10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Event Manag...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Contextuali...
10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Thanks fory...
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Event Management Proposal for Distribution Data Service Standard

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Event Management Proposal for Distribution Data Service Standard

  1. 1. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Event ManagementProposal forDistribution DataService StandardJosé-Luis Poza-LujánJuan-Luis Posadas-YagüeJosé-Enrique Simó-TenUniversity Institute of Control Systems andIndustrial Computing (ai2)Universitat Politècnica de València (UPV).···Events···········ConditioComponentOperationsQoS AlarmsQoC AlarmsMessage FiltersEvent-m_iState-m_iType-m_pCondition+setState()+getState()+getType()EventAlarm-m_iAlarmType-m_iAlarmValue-m_iStartupAlarm-m_iThersholdRising-m_iThersholdFallingEventComponent-m_iEventComponentType
  2. 2. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workNCS, EBC and Middleware• Event-Based Control (EBC) paradigm(also called event-driven control) isadopted to implement systems where…– The periodic sampling is not possible.– The periodic sampling is not recommended• In networked control systems(NCS), distributed control elements areconnected by a network.– Control elements needs to know thetechnology– Problems with synchronization
  3. 3. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workNCS, EBC and Middleware• A middleware enhances and offers tocontrol elements a set of services in orderto facilitate the access to the network.– If EBC is used in NCS, the middleware will be anessential component.• Middleware architecture is based oncommunications paradigms.– Message passing– Client-server– Publish/Subscribe (P/S)– Blackboard.
  4. 4. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workDDS Model• Data Distribution Service for Real-TimeSystems (DDS) is an OMG standardmiddleware based on the P/S paradigm.– Offers time-controlled communicationsbetween components using Quality ofService (QoS) polices.DomainParticipantwrite read, takeon_data_availableProducer ConsumerDataWriterDataWriterPublisherDataReaderListenerSubscriberTopicDataWriterDataReaderSubscriberPublisherDataReaderTopicTopicDomainParticipantDomainParticipantread, take writeConsumerProducer &ConsumerConsumerread, take
  5. 5. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workDDS Model• DDS can be used to send any kind ofData, including event data.• DDS allows the application (controlcomponent in NCS) to perform flexiblefiltering of events.EntityStatusCondition10..1statusconditionConditionWaitSet* *GuardCondition ReadCondition
  6. 6. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workDDS Model• DDS can be used to send any kind ofData, including event data.• DDS allows the application (controlcomponent in NCS) to perform flexiblefiltering of events.• NCS needs certain event management,But DDS does not define a built-in eventtype and advanced event management.
  7. 7. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workEvent management proposal• DDS offers adequate support tocoordinate communications betweennodes, but it does not provide amechanism to review the internalcharacteristics.• To extend the capacity of DDS, a newcomponent, called Action, is added.• The role of Condition and Waitsetcomponents has been increased.
  8. 8. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workEvent management proposal• Main components are“Events”, “Conditions” and “Actions”···Events···Actions·····················ConditionsComponentOperationsQoS AlarmsQoC AlarmsMessage FiltersComponentFunctionsQoS ConfigurationQoC ConfigurationMessage Filtering
  9. 9. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workEvent management proposal• In our proposal Condition is similar toWaitSet DDS element, Event is similar toCondition and Action is the new elementEntity1*condListEventEventAlarm EventFilterEventComponentConditioneventList*Action1 **actList
  10. 10. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workImplementation• A distributed mobile robot navigationenvironment has been used to test thevalidity and usefulness of the proposedmodel.• The mobile robot is controlled by a set ofcontrol algorithms.– Reactive behaviours algorithms, are embeddedwithin the robot.– The deliberative algorithms, are implementedin distributed nodes• In the experiment, all algorithms areimplemented in distributed nodes
  11. 11. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workImplementation• The algorithm used is the “obstacleavoidance” based on Braitenbergvehicles behaviours.Mleft MrightS0S1S2 S3S4S5S6S7
  12. 12. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workImplementation• Obstacle avoidance algorithm1. Sensor detects obstacle and sends thedistance to control node with a specifiedperiod2. Control node changes the robot speed andpath in function of the distance detected by allsensors in the ring using the formula to eachmotor
  13. 13. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workImplementation• Optimization with event management– The DDS based middleware filters sensormessages and only sends significantmessages. The time interval betweenmessages can change the robot speed bymeans the formula
  14. 14. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workExperiment and results• Two scenarios have been testeda) Robot in corridorb) Wall in front of robot• Event Management influence1) Robot navigation without middleware eventdetection2) The same navigation with event detectionand one Action linked by means a ConditionScenarios(1) DDS(2) DDS EventManagementSamplingPeriodDistanceAverageSamplingPeriodDistanceAverage(a) Robot in corridor 10.0 2.1 62.6 2.1(b) Wall in front of robot 10.0 1.1 9.4 0.6
  15. 15. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture worka) Robot in corridor– No significantdifferences indistance– Message loadoptimizationb) Wall in front ofrobot– Significant differencesin distance (speedoptimization)– Message loadoptimizationExperiment and results
  16. 16. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013ContextualizationDDS ModelEventmanagementproposalImplementationExperiment andresultsConclusions andfuture workConclusions and future work• Proposal to increase the eventmanagement system proposed in the DDSstandard• The most significant contribution is theinclusion of a new object called Action.– Actions automatically make changes on themiddleware based on a combination of events.• The Action object has been tested with asimple mobile robot system.• Future work– Test the middleware with complex eventcombinations to generate different actions.– Use priorities in Conditions or in Actions.
  17. 17. 10th International Symposium on Distributed Computing and Artificial IntelligenceSalamanca, 22nd-24th May, 2013Thanks foryour attention···Events···········ConditioComponentOperationsQoS AlarmsQoC AlarmsMessage FiltersEvent-m_iState-m_iType-m_pCondition+setState()+getState()+getType()EventAlarm-m_iAlarmType-m_iAlarmValue-m_iStartupAlarm-m_iThersholdRising-m_iThersholdFallingEventComponent-m_iEventComponentTypeThe study described in thispaper is a part of thecoordinated project COBAMI:Mission-based HierarchicalControl. Education andScience Department, SpanishGovernment. CICYT: MICINN:DPI2011-28507-C02-01/02

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