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Using Ontologies to Reduce User Intervention to Deploy Sensing Campaigns with the InCense Toolkit

1st International Workshop on Ubiquitous Mobile Instrumentation. Presented by : Marcela D. Rodríguez CICESE/UABC, Ensenada, México marcerod@uabc.edu.mx. Using Ontologies to Reduce User Intervention to Deploy Sensing Campaigns with the InCense Toolkit.

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Using Ontologies to Reduce User Intervention to Deploy Sensing Campaigns with the InCense Toolkit

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  1. 1st International Workshopon UbiquitousMobile Instrumentation Presentedby: Marcela D. Rodríguez CICESE/UABC, Ensenada, México marcerod@uabc.edu.mx UsingOntologiesto Reduce UserInterventiontoDeploySensingCampaignswiththeInCenseToolkit

  2. Challengestodeploy a sensingcampaign • Decidingthegranularity of thesensedinformation • Componentsthatcollectlow-level data vs high-level data • Calibratingthesensingcomponentstothepopulationto be monitored. • Tothe particular participantscharacteristics • Indicating a calibrationcriteria • The target users are researcherswithlittleor no technicalbackground Developinga toolforbehavioral data collectionfrommobilephonestoenableresearcherswithlowtechnicalskillstoimplement a sensingapplication: InCense

  3. InCenseimplementationmodel • Session:group of componentsconnectedtoachieve a sensinggoal. • Sensors: act as interfaces withthemobilephone’ssensors • Filters:preprocessraw data fromsensors • Survey: multiplechoiceor open- endedquestions • Triggers: startsessionsifcertainconditions are met • Sink: data pool whereinthesensedinformationisassembledintofiles

  4. InCenseManager Filter Generator Template Engine InCense API Configuration file Generator OntoInCense Sensors Library Filters Library InCenseArchitecture <plug-in> <template> Mobile application Use of theInCense API forimplementing a sensingapplication JSON < / > Contextual Database <template> Class Builder Project Server Codegeneration Analyse Deploy <ontology> Filter Explorer Customize Ontology-based GUI Ontologytosupportcustomization Implement Specificationlanguage and re-usable components User

  5. InCenseManager Filter Generator Template Engine InCense API Configuration file Generator OntoInCense Sensors Library Filters Library InCenseArchitecture OntoInCense <plug-in> <template> Mobile application JSON < / > Contextual Database <template> Project Server Class Builder Codegeneration Analyse Deploy <ontology> Filter Explorer Customize Ontology-based GUI Ontologytosupportcustomization Implement User Specificationlanguage and re-usable components

  6. InCenseManager Filter Generator Template Engine InCense API Configuration file Generator OntoInCense Sensors Library Filters Library InCenseArchitecture <plug-in> <template> Mobile application OntoInCense JSON < / > Contextual Database <template> Class Builder Project Server Codegeneration Analyse Deploy <ontology> Filter Explorer Customize Ontology-based GUI Ontologytosupportcustomization Implement Specificationlanguage and re-usable components User

  7. InCenseManager Filter Generator Template Engine InCense API Configuration file Generator OntoInCense Sensors Library Filters Library InCenseArchitecture <plug-in> <template> Mobile application JSON < / > Contextual Database <template> Class Builder Project Server Codegeneration Analyse Deploy <ontology> Filter Explorer Customize GraphicalWidget Ontologytosupportcustomization Implement Specificationlanguage and re-usable components User

  8. Scenario: • “A public health organization (PHO) is interested in comparing the walking habits of older adults in the winter and in the spring. They began using InCense for data gathering from 392 individuals during two weeks in the middle of January, and then again in May. The application captures the individual location, the activity level obtained from the accelerometers. A filter infers from the GPS and accelerometer, if the individual is walking or in a vehicle as he leaves his home. When InCense detects that the user is back at home, the mobile phones, will ask the individuals to complete a survey with question related to the activity being performed and their wellness. The data captured from the individuals is sent to the PHO to find interesting correlations with standard statistical packages.”

  9. ExtendingtheFilter Library Filter Explorer

  10. ExtendingtheFilter Library a b

  11. ExtendingtheFilter Library a b

  12. ExtendingtheFilter Library

  13. Develop a sensingcampaign Participantheight

  14. Conclusions and Futurework • Theontologyacts: • As a representationalmodel: Facilitatestounderstandtheimplementationmodel of InCense • As a graphicaluser: AddsflexibiltytoInCenseToolkitforcustomizing a sensingapplication. • We plan toevaluateInCense

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