## 4 Results: State-of-the-Art Approaches  
### Semantic Modeling in IoUT  
#### 4.1.1 Semantic Modeling and Interoperability  
In the work of [PERSON] et al. [59], a framework is proposed for supporting the idea that heterogeneous data should be processed by systems using different protocols, and these data should be finally available and accessible, to be also reviewable, by a higher-level decision-making agent. The proposed method uses a pool of hierarchical ontologies for the representation of the knowledge extracted from the experts (human) and from the processed sensor data. Moreover, this is the first approach to a specific goal-based autonomous planning using a semantic representation approach. Authors implement the concept of situational awareness (SA) of the vehicle, allowing the perception of the big picture by reusing existing ontology. The approach re-engineers the core ontology into a Status Monitor Application Ontology to represent SA, and into a Mission Planning ontology to integrate an adaptive goal-based decision-making process. The proposed framework has been evaluated against the problem of fault-tolerant adaptive mission planning. Based on the combination of two techniques, i.e., ontological representation of knowledge and adaptive mission planning repair techniques, their benefits are highlighted by showing the interlinking and interoperation of the two ontologies within the testing context. A common information model is the key for cooperation of UUVs. However, uncertainty of events that may occur during an underwater operation is very high and this leads to exchange of information with doubtful meaning between UUVs. In the work of [PERSON] et al. [59], the SWARMs ontology is presented, which provides uncertainty reasoning and probability annotation by utilizing ontology constructs defined in the PR-OWL ([[https://www.pr-owl.org/](https://www.pr-owl.org/)]([https://www.pr-owl.org/](https://www.pr-owl.org/)), accessed on 20 November 2022) ontology. SWARMs separate mission and planning modeling in two levels. The first level, high-level planning, support the ease of its handling, by allowing the user to command different tasks to each vehicle, without the need to specify exact actions. The second level, low-level-planning, regards tasks performed at the vehicle level (generation of waypoint, etc.) and also provides multiple surface and underwater unmanned vehicles, and combines networking and communication domains. By incorporating Protege (OWL ontology) and SWRL rules to express complex rule formations and relations, they experimented in a chemical pollution scenario, in which they estimated the emergency level of the polluted region. SWARM, by including several domain-specific ontologies, confronted mainly the uncertainty of the underwater environment; however, its implementation in heterogeneous devices has not yet been accomplished. In the work of [PERSON] et al. [60], an ontology-based marine environment situation awareness information modelling is proposed. Due to the difficulties of predictinguncertain events (target threat, cyber threat, abnormal thruster, water leakage) that may occur during an underwater operation, a core ontology of UUV is represented, in which uncertain events are triggered based on Semantic Web Rule Language (SWRL) rules and Resource Description Framework (RDF). Moreover, as uncertain factors and uncertain knowledge lead to probabilistic knowledge, a probability expansion of OWL language is required. Thus, UUVs are provided with important data for rapid decision-making when an uncertain event occurs. Nevertheless, since ontology cannot reason about uncertainty, it is transformed into a Bayesian network. Simulation experiments verify the effectiveness and accuracy of the situation awareness reasoning method that combines the ontology and the Bayesian network. [PERSON] et al.