results
How to automatically perform knowlege discovery over this global knowledge base
1997

20. 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

21. 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.

22. 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

23. SW Stack: Architecture, Standards
RDF: Resource Description Framework.
RDFS: RDF schema
Web Ontology Language (OWL).

24. From Syntax to Semantics
Shallow semantics
Deep semantics
Expressiveness,
Reasoning

25. a little bit about ontologies

26. Open Biomedical Ontologies
Many Ontologies Available Today
Open Biomedical Ontologies
Open Biomedical Ontologies,

27. Drug Ontology Hierarchy (showing is-a relationships)
formulary_ property
non_drug_ reactant
interaction_property
formulary
indication
property
indication_ property
owl:thing
monograph_ix_class
prescription_drug_ brand_name
interaction_ with_non_ drug_reactant
brandname_individual
prescription_drug
prescription_drug_ property
interaction
brandname_composite
brandname_undeclared
prescription_drug_ generic
interaction_with_monograph_ix_class
interaction_with_prescription_drug
cpnum_ group
generic_ composite
generic_ individual

28. A little bit about semantic metadata extractions and annotations

29. Extraction for Metadata Creation
Digital Videos
Nexis
UPI AP
Feeds/
Documents
Data Stores
. . .
WWW, Enterprise
Repositories
Digital Maps
Digital Audios
Digital Images
Create/extract as much (semantics) metadata automatically as possible;
Use ontlogies to improve and enhance
extraction
EXTRACTORS
METADATA

30. Web 2.0
Man Meets Machine

31. 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

32. Connects Intelligence
Semantic Web
Connects Knowledge
The Metaweb
Connects Intelligence
The Web
Connects Information
Social Software
Connects People
Artificial Intelligence
Personal
Assistants
Ontologies
Taxonomies
Knowledge
Bases
Management
Semantic
Webs
Intelligent Agents
Enterprise
Minds
Group
Lifelogs
Weblogs
The
“Relationship”
Web
Decentralised
Communities
Smart
Marketplaces
The Global
Brain
Search Engines
Content Portals
Databases
File Servers
“Push”
PIMs
Web Sites
Portals
Pub-Sub
Auctions
Groupware
Wikis
RSS
Community
P2P File-sharing
Conferencing IM
USENET
Social
Networks
Degree of Information Connectivity
Formal
Powerful
Web 3.0
Web 1.0
Web 4.0
Web 2.0
Social,
Informal
Implicit
Degree of Social Connectivity

33. Semantic Sensor Web Amit Sheth LexisNexis Ohio Eminent Scholar
Kno.e.sis Center, Wright State University

34. Spatial Temporal Thematic
Events – Spatial, Temporal and Thematic
Spatial
Temporal
Thematic
A first slide to make sure everyone is on the same page w.r.t. what we mean by spatial-temporal-thematic
Snippet from story about Halo 3 video game launch
Spatial – NY, Fifth Avenue and 44th Street
Temporal – Publication Date, Monday Night  time of event
Thematic – Halo 3 launch, Microsoft, George Clooney

35. Events and STT Dimensions
Going further
Can we use:
Who? Where? What? Why?
When? How?
Use integrated STT analysis
to explore
cause and effect
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?
More details about Events and STT dimensions
- Event/STT paradigm useful for integration
- Examples of relationships in each dimension
- Going further – can we use STT dimensions together to help figure out Why? And How?

36. 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 36

37. Raw Sensor (Phenomenological) Data
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)
Raw Sensor (Phenomenological) Data
Feature Metadata
Entity Metadata
Ontology Metadata
Expressiveness
Data (World)
Information (Perception)
Knowledge (Comprehension)

38. What is Sensor Web Enablement (SWE)? 38 38

39. SWE Components - Languages
Information Model for Observations and Sensing
Sensor and Processing Description Language
Observations & Measurements (O&M)
SensorML (SML)
TransducerML (TML)
GeographyML (GML)
Real Time Streaming Protocol
Common Model for Geographical Information
Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.

40. SWE Components – Web Services
Sensor Observation Service: Access Sensor Description and Data
Sensor Planning Service:
Command and Task Sensor Systems
Discover Services Sensors Providers Data
SOS
SPS
Sensor Alert Service Dispatch Sensor Alerts to registered Users
SAS
Catalog Service
Clients
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.

41. Semantic Sensor Web 41 41

42. Data-to-Knowledge Architecture
Object-Event Relations
Spatiotemporal Associations
Provenance/Context
Data Storage
(Raw Data, XML, RDF)
Semantic Analysis and Query
Information
Entity Metadata
Feature Metadata
Feature Extraction and Entity Detection
Semantic
Annotation
Data
Raw Phenomenological Data
Sensor Data Collection
Ontologies
Space Ontology
Time Ontology
Domain Ontology 42 42

43. 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
Oracle
SensorDB
Get Observation
Describe Sensor
Get Capabilities
Ontology & Rules
Weather
Time
Space
SWE
Annotated SWE
Semantic Annotation Service

44. National Institute for Standards and Technology
SSW Standards Organizations
W3C Semantic Web
SAWSDL
SA-REST
SML-S
O&M-S
TML-S
Resource Description Framework
RDF Schema
Web Ontology Language
Semantic Web Rule Language
Web Services
Web Services Description Language
REST
OGC Sensor Web Enablement
Sensor Ontology
SensorML
O&M
TransducerML
GeographyML
SAWSDL “Semantic Annotations for WSDL and XML Schema”:
National Institute for Standards and Technology
Sensor Ontology
Semantic Interoperability Community of Practice
Sensor Standards Harmonization

45. Summary
Wireless sensor network  ubiquitous sensor network: M2M and Internet of Things
Including participatory sensing & ubiquitous human computation
Semantic web, and semantic sensor web