Generic Adaptive Middleware for Behavior-driven Autonomous Services

Universität Duisburg-Essen ETRA Investigación y Desarrollo, S. A.National University of Ireland, Galway The Open UniversitySpeechConcepts GmbH & Co. KG Empresa 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

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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.

Generic Adaptive Middleware for Behavior-driven Autonomous Services

GAMBAS Scenario

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GAMBAS Middleware

Third-party Internet Services

Public Transport

Exploitation System

User Context Information

Public Transport Sensors and

Actuators

. . . . . .

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

GAMBAS approach towards 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

GAMBAS approach towards 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

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, 2008.

E. Bouillet, M. Feblowitz, Z. Liu, A. Ranganathan, A. Riabov, F. Ye, A semantics-based middleware for utilizing heterogeneous sensor networks, in: DCOSS, 2007.

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)

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