Towards the Representation of Network Assets in Health Care Environments Using Ontologies.

OBJECTIVES: The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies. METHODS: Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network. RESULTS: As a result of this work, the "Software Defined Networking Description Language-CUREX Asset Discovery Tool Ontology" (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc. CONCLUSION: The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.

Indoor Temperature Prediction in an IoT Scenario.

One of the hottest topics being researched in the field of IoT relates to making connected devices smarter, by locally computing relevant information and integrating data coming from other sensors through a local network. Such works are still in their early stages either by lack of access to data or, on the other hand, by the lack of simple test cases with a clear added value. This contribution aims at shading some light on how knowledge can be obtained, using a simple use case. It focuses on the feasibility of having a home refrigerator performing temperature forecasts, using information provided by both internal and external sensors. The problem is reviewed for both its potential applications and to compare the use of different algorithms, from simple linear correlations to ARIMA models. We analyse the precision and computational cost using real data from a refrigerator. Results indicate that small average errors, down to ≈0.09 ∘ C, can be obtained. Lastly, it is devised how can the scenario be improved, and, most importantly, how this work can be extended in the future.