Generic Adaptive Middleware for Behavior-driven Autonomous Services Generic Adaptive Middleware for Behavior-driven Autonomous Services Universität Duisburg-EssenETRA Investigación y Desarrollo, S. A. National University of Ireland, GalwayThe Open University SpeechConcepts GmbH & Co. KGEmpresa Municipal de Transportes de Madrid, S. A. IERC AC4 SEMANTIC INTEROPERABILITY WORKSHOP IoT Week 2012 Josiane Parreira

Generic Adaptive Middleware for Behavior-driven Autonomous Services Generic Adaptive Middleware for Behavior-driven Autonomous Services GAMBAS – Objectives  Development of a generic adaptive middleware for behavior- driven autonomous services that encompasses:  Models and infrastructures to support the interoperable representation and scalable processing of context.  Frameworks and methods to support the generic yet resource-efficient multi-modal recognition of context.  Protocols and tools to derive, generalize, and enforce user-specific privacy-policies.  Techniques and concepts to optimize the interaction with behavior- driven services.  Validation of the middleware using lab tests and a prototype application in the public transportation domain. 2

Generic Adaptive Middleware for Behavior-driven Autonomous Services Generic Adaptive Middleware for Behavior-driven Autonomous Services GAMBAS Scenario 3

Generic Adaptive Middleware for Behavior-driven Autonomous Services Generic Adaptive Middleware for Behavior-driven Autonomous Services Interoperability issues  Heterogeneous devices  Heterogeneous data representations  Heterogeneous APIs  Lack of data semantics describing data meaning  Resource constrained devices  Sensors, mobile devices  Dynamic, frequently changing information  e.g., stream data from sensors  Large-scale, distributed networks  Data needs to be discoverable

Generic Adaptive Middleware for Behavior-driven Autonomous Services Generic Adaptive Middleware for Behavior-driven Autonomous Services GAMBAS approach t owards interoperability  Linked Data paradigm to describe sensors and data streams  Associate meaning to raw data (e.g. feature of interest, accuracy, measuring condition, time point, location, etc. )  Unified, yet flexible data representation  Integration with other existing Linked Data infrastructures.  Analysis of current sensor semantic descriptions  Semantic Sensors Networks ontology  Semantic annotations for OGC’s SWE Sensor Model Language  Development of required formalisms and ontologies to support semantic descriptions at sensor level

Generic Adaptive Middleware for Behavior-driven Autonomous Services Generic Adaptive Middleware for Behavior-driven Autonomous Services GAMBAS approach t owards interoperability  Infrastructure to explore data storage and processing capabilities of mobile devices  SPARQL-like access down to the sensor level (lightweight)  Allow RDF Stream processing  Support generation of query execution plans that not only consider network and physical costs but also adapt to the dynamics of the data  Means of exchanging the descriptions of the data and devices  Allow devices to find relevant data, without knowing a priori the data’s particular location.  Develop infrastructures to support the discovery of dynamic data

Generic Adaptive Middleware for Behavior-driven Autonomous Services Generic Adaptive Middleware for Behavior-driven Autonomous Services References  D. Bimschas, H. Hasemann, M. Hauswirth, M. Karnstedt, O. Kleine, A. Kröller, M. Leggieri, R. Mietz, A. Passant, D. Pfisterer, K. Römer, C. Truong: Semantic- Service Provisioning for the Internet of Things. ECEASST 37: (2011)  A. P. Sheth, C. A. Henson, and S. S. Sahoo. Semantic Sensor Web. IEEE Internet Computing, 12(4):78-83,  E. Bouillet, M. Feblowitz, Z. Liu, A. Ranganathan, A. Riabov, F. Ye, A semantics-based middleware for utilizing heterogeneous sensor networks, in: DCOSS,  Whitehouse, K., Zhao, F., Liu, J.: Semantic streams: A framework for composable semantic interpretation of sensor data. In: EWSN’06. (2006)  Christian Bizer, Tom Heath, Tim Berners-Lee: Linked Data - The Story So Far. Int. J. Semantic Web Inf. Syst. 5(3): 1-22 (2009) 7