## 4 Results: State-of-the-Art Approaches
### Semantic Modeling in IoUT
#### 4.1.1 Semantic Modeling and Interoperability
[61], in order to bridge the gap between heterogeneity and manageability of huge number of sensors, proposed an ontological approach by integrating the concept of large-scale service-oriented architecture (SOA) (LSS). Suitable for dynamic environments such as IoUT, this approach extends the existing SSN ([[https://www.w3.org/TR/vocab-ssn/](https://www.w3.org/TR/vocab-ssn/)]([https://www.w3.org/TR/vocab-ssn/](https://www.w3.org/TR/vocab-ssn/)), accessed on 20 November 2022) ontology by the concept of a large-service domain. By using new properties, this extension provides the ability to detect available or unavailable devices, but also the geographical coordinates, date, and time of the results. The extracted ontological framework is referred to an IoT service-based environment to efficiently use service composition mechanisms in the large service domain. Being reusable and integration-friendly with other ontologies, this ontology concept is easily implemented in IoUT. Adaptive planning, a main capability of UUVs, is dependent on a key factor, i.e., weather conditions. Extracting accurate information about weather conditions at the exact location of an operation and distributing these data among underwater assets, facilitate their decision-making. An interesting ontological model to integrate meteorological measurements is presented in [62]. By linking several, mainly sensor-based, ontologies (SOSA, SSN, W3C Time Ontology, etc. ), they created a real-time dataset (temperature, wind speed, etc.) in CSV format, containing all the individuals that describe measurements of their own weather station in a farm, to support farmer activities. The transformation of these data to RDF triples and their integration, was achieved by a manuscript Python program. The main issue of this project concerns duplicate values, due to recurring information from different sensors. In IoUT, such a model could be integrated in scattered assets, in order to distribute environmental data to other underwater components. However, this demands a massive number of distinct devices, acting solely as weather forecasters, congesting even more of an underwater network architecture. A possible solution for the above issue is described in the work of [PERSON] et al. [63]. An ontology-based approach for environmental data incorporation is presented, by integrating raw data from a well-known database, which generates a massive amount of climate data in real-time from various sources, such as satellites, ships, buoys, radars, etc., named NOAA (National Oceanic and Atmospheric Administration's National Weather Service, [[https://www.noaa.gov/information-technology/open-data-dissemination](https://www.noaa.gov/information-technology/open-data-dissemination)]([https://www.noaa.gov/information-technology/open-data-dissemination](https://www.noaa.gov/information-technology/open-data-dissemination)), accessed on 20 November 2022). The proposed ontology, i.e., CA (climate analysis ontology), utilizes a SPARQL server, with the ability to store RDF triples and to perform RDF queries. It also employs NOAA CDO (Climate Data Online, [[https://www.ncei.noaa.gov/cdo-web/](https://www.ncei.noaa.gov/cdo-web/)]([https://www.ncei.noaa.gov/cdo-web/](https://www.ncei.noaa.gov/cdo-web/)), accessed on 20 November 2022), to populate CA with climate data. To map raw data to RDF triples, an open-source Python program ([[https://github.com/futaoo/codespaceRepo](https://github.com/futaoo/codespaceRepo)]([https://github.com/futaoo/codespaceRepo](https://github.com/futaoo/codespaceRepo)), accessed on 20 November 2022) was developed, based on the Python package _rdflib_ ([[https://rdflib.readthedocs.io/en/stable/](https://rdflib.readthedocs.io/en/stable/)]([https://rdflib.readthedocs.io/en/stable/](https://rdflib.readthedocs.io/en/stable/)), accessed on 20 November 2022). The experimental results with the ontological data model, data processing, ontology creation, and a query engine, prove the overall efficiency of the CA.