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A Framework for Context-aware applications for Smart Spaces. ruSmart 2011 St Petersburg, 22.8.11


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A Framework for Context-aware applications for Smart Spaces. ruSmart 2011 St Petersburg, 22.8.11

  1. 1. A Framework for Context-Aware Applications For Smart SpacesMohsin Saleemi, Natalia Díaz Rodríguez, Johan Lilius, Iván Porres, Åbo Akademi University, Turku (Finland) ruSmart 2011
  2. 2. Contents• Introduction• What is a Smart Space?• Smart-M3 Platform• Context Ontology Model • System Architecture• Context Inference Rules • Application Development Tool for Smart-M3 • PythonRule Structure • Python Programming of Smart Spaces with PythonRules• Conclusions• Future Work
  3. 3. Introduction• Ubiquitous computing involving heterogeneous devices.• Need for tackling Device Interoperability.• Context : Any information that can be used to characterize the state of an entity.• Focus on the User: A context system that adapts to the user’s preference.• Against traditional context aware systems (based on sensors and static rules, requiring significant amount of human interactions to become adaptative) we chose Ontology based Context Modelling: • Expressive models. • Provides flexibility, genericity and extendibility. • Smart-M3 Ontology based solution.- E.g. PVR and mobile phone example
  4. 4. What is a Smart Space?SMART SPACE:An abstraction of space encapsulating both information from a physical space and access to this information allowing devices to join and leave the space.Publish-subscribe methods are used in these dynamically changing environments.
  5. 5. Smart-M3NOKIA’S SMART-M3 (An implementation of Smart Space):• A Multi device, Multi part and Multi vendor (M3) open source cross-domain platform for independent agents to communicate.• Semantic Information Broker (SIB): The central repository of RDF triples responsible for information storage, sharing and management through the Smart Space Access Protocol (SSAP).• KNOWLEDGE PROCESSORS (KPs) entities implement functionality and interact with the Smart Space by inserting/retrieving/querying common information.• An APPLICATION is constructed by aggregating KPs which perform tasks.• COMMUNICATION happens not device to device but through the SIB.
  6. 6. Smart-M3 concept
  7. 7. Context Ontology Model Inferred information causes the context ontology to be extended Enabling the system to initiate adaptative decisions appropriate for a particular application
  8. 8. Context Ontology Model
  9. 9. System ArchitectureContext Providers • Observed • SpecifiedContext Datatype Interpreter • Type conversion • OWL-S can be used to specify functionalityContext Reasoner/Rule Interpreter • Infer high level context info. • Based on inference rules.Ontologies • OWL ontologies define context information in the SIB.Inference Rules • Specific format • Domain specific • Can be provided as separate libraries
  10. 10. Context Inference RulesSince the end-user should not deal with the RDF storedirectly, a PythonRules module is presented to translatePython logic expressions to the SIB API (Query,Subscribe, Insert, Remove, Update).AIM:An independent PythonRules Module to allow easydefinition of Rules to model Smart Spaces:•No need for learning Query languages or treat RDF data.•Including Rule Reasoning.
  11. 11. Application Development tool for Smart-M3• Ontology-Based application development• Tool for rapid application developmentTools1. Ontology Library Generator OWL-DL -> Python and C.2. Middleware framework: Abstracts the communication with the SIB providing to the generated API handling of RDF triples and queries.
  12. 12. PythonRule Structure: With()//When()>>Then()• With clause: Assumptions, Assertions or Declarations about existence of individuals.• When clause: Conditions or events that must hold before the rule is triggered.• Then clause: Actions to trigger, Conclusions representing the inferred information.
  13. 13. Python Programming of Smart Spaces with PythonRules
  14. 14. Python Programming of Smart Spaces with PythonRules
  15. 15. Python Programming of Smart Spaces with PythonRules
  16. 16. Conclusions• Smart Spaces: well suited for ambient applications to adapt to the user’s preferences.• Information Sharing and Reusability allowed for diverse Dynamic Applications.• PythonRules: Allows End-User to configure the behaviour of the Smart Space with no knowledge of Semantic Web technologies (query languages or RDF data).• PythonRules aims at being independent of the RDF Store (other than Smart-M3 will be used).And finally: Easy UI for non programmers.
  17. 17. Future Work• Ongoing PythonRules module: Furtherdevelopment.• SIB consistency related issues (and efficient subscriptions implementation).• Privacy Control.• Integrationg with OWL-S services.• Different Domain Applications: • BioInformatics. • Office Domain, Home Automation. • Elderly Monitoring Systems, etc.
  18. 18. ReferencesSmart-M3 approach and our Development Tools:- Smart-M3 Software, Release 0.9.4 beta. Available: Smart-M3 Ontology Library Generator OWL->Python API: ontology_to_Python-API_generator_v0.9.1beta.tar.gz/- Framework for Smart Space Application Development. Kaustell, Andre and Saleemi, M. Mohsin and Rosqvist, Thomas and Jokiniemi, Juuso and Lilius, Johan and Porres, Ivan. In Proceedings of the International Workshop on Semantic Interoperability, IWSI 2011- End-User’s Service composition in Ubiquitous Computing using Smart Space approach. Saleemi, M. Mohsin and Lilius, Johan. Sixth International Conference on Internet and Web Applications and Services, IEEE, 2011.- Ontology-Driven Smart Space Application Development. River Publishers Book Chapter (in revision).MORE INFORMATION: Natalia Díaz, Mohsin Saleemi,