FIWARE Global Summit - Thing’in, an NGSI-LD-compatible Graph Database for System-level Representation of IoT Environments
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The document discusses 'thing’in,' a graph-based platform for representing IoT and cyber-physical systems that utilizes an NGSI-LD compatible model. It explains the platform's role in maintaining structured, semantic relationships between various IoT entities and is distinct from traditional IoT platforms by not managing devices or storing raw data. The 'thing’in' graph serves as a comprehensive system description tool, enabling interoperability and information sharing between IoT applications and data sources.
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{« street X » :
« hasState » : [
« Congested »
……
]
}
Information abstraction “à la FIWARE” vs. Thing’in
FIWARE “Context Entities” are implicitly thought of as “container things” or “things containers”
consolidate and jointly abstract away multiple sources of context data
relay corresponding information to applications
Thing’in captures all granularities:
atomic things & devices
containers and basic systems
composite systems & systems of systems
What’s the current
traffic density
in street “X”?
FIWARE context API
inductive-loop vehicle
detector
surveillance
cameras
driver smartphones
Notify me in case of congestion
of street “X”](https://image.slidesharecdn.com/2019-10-23-thinginfiwaresummitberlin-191028103551/85/FIWARE-Global-Summit-Thing-in-an-NGSI-LD-compatible-Graph-Database-for-System-level-Representation-of-IoT-Environments-10-320.jpg)
FIWARE Global Summit - Thing’in, an NGSI-LD-compatible Graph Database for System-level Representation of IoT Environments
- 1. An NGSI-LD compatible graph-based platform for contextual system-level representation of IoT/CPS environments Dr. G. Privat Senior Scientist Orange Labs Grenoble, France Thing’in FIWARE Summit October 23, 2019 , Berlin23
- 2. 1.What’s Thing’in? A social networks of things, i.e. a graph 2.What are these « things » in Thing’in? From IoT/M2M Devices to things, from things to context entities, from context entities to Cyber-Physical Systems 3.What’s this Thing’in graph? Why a graph? What kind of graph? Property Graphs vs. RDF graphs 4.What kind of systems are captured by the Thing’in graph? Multilevel Cyber-Physical System decomposition Capturing bottom-up vs top-down systems in NGSI-LD & Thing’in Outline
- 3. 1.What’s Thing’in? 2.What are these « things » in Thing’in? 3.What’s this Thing’in graph? Why a graph? What kind of graph? 4.What kind of systems are captured by the Thing’in graph?
- 4. Thing’in is : • A graph database • maintaining persistent and curated representations of IoT environments at large… • …as a property graph , with « things » ( FIWARE entities) as its nodes • capturing structural and semantic context as things relationships, properties and classes • A multi-sided platform • mediating consolidated information between: • data providers • other platforms • applications • enabler developers Thing’in enablers Thing’in graph & platform IoT Applications IoT networks & platfoms Other data sources (open data, etc) Linked open data
- 5. Thing’in is NOT: • Just another IoT platform: • Thing’in does not manage IoT/M2M devices and their connectivity • Thing’in does not store raw data • A pure « knowledge graph » à la Google: • Thing’in is exclusively about instances of physical things • Thing’in’s scaffolding is structural relationships between things • Semantic graph is an overlay, « the icing on the cake » • The Web of Things, or the web of data: • Thing’in may be distributed ,...but not to that extent: • Thing’in is centrally managed and curated
- 6. Thing’in as a complementary platform to FIWARE sensor data sources developers end users other data providers FIWARE Apps LiveObjects Other IoT Platforms: OpenMTC, OM2M… sensor data sources Open Data Repositories Thing’in
- 7. 1.What’s Thing’in? 2.What are these « things » in Thing’in? 3.What’s this Thing’in graph? Why a graph? What kind of graph? 4.What kind of systems are captured by the Thing’in graph?
- 8. 8 Internet of Things Internet of Devices Core Internet Actuator Sensor Beyond the Internet of (connected) Devices: bringing un-connected physical things into the IoT fold Thing Thing Thing Thing Thing Thing Thing Thing Thing Thing
- 9. Digital avatars / twinsPhysical « Things » stand for Physical Things, digital twins & networked-device proxies networked device proxies Sensor Actuator
- 10. 10 10 {« street X » : « hasState » : [ « Congested » …… ] } Information abstraction “à la FIWARE” vs. Thing’in FIWARE “Context Entities” are implicitly thought of as “container things” or “things containers” consolidate and jointly abstract away multiple sources of context data relay corresponding information to applications Thing’in captures all granularities: atomic things & devices containers and basic systems composite systems & systems of systems What’s the current traffic density in street “X”? FIWARE context API inductive-loop vehicle detector surveillance cameras driver smartphones Notify me in case of congestion of street “X”
- 11. Cyber-Physical Systems From IoT/WoT to CPS (Cyber-Physical Systems) opening up « black-box » Things Internet of Devices Actuator Sensor Core Internet
- 12. 1.What’s Thing’in? 2.What are these « things » in Thing’in? 3.What’s this Thing’in graph? Why a graph? What kind of graph? 4.What kind of systems are captured by the Thing’in graph?
- 13. Why graph representations? Graphs are the most universal, versatile and adaptable way to structure information −graphs do not enforce any other rigid a priori structuration −they work by default under « open world » assumptions Graphs capture multi-scale & multi-level « systems of systems » composition Graphs build up incrementally and become richer with each added link −self-reinforcing information percolation within the graph Graph databases are extensively used: they scale!
- 14. Example of FIWARE-level Graph model example in city environment Street S1 congested monitors Inductive-loop detector hasState monitors Camera Crossing C1 Controls Traffic light L1isConnectedTo Vehicle V1 isInside smartphone locates Relationships between entities capture invariant or slowly varying configuration info Properties of entities may capture instant state information or permanent features hasPart lane
- 15. 15 A tale of two graphs: Property Graphs vs RDF… Property Graphs (PG) = common denominator model of most graph data bases Halfway between object data models & semantics-oriented graph models (à la RDF) − nodes of the graph have attached properties − just as objects have attached key-value attributes − relationships are « first-class citizens » of the model − they can also have properties Differences with RDF − strong distinction between properties with literal objects and those with resource objects relationships − relationships & properties are uniquely identified when instantiated no reification needed Street S1 hasCoverage congested monitorsCamera hasState 70%
- 16. 16 The NGSI-LD Information Model, as applied in Thing’in Things /devices AND systems at all levels of nesting are all represented by nodes (vertices) of the graph Systems are also captured as subgraphs of the overall graph Relationships capture the structure of these systems mostly static or semi-static – for systems that are physical networks (e.g. electrical grid, water distribution system), the graph matches the structure of the physical network Properties may capture: – permanent features of a thing – state of a system (in the sense of dynamical system theory) All nodes and relationships are defined by reference to known classes/categories of shared ontologies /vocabularies NGSI-LD cross-domain ontology provides grounding for common-denominator IoT/CPS classes The Thing’in NGSI-LD graph can be exported as an RDF graph to become part of the Linked Open data cloud
- 17. Referent Physical Entities to class) Structural graph Ontology graph Semantic Referencing Graph ParkingLot P1 thoroughfare street parking Lot parking structure Street S1 Parking lot PL1Traffic Light TL1 refersto refersto
- 18. 1.What’s Thing’in? 2.What are these « things » in Thing’in? 3.What’s this Thing’in graph? Why a graph? What kind of graph? 4.What kind of systems are captured by the Thing’in graph?
- 19. Thing’in as system description graph: Capturing nested & entwined Cyber-Physical Systems Multiple system composition levels in complex environments: the building is inside the city, but building subsystems are not directly city subsystems the apartment is inside the building, but home « things » are not directly building subsystems Need for proper separation of concerns data structuration is still paramount! Distinguish different views of same physical « plant » Should be used to manage privacy, security & legal ownership issues with corresponding data opensOnto Street S1 Crossing C1 opensOnto Controls Traffic light L1 adjacentTo hasState red hasState congested monitorsCamera C2 hasCoverage 70% BuildingA hasPart Apartment A Parking GarageG1 LiveBox B1 LED LightL1 TeddyBear isContainedIn hasPart (Outdoor) CameraC1 ElectronicGateLockEG1 AlarmA1 isContainedIn isContainedIn FireSensorFS1 Place PG2 hasPart Room R1 hasPart isContainedIn Parking Lot L1 1865/01/01 Place PL3 available Y/N Place PA1 available Y/N Parking Lot L2 hasPart Place PL2 Gate G1 actuates monitors ConstructedAt isLocatedAt N 47° 37' 9.7536'‘ E 6° 9' 10.5726''
- 20. Building Structural Graph Building Security System Graph Parking Garage Graph Smart City Graph opensOnto Street S1 Crossing C1 opensOnto Controls Traffic light L1 adjacentTo hasState red hasState congested monitorsCamera C2 hasCoverage 70% BuildingA hasPart Apartment A Parking GarageG1 LiveBox B1 LED LightL1 TeddyBear isContai nedIn hasPart (Outdoor) CameraC1 Electronic GateLock EG1 AlarmA1 isContained In isContain edIn FireSensorFS1 Place PG2 hasPart Room R1 hasPart isContai nedIn Parking Lot L1 1865/01/01 Place PL3 available Y/N Place PA1 available Y/N Parking Lot L2 hasPart Place PL2 Gate G1 actuates monitors ConstructedAt Apartment Graph Parking Management System Graph isLocatedAt N 47° 37' 9.7536'‘ E 6° 9' 10.5726''
- 21. City C Building B1 Building B2 isContainedIn isContainedIn Building B1 Wall W1 Floor F1 hasPart hasPart Top- Down System comp. Bottom -up System comp. Top-down vs bottom-up system composition
- 22. Referent Physical Entities to class) Ontology graph Semantic Referencing Graph thoroughfare street parking Lot parking structure Structural graph Street S1 Parking lot PL1Traffic Light TL1 System composition graph Trafic management System graph Parking management graph Traffic Lights graph
- 23. BuildingA hasPart Apartment A Parking GarageG1 LiveBox B1 LED LightL1 TeddyBear isContai nedIn hasPart (Outdoor) CameraC1 Electronic GateLock EG1 AlarmA1 isContained In isContain edIn FireSensorFS1 Place PG2 hasPart Room R1 hasPart isContai nedIn Parking Lot L1 ParkingAggregation SystemS1 Graph hasAvailabilities 1865/01/01 Building Security System Graph isNodeOf Graph Overall Building Graph Building Structure Graph isSub GraphOf ApartmentGraph isNodeOf Graph Overall CityGraph isSub GraphOf isNodeOf Graph Place PL3 available Y/N Place PA1 available Y/N Parking Lot L2 Building ParkingGraph hasPart Place PL2 Gate G1 actuates monitors isNodeOf Graph isNodeOf Graph isSub GraphOf ParkingLot Graph isNodeOf Graph createdAt ConstructedAt 2019/01/01 42OneM2M: device OneM2M: thing SOSA:Sensor NGSI-LD: graph Sosa: observes SOSA: actuation DUL:PhysicalObject DUL: InformationEntity RDFS:SubClassOf RDF:type
- 24. Complementary Example : graph for sharing & interlinking information between 3 smart city operators flat view Retractable Bollard B1 Surveillance Camera A1 Parking A parking space B Alley B Street A hasState gate A hasState hasState hasState hasState Parking A Management System Parking B Management System Traffic Management System Open/closed Empty/ Occupied open/ closed y% congested n/N available spaces Inductive sensor Barrier B handicapped parking space AH1 hasState Empty/ Occupied EV charging space AE1 hasState Empty/ Occupied EV fast charger hasPower P watts hasDirection OneWay/TwoWay hasCoverage 70% Traffic Light TL1
- 25. Complementary Example : graph for sharing & interlinking information between 3 smart city operators viewed at the graph composition level Retractable Bollard B1 Surveillance Camera A1 Parking A parking space B Alley B Street A hasState gate A hasState hasState hasState hasState Open/close d Empty/ Occupied open/ closed y% congested n/N available spaces Inductive sensor Barrier B handicapped parking space AH1 hasState Empty/ Occupied EV charging space AE1 hasState Empty/ Occupied EV fast charger hasPower P watts hasDirection OneWay/TwoWay hasCoverage 7 0 % Traffic management System Graph isNodeOf Graph Parking A management System Graph Smart City System Graph Parking A management System Graph isNodeOf Graph isSub GraphOf Traffic-lights control System Graph Traffic Light TL1 isNodeOf Graph isNodeOf Graph May Override
- 26. Take-home message The Thing’in graph is a special type of NGSI-LD compatible property graph that captures − IoT devices with their sensing and actuation relationships to their environment − the multilevel system composition of this environment The graph is the basis for cross-silo configuration-stage information mediation between − IoT applications − low-level IoT platforms − other data sources (e.g. Open Data repositories) Formal semantics gets overlaid to the graph The graph is part of the linked open data cloud through RDF import/export IoT IoT IoT IoT IoT IoT IoT