M2M Gateway Client Application M2M Application M2M Area Network M2M Architecture (ETSI) 7 Service Capabilities M2M Core * : ETSI M2M
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 , 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 * : TIA TR-50.1
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)
ETSI M2M Standards M2M apps under development including: – Smart Meters Draft ETSI TR V0.3.2, Jan – eHealth Draft ETSI TR V0.2.1, Sep – Connected Consumers Draft ETSI TR V0.0.1, Dec – City Automation Draft ETSI TR V0.0.2, Jan – Automotive Apps Draft ETSI TR V0.1.0, Jan – Car Charging, Fleet Management, Anti-Theft
3GPPs 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.
Access networks Application Service Platform IP Network Wide Area Network M2M Gateway wireless wireline IPSO IPV6 Hardware and Protocols ZigBee Alliance. ZB Application Profiles 3GPP SA1, SA3,,… IETF 6LowPAN Phy-Mac Over IPV6 OMA GSMA SCAG,… IETF ROLL Routing over Low Power Lossy Networks IUT-T NGN CENELEC Smart Metering CEN Smart Metering ISO/IEC JTC1 UWSN IEEE 802.xx.x ESMIG Metering WOSA KNX ZCL HGI Home Gateway Initiative EPCGlobal GS1 Utilities Metering OASIS W3C W-Mbus Relationship with Other Standards * : ESTI M2M
References KT M2M SKT M2M Activities in ETSI Connected World Conference hair_Numerex_CTO_Jeff_Smith.pdf hair_Numerex_CTO_Jeff_Smith.pdf Update of M2M Standard Work 50_MAIN/Public/ _Denver_CO/TR _Update%20of%20M2M%20Standard%20work%20v3%28Mitch%20Tseng%29.pdf 50_MAIN/Public/ _Denver_CO/TR _Update%20of%20M2M%20Standard%20work%20v3%28Mitch%20Tseng%29.pdf Overview of M2M
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 collaboratorsKno.e.sis
Evolution of the Web Web of pages - text, manually created links - extensive navigation 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
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.
A little bit about semantic metadata extractions and annotations
WWW, Enterprise Repositories METADATA EXTRACTORS Digital Maps Nexis UPI AP Feeds/ Documents Digital Audios Data Stores Digital Videos Digital Images... Create/extract as much (semantics) metadata automatically as possible; Use ontlogies to improve and enhance extraction 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 OReilly) Semantic Web focuses on artificial agents The relationship web combines the skills of humans and machines
Semantic Web Connects Knowledge The Metaweb Connects Intelligence The Web Connects Information Social Software Connects People Artificial Intelligence Personal Assistants Ontologies Taxonomies Knowledge Bases Knowledge Management Semantic Webs Intelligent Agents Enterprise Minds Group Minds Lifelogs Semantic Weblogs The Relationship Web Decentralised Communities Smart Marketplaces The Global Brain Search Engines Content Portals Databases File Servers Push PIMs Web Sites Enterprise Portals Pub-Sub Marketplaces Auctions Groupware Weblogs Wikis RSS Community Portals P2P File-sharing Conferencing IM USENET Social Networks Formal Social, Informal Implicit Powerful
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
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 Expressiveness 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)?
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, SWE Components - Languages
Catalog Service SOSSPS Clients Sensor Observation Service: Access Sensor Description and Data Sensor Planning Service: Command and Task Sensor Systems Discover 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, 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 Semantic Annotation Semantic Annotation
43 Ontology & Rules Weather Time Space Oracle SensorDB Get Observation Describe Sensor Semantic Sensor Observation Service Collect Sensor Data BuckeyeTraffic.org Get Capabilities Semantic Annotation Service S-SOS Client SWEAnnotated 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
Summary Wireless sensor network ubiquitous sensor network: M2M and Internet of Things – Including participatory sensing & ubiquitous human computation Semantic web, and semantic sensor web