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Building the Environment for the Things as a Service

Building the Environment for the Things as a Service. Bruxelles , 12 th May 2013. Concertation Meeting “ Software&Service , Cloud Computing”. The BETaaS platform.

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Building the Environment for the Things as a Service

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  1. Building the Environment forthe Things as a Service Bruxelles, 12th May 2013 Concertation Meeting “Software&Service, Cloud Computing”

  2. The BETaaSplatform Itis an IoTplatform to executeM2Mapplicationswithin a localcloud of gateways, focusing on context and abstracting from devicesthough an ontologydefinition. Itadapts to severaldevicetechnologies, includingETSIstandards. Features: Things Services Semantic-basedaccess Contextand Resource awareness ETSI adaptation Qualityof Service (QoS) management Big Data Virtualization

  3. Motivations

  4. Functional Model S S S GW GW GW GW GW GW

  5. Functionalmodelinstance GW1 GW2 GW3 GW4 GW5 SYSTEM3 Service Service Service Service Service BETaaS Instance SYSTEM2 Local Component Local Component Local Component TaaS Local Component Local Component SYSTEM1 Adaptation A1 An PHY P1 Pn BETaaS-Unaware BETaaS-Aware

  6. Things Services • Basic Services generated by the platform and associated to the content and contextsurrounding the thing. • They are managed by the TaaSlevel and are notvisible to Application Layer. • They are distributedamong the GWs. • Equivalentthingsservices are selectedaccordingly to performance-needsrequested by the application

  7. Access to the Platform

  8. Content Awareness A thing behaves differently depending on the context and so depending on the things locatedin its surroundings. • Model the Knowledge • build a network of ontologies  BETaaS Things Ontology • model different aspects of the 2 BETaaS scenarios: Home Automation and Smart City. • Model the Context • The location of the things (Home Automation: floor, room) • The functionality of the things: • Type of thing (sensor/actuator). • Type of measurement (e.g. humidity, temperature, presence, irrigation, etc) • Communication protocol(e.g. ZigBee) • Build a network of ontologies to model the context  BETaaS Context Ontology.

  9. Learning Algorithms • Goal: to infer information which is not explicitly reflected in the ontologies. • How: using learning algorithms that bring self-management features into BETaaS: • Semantic reasoner + semantic rules • Rule to detect Equivalent Thing Services • Rule to detect the need to combine Thing Services • Rule to calculate the operator to combine Thing Services

  10. Quality of Services

  11. Local Cloud Operations

  12. M2M Adaptation - ETSI • Add one plugin for each M2M technology to take care of the corresponding communication protocols. • Focus on M2M ETSI • Most promising M2M solution • Great architectural flexibility BETaaS GSCL GIP END DEVICE UpperLayers UDP UDP ProprietaryProtocol Discovery GetData Register Set Sensor Data Resource Container Protocoladaptation Sensor Capabilities ETSI Plugin Command Container Command

  13. Trial: Smart City Scenario • EDI System (Electronic Devices for Illumination). • Passive InfraRed PIR Sensors. • Car modules. • Use Case • Lamp dimmeringwhenpresencedetected. • Nearest car discovery • Automatically and dinamicallylampilluminationaccording to user position • Statistical analysis on lampstatus • Goal • Interoperabilitybetweenthreepre-existingservicesmanaged by a GW each. • Distribution of services • Content access & Contextawareness • Big Data

  14. Trial: Home Automation • Proprietary software acting as an Alarm system together with the required presence sensors. • Proprietary Domotics System accompanied with the appropriate sensors. • BETaaSApplication specifically created for Water Gardening Systems.

  15. Motivations vs Solutions • BETaaS will allow the definition of new M2M applications whose scope spans across different domains.

  16. Motivations vs Solutions • BETaaS runtime platform will ease the development and execution of content centric user applications. • Data and resources would be defined from the content point of view, regardless of the physical location in the local cloud. • .

  17. Motivations vs Solutions • BETaaS platform stays close to the M2M nodes, through the creation of local clouds among nodes in concrete contexts • Better reliability • Better control over the location of data • Better scalability • A reduction of the overall energy consumption by reducing the transmission costs

  18. Motivations vs Solutions • BETaaS platform will use semantic technologies in M2M networks in order to: • filter and unify the information that comes form sources of very different nature; • discover services or things; • model the behaviour of the things so they can react to unexpected circumstances or to changing conditions.

  19. Motivations vs Solutions • Optimized resource reservation and allocation to services. • New QoS measures will be considered, like, e.g., energy consumption rates in battery-operated devices

  20. Motivations vs Solutions • BETaaS enables virtualization in ARM-based devices, so it is possible to perform more complex tasks in the local environment (i.e. Big Data analysis). • BETaaSproposes a resources management mechanism, which benefits the BETaaS platform itself and the applications.

  21. T2.5 - IntecsThank you

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