A context-aware and workflow-based framework for pervasive environments

Date

2019-01-01

Author

Avenoglu, Bilgin
Eren, Pekin Erhan

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Using currently available infrastructure in pervasive environments it is possible to provide intelligent mechanisms that offer people help and guidance for organizing their daily activities. In this study, a framework providing such capabilities is proposed. This framework allows users to model their daily activities in the form of workflows, which are adaptable at run-time according to context information collected in pervasive environments. A workflow engine is used for modelling and management of workflows, while a separate rule engine with complex event processing (CEP) capability is incorporated into the framework for enhancing workflow adaptation and execution. The adaptation model in the framework allows for the modelling of activities in a hierarchical manner, from high level abstract activities to more detailed ones. An event-driven architecture (EDA) is utilized for loosely coupled interaction between the workflow engine and the rule engine, allowing these engines and other context sources to exchange data among themselves. Moreover, the EDA allows incorporation of context information into the workflow models without modifying the workflow language. A level of automation higher than the level supported by workflows is proposed by processing events in pervasive environments using CEP. A prototype implementation is developed and the framework is evaluated with some real life examples that demonstrate its applicability.

Subject Keywords

Workflows, Context-aware systems, Pervasive computing, Complex event processing, Workflow adaptation, Event-driven architecture

URI

https://hdl.handle.net/11511/31456

Journal

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING

DOI

https://doi.org/10.1007/s12652-017-0633-y

Collections

Graduate School of Informatics, Article

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Citation Formats

B. Avenoglu and P. E. Eren, “A context-aware and workflow-based framework for pervasive environments,” JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, pp. 215–237, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31456.