Making heterogeneous smart home data interoperable with the SAREF ontology

SAREF is an ontology created to enable interoperability between smart devices, but there is a lack in the literature of practical examples to implement SAREF in real applications. We validate the practical implementation of SAREF through two approaches. We first examine two methods to map the IoT data available in a smart home into linked data using SAREF: (1) by creating a template-based mapping to describe how SAREF can be used and (2) by using a mapping language to demonstrate it can be simple to map, while still using SAREF. The second approach demonstrates the communication capabilities of IoT devices when they share knowledge represented using SAREF and describes how SAREF enables interoperability between different devices. The two approaches demonstrate that all the information from various data sets of smart devices can successfully be transformed into the SAREF ontology and how SAREF can be applied in a concrete interoperability framework

Validating SAREF in a Smart Home Environment

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

Using Knowledge Graphs for Machine Learning in Smart Home Forecasters

Internet of Things (IoT) brings together heterogeneous data from smart devices in smart homes. Smart devices operate within different platforms, but ontologies can be used to create a common middle ground that allows communications between these smart devices outside of those platforms. The data communicated by the smart devices can be used to train the prediction algorithms used in forecasters. This research will first focus on the creation of a mapping to transform IoT data into a knowledge graph than can be used in the common middle ground and investigate the effect of using that IoT knowledge graph data as input for prediction algorithms. Experiments to determine the impact of incorporating other related information in the training of the prediction algorithms will be performed by using external datasources that can be linked to the knowledge graph and by using federated learning over IoT data from other smart homes. Initial results on the transformation mapping of IoT data to an ontology is presented