## 4 Results: State-of-the-Art Approaches
### Semantic Modeling in IoUT
#### 4.1.3 Data and Information Modeling for UUVs  
As already mentioned, the volume and variety of data generated from numerous and heterogeneous sensors are key challenges, in addition to the issues related to the water as a medium of transmission, and to many other cybersecurity issues that are emerging. To overcome such issues in an efficient and effective way, the key is the design of approaches followed by engineers for the data and information modeling. Information modeling mainly concerns the \"representation of concepts and the relationships, constraints, rules and operations to specify data semantics for a chosen domain of discourse\" [80]. Specifically, information modeling supports the organization by facilitating accessibility and reuse of content with various and innovative ways. The result of efficient and effective information modeling approaches is the delivery of an open, reusable, validated and evaluated information model, which is a formal description of the information needs of a group of users [81]. Thus, the modeling of information needed by users/actors/agents to interoperate in IoT environments, in which the information generated by underwater assets is vast and heterogeneous, must be taken into serious consideration. Several challenges are encountered during data modeling, and mainly concern big data (the four Vs) and its characteristics. Furthermore, data quality, information quality, and the computation complexity of vast numbers of heterogeneous data, affect enormously the results [82]. IoUT must support the sharing of information between assets in a time- and energy-efficient manner. The design of a hierarchical information system is undoubtedly essential in order to keep a smooth flow of data/information in the UWSN. Members of the Department of Electronic Engineering of Tsinghua University, Beijing, China, proposed a system for hierarchical information acquisition as well as a flowchart for the collection scheme of UUV/AUVs or other interoperating assets [83]. Thus, depending on which state of the chart the asset is at, it makes decisions for the number of channels that will be used, or decisions for the time the gathering of information should be established; this process is considering energy-efficiency as the principal factor. Simulation experiments confirmed the efficiency of the proposed algorithm. In addition, information exchange in the IoUT setting, especially in SARs and military operations, should be extremely precise and succinct, containing only the necessary information for the leader of UUV/AUV or shore platform to act promptly and coordinate the swarm efficiently. Localization is the initial task in order to further extend the capabilities of an unmanned vehicle. In related work [84], a method for localization is proposed based on error ellipse for UUVs/AUVS geometric interpretation and intuitiveness. The idea is that the leader (master UUV/AUV) carries high-precision sensors, sending highly accurate data to the rest of vehicles (slaves) in order to be able to correct their position accordingly, due to their low-precision sensors. Visually represented information is also necessary for commanders to have a picture/view of the environment where the vehicles move, but also to support the adaptive decision process of the vehicle itself. Related work presents an adaptive visual information gathering (AVIG) framework for underwater autonomous vehicles, in order to enhance their various capabilities such as positioning, navigation and object recognition [65]. The work has been motivated by the difficulties emerging in deep waters, making direct communication between an operator and an underwater robot impossible. Utilizing semantics, a convolutional neural network (CNN) for semantic image segmentation and algorithms for environmental modeling, the proposed work has been integrated in a ROS (robotic operating system) environment.