Internet Computing & Security Lab. Kyung Hee University

1. Sensor Event Processing on Grid Eui-Nam John Huh Internet Computing & Security Lab. Kyung Hee University

2. Ⅰ. Background Ⅱ. Related Works Ⅳ. Experiment Ⅴ. Conclusion Ⅲ. ARROW architecture 목 차

3. ⅠBackground

4. Background • Wireless sensor networks are increasingly being deployed in many important applications such as BT, ET, and CT. • For sharing huge amount of sensor data efficiently with diverse users, an data aggregation, processing, event processing and information dissemination are very necessary and important components. • Our proposal • ARROW (Adaptive and Reconfigurable ResOurce management for Wireless sensors using grid technology) • an architecture integrated with sensor network and Grid technology. • CGM algorithm • an efficient matching algorithm on pub/sub (publish/subscription) systems • designed for a u-Healthcare application

5. Features of the ARROW • Required Function • Scalability (Distributed) for High volume data • Unified Access to Heterogeneous Storage • Deriving Event by Knowledge Processing • Time(Periodic)/Event(on-Demand) Triggering scheduler • Matching on Pub/Sub system • SOA • Transformation of Messages • Aggregated event • Diverse Delivery Mechanism

6. ⅡRelated Works

7. Related works INFO-D standard OGSA-DAI • OGF INFO-D WG • define information dissemination patterns • base specification • use cases • Data Grid Middleware • provides efficient data access to heterogeneous storage • SOA employed ARROW architecture

8. Information Dissemination (INFO-D WG standard) • INFOD (Information Dissemination) provides a general means to determine which messages are to be sent from which publishers to which consumers based upon information kept in a registry. • INFOD allows the characterization of publishers, consumers and various other components using vocabularies that are meaningful to members of the communities they belong to. • INFOD extends the publish/subscribe paradigm by allowing consumers to be determined dynamically based on the message content. • INFOD allows subscribers to determine which messages should be created in response to events.

9. INFO-D Basics states messages events consumers State changes can cause events which in turn can cause messages to be produced which are then delivered to consumers

10. INFO-D Disseminators states events publishers messages Disseminator INFOD disseminators help with distributing messages INFOD principals (e.g., Publishers, Disseminators, Consumers) can define message filters for any stage INFOD components apply the appropriate filters at any stage messages INFOD disseminators can communicate with other INFOD disseminators through propagation consumers

11. INFO-D components • Registry • provides storing, managing the subscriptions distributing the subscriptions to the disseminator(s) • Publishers • publish status or event data. • Disseminator • receives the consumer’s and publisher’s information from registry • schedules disseminating job using this information. • sends data to consumer when event data is collected. • Subscribers • translate consumer/publisher’s information format from web page to suitable registry format. • Subscriptions • specify consumer’s interests.

12. INFO-D Basic Patterns Base INFO-D Service 1 Base INFO-D Service 2 Base INFO-D Service 3 Base INFO-D Service 4

13. Info-D Pattern Single Disseminator Service Subscriptions Operations • Our experiment targets single disseminator service pattern. • In the architecture, five entities - sensors or Application specific service as publisher, doctors as consumers, subscriber, registry, and disseminator – are modeled. Registry Subscriptions Publication Publication Consumers Publisher Disseminators

14. OGSA-DAI • An extensible framework for data access and integration. • Expose heterogeneous data resources to a grid through web services. • Interact with data resources: • Queries and updates. • Data transformation / compression • Data delivery. • Customise for your project using • Additional Activities • Client Toolkit APIs • Data Resource handlers

15. OGSA-DAI Core Features • A framework for building applications • Supports data access, insert and update • Relational ; XML; Files • Supports data delivery • SOAP over HTTP • GridFTP; FTP • Inter-service • E-mail

16. OGSA-DAI Activities • An Activity dictates an action to be performed • Subset of activities available to a Data Resource • Specified in configuration files • Data can flow between activities

17. OGSA-DAI Basic Activities(1/2)

18. OGSA-DAI Basic Activities(1/2)

19. OGSA-DAI Activities using perform documents OGSA-DAI Data Service Activity1 Activity1 Client Activity2 Activity2 Activity3 Activity3 Perform document

20. ⅢARROW architecture Adaptive and Reconfigurable ResOurce management for Wireless sensor networks

21. ARROW ARROW for Event Processing Information Dissemination CGM algorithm Sensor Network INFO-D standard OGSA-DAI Infra Grid Standard

22. Requirements of the ARROW for u-Health System • The proposed system provides some specific functions to disseminate data efficiently for u-Health environment as follows: 1) The information dissemination system should send sensor event messages to consumers directly on triggering and deliver them every period subscribed. 2) The Info-D system should support several types of data such as blood pressure, temperature, pulse, or electrocardiogram 3) The Info-D system should allow subscribers to describe two ranged predicates with 2 or more data types(blood pressure/temperature/heartbit) • low_threshold_value < observed_data < high_threshold_value • low_threshold_value > observed_data, high_threshold_value < observed_data 4) The Info-D system need to process efficiently to handle a number of subscriptions of various consumers-community. (n:n relationship)

23. ㆍ Event Processing and Dissemination Information Disseminator Provides creating and reconstructing new activities using perform document. stores various types of data and transfers the data to xml format, and delivery to users CGM algorithm provides an efficient matching algorithm using predicate grouping to reduce matching time and resources. OGSA-DAI INFO-D standard Information Disseminator part of ARROW is designed based on INFO-D single disseminator service pattern

24. Features of the ARROW • Supports • Scalability (Resources and Users) by using Grid tech. • Unified Access to Heterogeneous Storage using OGSA-DAI • Deriving Event by Knowledge Processing on Cluster • Time(Periodic)/Event(on-Demand) Triggering scheduler • N:N Matching on Pub/Sub system • Statefull Web Services (SOA-WSRF) • Transformation of Messages • Aggregated event using event aggregation queue(EAQ) • Diverse Delivery Mechanism with Web Service (Activity)

25. Knowledge Processing Stream Processing Stream Processing Filtration Doctrine Rule Run-time Monitoring Knowledge Processing 2 Resource Cluster A 3 4 Filtration & Preprocessing worker Sensor Network Communication Manager Grid Proxy Real Time Resource Broker 4 Resource Cluster B worker 5 Application Specific Service 1 6 1 Registry 1 Disseminator 7 Event Processing 8 8 Database Replicas OGSA-DAI Core Consumer Oracle DB XML DB MySQL DB Actuator Web Portal Event Processing & Disseminator ARROW Architecture

26. Sensor Network Communication Manager Stream Processing Knowledge Processing Stream Processing • Step 1: Sensors sense data from the environment. • Step 2: CM gathers the data and propagates to FM for pre-process and low level filtration. • Step 3: FM performs simple pre-processing sensor data and sends the data새 application Specific Service and RM that will derive new events. • Step 4: RM monitors computational resources and allocates the resources to remove the anomalies, and to derive events from history. RM can reconfigure resource allocation dynamically. • Step 5: RM sends to Application Specific Service to disseminate the data to users using Information Disseminator. Filtration Doctrine Rule Run-time Monitoring 1 Resource Cluster A 2 4 Filtration & Preprocessing worker Grid Proxy Real Time Resource Broker 4 Resource Cluster B worker 5 Application Specific Service

27. Information Disseminator • Step 1: Each entity should register their information to the registry. • Step 2: Application specific service sends the processed(filtered) data to the disseminator. • Step 3: Registry can manage, store subscription of each entities and distribute the information to disseminator(s). Disseminator can distribute sensor data to users efficiently using the information • Step 4: Disseminator detects event data for immediately delivery and schedules time based data for periodic delivery according to the consumer’s subscriptions. It also stores sensor data to heterogeneous and distributed resources in amount of uniform access using OGSA-DAI.

28. Event Detection and Information Disseminator

29. Why CGM algorithm? (1/2) • Consumers require “ranged predicates” to describe their interests in u-Health sensor event processing system. • If predicate’s condition requires certain range, the problem become harder and difficult to apply previous techniques to trigger events. • This causes complexity on matching in the time and space domain. we need an efficient matching algorithm considering various ranged predicates of subscriptions requested by many subscribers such as nurses, staffs, and doctors.

30. Why CGM algorithm? (1/2) • Without an efficient matching algorithm, a data should be matched to “all“ predicates sequentiallyin the EP (Event Processing) and ID(Information Dissemination) system. • A sequential matching, which matches all predicates, wastes computational resources and matching time. we proposed scheme reduces resources consumption and matching time efficiently using grouped index of predicates. grouped index of predicates is used to search subscriptions joining predicate id and subscription id.

31. BDD model for sequential matching of “all” predicates

32. The ranged predicates • Patient states are changed continuously within certain range(min, max) • Consumers will require events in various range. • The ranged predicates are more efficient to describe consumer’s interests. • The ranged predicate conditions are equivalence, subset, or intersection.

33. Expressions of predicates • The Information Dissemination system in the ARROW supports various expressions (rules) of predicates. • Case 1 - (mPV< data < MPV) is used when consumers want to know normal patterns of sensed data as shown in figure (a). • Case 2 - (mPV>data || MPV<data) is used when consumers need to receive unusual states of the patient as shown in figure (b). • Case 3 - (mPV> data1 && MPV<data2) is used to support for two outputs of a sensor such as blood pressure as shown in figure (c).

34. The grouping of predicates • When a sensor data reaches to Information Dissemination system, the data will be matched to “all” predicates of subscriptions exactly. • To reduce overhead in matching, ARROW employed the grouping scheme. • The algorithm groups all minimal threshold value of predicates from all subscription denoted to mS for the set of mPVs, and MS for the set of MPVs. • mS={mPV1,mPV2…..mPVi} • MS={MPV1,MPV2....MPVi}

35. CGM (Classed Group Matching ) Algorithm

36. Subscription’s distribution • As the range of the human body conditions has limitation, consumer’s interest(subscription) is also within reasonable boundary.

37. Grouping predicates(1/2) • Statistical method • decide the number of class for mS and MS statistically. The m means number of mS and M means number of MS. • m=1+log2 n(mS) • M=log2 n(MS) • interval of each class • Interval of mS : (y-x)/m • Interval of MS : (Y-X/M)

38. Grouping predicates(2/2) • Set number of groups of predicates • By setting various numbers of groups for grouping. • The m means number of mS and M means number of MS. • interval of each class • Interval of mS : (y-x)/m • Interval of MS : (Y-X/M)

39. ⅣExperiment

40. Case 1 (mPV< data < MPV) • Blue line shows total matching time using CGM algorithm. The average matching time is 262. • examples of sensed data : • data= { 30, 33, 36, 38. 39} • Wasted matching time • Sequential matching : 374 times • CGM algorithm : 136 times • CGM can reduce 2/3 of wasted matching times than sequential matching

41. Case 2 (mPV > data || MPV < data) • Blue line shows total matching time using CGM algorithm. The average matching time is 427. • examples of sensed data : • data= { 30, 33, 36, 38. 39} • Wasted matching time • Sequential matching : 216 times • CGM algorithm : 143 times • CGM can reduce 1/3 of wasted matching times than sequential matching

42. Case 3 (mPV > data1 && MPV < data2) • This case is designed for sensed pair inputs. • examples of sensed data : • (data1=29, data2=38) • (data1=29, data2=33) • (data1=33, data2=37) • (data1=32, data2=35) • Blue line shows total matching time using CGM algorithm. • Wasted matching time • Sequential matching : 408 times • CGM algorithm : 119 times • CGM can reduce 2/3 of wasted matching times than sequential matching

43. Conclusion • ARROW architecture • provides information dissemination based on INFO-D standard • supports a gateway between sensor network and Grid • processes, manages huge amount of data • provides interfaces for consumers • CGM algorithm • considers three types of predicates • reduces matching overhead significantly • Future research • find the relation between the number of groups and matching time • propose efficient algorithm will be proposed to find appropriate the number of class rather than well-known statistical method. • Semantic matching

44. Thank you