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SAREF is an ontology created to enable interoperability between smart devices. While the IoT community has shown interest and understanding of SAREF as a means for interoperability, there is a lack in the literature of practical examples to implement SAREF in real applications. In order to validate the practical implementation of SAREF we perform two experiments. First we map IoT data available in a smart home into RDF using SAREF. In the second part of the paper an IoT environment is created by using the Knowledge Engine, a framework created to allow communication between smart devices, operating on Raspberry Pi’s emulating IoT devices, where the communication of the IoT devices is performed by sharing knowledge represented with SAREF. These experiments demonstrate that SAREF is an ontology that is successfully applicable in different situations, with data-mapping showing that SAREF is able to represent the information of different smart devices and by using the Knowledge Engine showing that SAREF can enable interoperability between smart devices.

Most reported MDA approaches give much attention to structural aspects in PSMs and in generated code, and less attention to the PIM level and the behaviour of the modelled applications. Consequently, application behaviour is generally not (well) defined at the PIM level. This paper presents an MDA-based approach that incorporates behaviour modelling at the PIM level in the development of a specific category of applications, i.e., context-aware mobile applications. The paper also illustrates a behaviour model transformation realized by using Medini QVT , which is a tool that implements the Query/ View/ Transformation (QVT) Relations specification defined by OMG for model-to-model transformations.

This research was performed in the GOOSE project, which is jointly funded by the MIST research program of the Dutch Ministry of Defense and the AMSN enabling technology program., The number of networked cameras is growing exponentially. Multiple applications in different domains result in an increasing need to search semantically over video sensor data. In this paper, we present the GOOSE demonstrator, which is a real-time general-purpose search engine that allows users to pose natural language queries to retrieve corresponding images. Top-down, this demonstrator interprets queries, which are presented as an intuitive graph to collect user feedback. Bottomup, the system automatically recognizes and localizes concepts in images and it can incrementally learn novel concepts. A smart ranking combines both and allows effective retrieval of relevant images., peer-reviewed