4 Internet of Things: Ubiquitous Networking Figures from
5 M2M DefinitionM2M은 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 Definition사람, 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 네트워크광대역/모빌리티/글로벌화: 수천억개의 사물 간 정보교환을 위해 광대역/이동성이 보장, 인터넷 기반으로 세계 어느 곳에서도 사물정보의 상호 교환이 가능보안/품질 보장화: 공공/민간의 중요한 사물 정보 및 서비스에 대한 차별화 된 보안 및 고품질 보장이 가능고기능화: IPv6 기반으로 u-City등 대규모 사물정보 서비스 제공에 적합주로 단방향적인 지식/지능 정보 전달 서비스에 중점을 둠
7 M2M Architecture (ETSI) M2M ApplicationM2M Area NetworkM2M CoreServiceCapabilitiesM2M GatewayClientApplicationApplicationDomainNetwork DomainM2M 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 NetworkA 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 GatewaysEquipments 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 applicationsExpose functionalities through a set of open interfacesUse Core Network functionalities and simplify and optimize applications development and deployment whilst hiding network specificities to applicationsExamples 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 operatorsLong-tail businessLow ARPU (<$10) compared to voice (<$30)Lagging standards
11 M2M Standard Trends So far heterogeneous M2M devices/platforms SKT/KT/LG M2M platformsOrange M2M ConnectNokia M2M GatewaySprint Business Mobility FrameworkM2M standard activities for interoperabilityAccess 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 AreasETSI formed a TC to focus on describing the scenarios of applications:Smart Grid/Smart MeterseHealthAutomotive ApplicationsCity AutomationsConnected Consumers3GPP 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 StandardsM2M 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, accountingFunctional requirements: data collection and reporting, remote control, QoS support, etc.Security: authentication, authorization, data integrity, trust managementNaming/numbering/addressing: IP, URL, SIPM2M 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. 2010eHealth Draft ETSI TR V0.2.1, Sep. 2009Connected Consumers Draft ETSI TR V0.0.1, Dec. 2009City Automation Draft ETSI TR V0.0.2, Jan. 2010Automotive Apps Draft ETSI TR V0.1.0, Jan. 2010Car 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 EPCGlobalGS1ISO/IEC JTC1UWSNESMIGMeteringIUT-TNGNUtilitiesMeteringHGIHome GatewayInitiativeAccess networksApplicationService PlatformIP NetworkWide Area NetworkM2M GatewaywirelesswirelineCENSmart MeteringCENELECSmart MeteringOASISWOSAW3CIETF 6LowPANPhy-Mac Over IPV6KNXIPSOIPV6Hardware andProtocolsW-MbusIETF ROLLRouting over Low PowerLossy NetworksIEEE802.xx.xZCLZigBee Alliance.ZB Application Profiles3GPPSA1, SA3, ,…OMAGSMASCAG,…* 출처: ESTI M2M 소개
17 ReferencesKT M2M 사업추진 방향SKT 사물통신 서비스 소개M2M Activities in ETSIConnected World ConferenceUpdate of M2M Standard WorkOverview 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- MelbourneAustralia, 2008Amit P. ShethKno.e.sis Center, Comp. Sc & EnggWright State University, Dayton OH, USAThanks Kno.e.sis team and collaborators
19 Semantic Technology Used Evolution of the WebSemantic Technology UsedWeb as an oracle / assistant / partner- “ask the Web”: using semantics to leverage text + data + services- PowersetWeb of databases- dynamically generated pages- web query interfacesWeb of resources- data = service = data, mashups- ubiquitous computingWeb of people- social networks, user-created casualcontent- Twine, GeneRIF, ConnoteaWeb of pages- text, manually created links- extensive navigation2007We 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 WebHow to detect and propagate changes, control authorship and trustHow to ask questions and visualize the resultsHow to automatically perform knowlege discovery over this global knowledge base1997
20 Semantic Web: Key Components Ontology: Agreement with a common vocabulary/nomenclature, conceptual models and domain KnowledgeSchema + Knowledge baseAgreement is what enables interoperabilityFormal 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.
28 A little bit about semantic metadata extractions and annotations
29 Extraction for Metadata Creation Digital VideosNexisUPIAPFeeds/DocumentsData Stores. . .WWW, EnterpriseRepositoriesDigital MapsDigital AudiosDigital ImagesCreate/extract as much (semantics) metadata automatically as possible;Use ontlogies to improve and enhanceextractionEXTRACTORSMETADATA
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 WebConnects KnowledgeThe MetawebConnects IntelligenceThe WebConnects InformationSocial SoftwareConnects PeopleArtificial IntelligencePersonalAssistantsOntologiesTaxonomiesKnowledgeBasesManagementSemanticWebsIntelligent AgentsEnterpriseMindsGroupLifelogsWeblogsThe“Relationship”WebDecentralisedCommunitiesSmartMarketplacesThe GlobalBrainSearch EnginesContent PortalsDatabasesFile Servers“Push”PIMsWeb SitesPortalsPub-SubAuctionsGroupwareWikisRSSCommunityP2P File-sharingConferencingIMUSENETSocialNetworksDegree of Information ConnectivityFormalPowerfulWeb 3.0Web 1.0Web 4.0Web 2.0Social,InformalImplicitDegree 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 ThematicSpatialTemporalThematicA first slide to make sure everyone is on the same page w.r.t. what we mean by spatial-temporal-thematicSnippet from story about Halo 3 video game launchSpatial – NY, Fifth Avenue and 44th StreetTemporal – Publication Date, Monday Night time of eventThematic – Halo 3 launch, Microsoft, George Clooney
35 Events and STT Dimensions Going furtherCan we use:Who? Where? What? Why?When? How?Use integrated STT analysisto explorecause and effectPowerful mechanism to integrate contentDescribes Real-World occurrencesCan have video, images, text, audio (same event)Search and Index based on events and STT relationsMany relationship typesSpatial: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 SensorLow-level SensorHow do we determine if the three images depict …the same time and same place?the same entity?a serious threat?3636
37 Raw Sensor (Phenomenological) Data Data Pyramid“An object by itself is intensely uninteresting”.Grady Booch, Object Oriented Design with Applications, 1991Keywords|Search (data)EntitiesIntegration (information)Relationships,EventsAnalysis,Insight (knowledge)Raw Sensor (Phenomenological) DataFeature MetadataEntity MetadataOntology MetadataExpressivenessData (World)Information (Perception)Knowledge (Comprehension)
39 SWE Components - Languages Information Model for Observations and SensingSensor and Processing Description LanguageObservations & Measurements (O&M)SensorML (SML)TransducerML (TML)GeographyML (GML)Real Time Streaming ProtocolCommon Model for Geographical InformationSam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
40 SWE Components – Web Services Sensor Observation Service: Access Sensor Description and DataSensor Planning Service:Command and Task Sensor SystemsDiscover Services Sensors Providers DataSOSSPSSensor Alert Service Dispatch Sensor Alerts to registered UsersSASCatalog ServiceClientsAccessible from various types of clients from PDAs and Cell Phones to high end WorkstationsSam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.
42 Data-to-Knowledge Architecture Object-Event RelationsSpatiotemporal AssociationsProvenance/ContextData Storage(Raw Data, XML, RDF)Semantic Analysis and QueryInformationEntity MetadataFeature MetadataFeature Extraction and Entity DetectionSemanticAnnotationDataRaw Phenomenological DataSensor Data CollectionOntologiesSpace OntologyTime OntologyDomain Ontology4242
43 Semantic Sensor Observation Service S-SOS ClientBuckeyeTraffic.orgCollect Sensor DataHTTP-GET RequestO&M-S or SML-S ResponseSemantic Sensor Observation ServiceOracleSensorDBGet ObservationDescribe SensorGet CapabilitiesOntology & RulesWeatherTimeSpaceSWEAnnotated SWESemantic Annotation Service
44 National Institute for Standards and Technology SSW Standards OrganizationsW3C Semantic WebSAWSDLSA-RESTSML-SO&M-STML-SResource Description FrameworkRDF SchemaWeb Ontology LanguageSemantic Web Rule LanguageWeb ServicesWeb Services Description LanguageRESTOGC Sensor Web EnablementSensor OntologySensorMLO&MTransducerMLGeographyMLSAWSDL “Semantic Annotations for WSDL and XML Schema”:National Institute for Standards and TechnologySensor OntologySemantic Interoperability Community of PracticeSensor Standards Harmonization
45 SummaryWireless sensor network ubiquitous sensor network: M2M and Internet of ThingsIncluding participatory sensing & ubiquitous human computationSemantic web, and semantic sensor web