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M2M and Semantic Sensor Web

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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 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

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