Event-based Middleware for Sensor Networks

1. Event-based Middleware for Sensor Networks Bin Wu Roy George Department of Computer and Information Science Clark Atlanta University rkavil@cau.edu

2. Incorporating Semantics in Sensor Networks (SSN) - Driver Applications Event-based Multisensor Data Fusion Why Use Events? Event Definitions Event Hierarchical Model System Architecture Event Ontology Previous Work Architecture of Event Ontology Related Ontologies Event Ontology Language The Application of SSN – Healthcare Application Conclusion & Future Work Overview

3. Enables incorporation of semantics into network definitions Adaptive with capability to respond to environmental changes Deals with event streams Facilitates automatic processing Incorporation of Semantics in Sensor Networks

4. Decision support is based on the dynamically updated events. Events are fused from complex and heterogeneous data sources. Need for real-time or near real-time data storage and retrieval. Spatio-temporal data is norm rather than an exception. Exploration is the predominant mode of interaction rather than query. Context and state are very important. In the applications of SSN …

5. Explosion of Raw Data from Heterogeneous Data Sources Need for Real-time Decision Support Need for Service Oriented Integration Need for High Performance Data Repositories … And The Problems Are

6. Heterogeneous data is located within silos. Relationships between events are hard to recognize. Context information of an event lost. Keyword based Queries Centralized handling of Events Current Approaches

7. Operating systems aDBMS Interface design Distributed simulation systems Event-based Systems: Applications

8. Temporal and Spatial properties are a fundamental organizational mechanism for events. Provides a natural way of filtering data. Real-time decision making. Why Event-based Middleware?

9. An event is an occurrence or happening of significance in spatial-temporal-attribute space. Given k events 1, …, k, the ith event is formally denoted as and uniquely identified by its event identifier, called eID. In this notation:tcharacterizes the event temporally; s denotes the spatial location(s) associated with the event; are the attributes associated with the ith event. Definition of Event

10. Where: At or in what place When: At what time What: What is the relationship between events? How does the event evolve? Features of an Event

11. ER Model of an Event eID Event Name Event latitude m m 1 1 1 1 n occurs at Space Location has longitude name n has TranscludedMedia URI n name Event Topic has n 1 Sub-topic 1 occurs at Time time 1 1 1 occurs at StartTime date time 1 occurs at End Time date

12. C2 C1 timeline t1 t2 t3 t4 C2 C1 timeline t1 t2 C2 C1 timeline t1 t2 t3 C2 C1 timeline t1 t2 t3 t4 C2 C2 C1 timeline t1 t2 t3 t4 C2 C2 C1 timeline t1 t2 t3 C2 C2 C1 timeline t1 t2 t3 Temporal Relationships* * “Maintaining Knowledge about Temporal Intervals”, James Allen, 1983.

13. Legend: event type 1; event type 2; event type 3. Temporal Scenario 1990 1994 1996 2001 2003

14. Spatial overlap:C1 (s) C2 (s) { }: Some events in the respective categories overlap in space. Spatial independence:C1 (s) C2 (s) = { }: No event in the respective categories overlaps in space. Spatial Co-Occurrence Spatial overlap Spatial independence Legend: C1 is marked by Red Color; C2 is marked by White Color. Spatial Relationships Spatial co-occurrence:C1 (s) = C2 (s): All events in the respective categories overlap in space.

15. 1990 1994 1994 1996 1994 - 1996 2002 2003 Legend: Event type 1; event type 2; event type 3. Temporal & Spatial Scenario

16. Causality in events Causality is the relation between causes and effects. It is used for describing the evolution of an event.

17. Event Hierarchical Model

18. Web sensor data stream Legend wireless data stream local server distributed sensor reader Sensor Event Filter Engine Sensor Reader Sensor Interrogator In-memory event database Local server event data type Event portal server Real-time event management Eventmodel Event-based Edgeware Ontologies Business Service Layer Event-based Edgeware Architecture

19. Event Directory Timeline 05 / 04 / 1945, 11:00a – 12:00a Location Event Preview Event Viewer Century Year Month Graphical User Interface

20. Representation of the semantics of events, processes and states Basis of sensor-based models of the dynamic world. Distributed intelligence required to handle the transaction at point of its occurrence. Common vocabularies to needed to understand and share events The Event Ontology

21. Represents the attributes of an event, such as time, space, causality, etc. Assist the construction of associated context where events happen and reasoning the evolution of events in enterprise applications. Event ontology is used for

22. Sensor networks ontologies Current sensor network ontologies focus on: Adaptive sensor networks to determine the future state of the network (Avancha, 2004) General interface between sensor networks and Internet services that facilitates bidirectional interactions between internet users and sensors, as well as interactions between sensor networks themselves. (Ota, 2003) Describe the major properties of sensor networks such as sensor location and sensing mechanism. (Jiang, 2003) Context ontologies The Aspect-Scale-Context (ASC) model describes contextual facts and contextual interrelationships as well as allow to determine service interoperability on the context level. (Strang, 2003) CONtext ONtology (CONON) an extensible ontology for modeling context in pervasive computing environments. (Gu, 2004) Ontologies in FLAME2008 developed on three levels, upper ontology, domain and task ontologies, and application ontology, based on standards like ISO 19115 (geo metadata) and ISO 19119 (geo service). (Weiβenberg, 2004) MIX model is a set of common domain-specific vocabularies for the representation of event content. (Bornhövd, 2000) CORBRA-ONT was developed as a part of the Context Broker Architecture (CoBrA) to model places, agents, events and their associated properties in an intelligent meeting room domain. (Chen, 2003) Event-related ontologies Video Event Representation Language (VERL) ARDA-sponsored Event Taxonomy project provides a common representational framework and ontology for describing video events. (Nevatia, 2004) Discrete-Event Modeling Ontology (DeMO): discrete-event modeling (DEM) aiming to assist the researchers in simulation area. (Miller, 2004) Versatile Event Logic (VEL) was a semantic language to represent temporal relationships and events. (Bennett, 2004) Related Ontology Work

23. Time Ontologies DAML-Time http://www.cs.rochester.edu/~ferguson/daml/ Entry Sub-ontology of Time http://www.isi.edu/~pan/OWL-Time.html The vocabularies of DAML-Time & the Entry Sub-ontology of Time are designed for expressing temporal concepts and properties common to any formalization of time. Space Ontologies SNAP and SPAN spatial ontologies Spatial ontologies define a vocabulary for symbolic representation of space. The ontology of GIS Consists of vocabularies for expressing spatial relations for qualitative spatial reasoning. Related Ontologies

24. isa_role cast role - id information person - id section delivery - id actor device -type document entry -name - id - name domain ontologies Domain Ontologies Global Ontologies position spatial-temporal relation space spatial relation space time syncro_colocation … abstract place time period concrete place - name proceeding_colocation time Room -number -address part_of concrete time abstract time -tval Two-level Model of Event Ontology

25. Integrates components from related ontologies. Based on OWL. Supports semantic interoperability to exchange and share event knowledge between different domains. Event Ontology Language

26. Time Ontology Space Ontology Global Ontologies

27. Adopts the vocabularies of the DAML-Time and the Entry Sub-ontology of Time. Basic vocabularies are time:TimeInstant and time:TimeInterval classes. The objects in an event is divided into two disjoint classes: time:InstantThing and time:IntervalThing. Time Ontologies Time ontologies are proposed to express time and temporal relations. They can be used to describe the temporal properties of different events that occur in the physical world.

28. Example <tme:TimeInterval> <tme:from> <tme:TimeInstant> <tme:at rdf:datatype="xsd;dateTime"> 2004-02-01T12:01:01 </tme:at> </tme:TimeInstant> </tme:from> <tme:to> <tme:TimeInstant> <tme:at rdf:datatype="xsd;dateTime"> 2004-02-11T13:41:21 </tme:at> </tme:TimeInstant> </tme:to> </tme:TimeInterval>

29. Temporal Relationship in EOL EOL defines the following properties for describing the temporal relationships between events. • time:startsSoonerThan • time:startsLaterThan • time:startsSameTimeAs • time:endsSoonerThan • time:endsLaterThan • time:endsSameTimeAs • time:startsAfterEndOf • time:endsBeforeStartOf.

30. Adopts the vocabularies of SNAP and SPAN spatial ontologies OpenGIS Two documents: spatial relationships typical geospatial vocabularies The objects in an event is described with class: space:SpatialThing. Space Ontologies Space ontologies support reasoning about the spatial relations between events.

31. Object Ontology Event Ontology Domain Ontologies

32. Obj:Sense: Describes the sensing element. Obj:People: Describes the actors in an event. Obj:Relationship: Describes the relationships Object Ontology Object Ontology is used for describing objects in an event by a set of properties. In EOL, it is included within the obj:Product, obj:People, obj:Relationship classes.

33. Example <obj:Sense> <obj:name rdf:datatype="&xsd;string">Temperature Sensor </obj:name> <obj:manu rdf:datatype="&xsd;string">Oregon </obj:man> <obj:model rdf:datatype="&xsd;string">THC268 </obj:model> <obj: manfcDate rdf:datatype="&xsd;date">2004-09-12</obj: manfcDate> <obj:price rdf:datatype="&xsd;string">$23.97</obj:price> <obj:spec rdf:resource="http://www.amazon.com/exec"/> <obj:picture rdf:resource="http://www.amazon.com/exec1"/> </obj:Sense>

34. The event ontology can be used to describe the occurrence of different activities, schedules, and sensing events. In the event ontology document, the eve:Event class represents a set of all events in the domain. eve:SpatialTemporalEvent class is defined to specifically describe events that have both temporal and spatial extensions. Event Ontology

35. An example • <owl:Class rdf:ID="DetectedBluetoothDev"> • <rdfs:subClassOf rdf:resource="&eve;TemporalSpatialEvent"/> • </owl:Class> • <owl:ObjectProperty rdf:ID="foundDevice"> • <rdfs:domain rdf:resource="#DetectedBluetoothDev"/> • </owl:ObjectProperty> • <DetectedBluetoothDev> • <space:hasCoordinates> • <geo:LocationCoordinates> • <geo:longitude rdf:datatype...> • -76.7113 • </geo:longitude> • <geom:latitude rdf:datatype...> • 39.2524 • </geom:latitude> • </geo:LocationCoordinates> • </sapce:hasCoordinates> • <foundDevice rdf:resource="url-x-some-device"/> • <time:at> • <time:TimeInstant> • <time:at rdf:datatype="xsd;dateTime"> • 2004-02-01T12:01:01 • </time:at> • </time:TimeInstant> • </time:at> • <DetectedBluetoothDev>

36. Application

37. light meter temperature meter RFID ECG accelerometer body temperature meter PDA eWellness Edgeware route pulmometer blood pressure meter fetal heart rate sensor cellphone DB pulse oximeter maternal uterine sensor laptop fire alarming sensor Transmission Layer Ambient Sensor Network Hospital Network Body Sensor Network Emergency Care Sensor Network

38. : room : concretetime : concretetime number=2011 address=1968 Peachtree Rd. tval=12:20pm tval=11:12am to patientchristina :Cast from : timeperiod : position from : timeperiod : position to : now christina :Person : concretetime : concretetime to tval=10:10am : room tval=4:54pm patient :Role number=1012 address=1968 Peachtree Rd. : timeperiod from : position office : abstractplace : position phn :Entry david :Person doc_david :Cast to : concretetime : timeperiod patient_info :Section tval=3:24pm doctor :Role from : concretetime slit_lamp_exam :Section workstation :Device tval=1:38pm :Delivery result_report :Document

39. state k+2 state l state l+1 state l+2 state l+3 state j+2 state k 22:00 02/11/2005 9:19 02/12/2005 go to operating room delivery rest (in recovery room) episode 0 episode 1 episode 2 Domain event level 22:00 12:30 12:40 6:05 7:45 9:19 thread 1 thread 2 thread 3 thread 4 thread 5 Element event level 22:00 22:15 22:30 12:30 12:40 7:45 9:19 …… …… …… state 1 state 2 state 3 state i state j state j+1 state k+1 state m state n Data event level 3:00 3:05 6:00 6:05 6:10 6:20 level medical care domain domain Timeline (Unit: hour)

40. sensed by rdfs:subClassOf eventRelation owl: Property local individual global individual value 1968 Peachtree Rd., Atlanta, GA Sn Address …... 2011 room place hospital birth T5 Sm named …... happenAt S1 Sl+3 E1 T4 S2 T1 Sl+2 T2 S3 Sl+1 T3 Si Sl …... Sj Sk+2 …... …... Sj+1 Sk+1 Sk Sj+2 …... Amy English starttime timestamp 06:05 time named push Christina Lee Danger status Danger status endtime timepoint midwife happenDuring timeAt named time named 07:45 named happenAt role mother role T4 Sm hasMember participant RFID before role …... before before Sl+3 before father Sl+2 before Sl+1 Sl happenAt named timeAt time timepoint Steven Lee

41. Events are used as the container to encapsulate the data stream. EOL is presented a formal and extensible event model based on OWL to represent, manipulate and access event streams and their properties in intelligent environment. Numerous applications of Event-driven Semantic Sensor Networks. Conclusions

42. Semantic query for event streams will be developed. Event-based Reasoning The domain ontology of event ontology will be extend to the C4ISR application domain. Future Work

43. B. Wu, Z.J. Liu, and R. George, “Event-based Edgeware: Managing Data from RFID Networks,” International Conference on Sensor Networks, Montreal, Canada, 2005. B. Wu, Z. Liu, R. George, and K. Shujaee. “eWellness: Building a Smart Hospital by Leveraging RFID Networks,” Sep. 2005. IEEE EMBS 2005 Conference in Shanghai, China. B. Wu, R. George, “Event-based Edgeware in Hospital Networks,” submitted to Journal of UCS, 2005 Bin Wu, Rahul Singh, Punit Gupta, Ramesh Jain. “eVitae: An Event-Based Electronic Chronicle”. Demo paper, 9th International Conference on Extending Database Technology, EDBT’04. Heraklion, Crete, Greece, March 2004. Related Publications