## 4 Results: State-of-the-Art Approaches
### Semantic Modeling in IoUT
#### 4.1.1 Semantic Modeling and Interoperability
Another work for semantic modeling for interoperability, following a different approach, is the one presented by [PERSON] [64]. To prevent the potential pollution of water, they propose an adaptive sampling algorithm. By using a swarm of autonomous underwater vehicles as agents, which interoperate with a surface buoy in a Multi-AgentReinforcement Learning (MARL) framework, they accomplish an autonomous decision-making loop, with energy and time efficiency. By using reinforcement learning (RL), they conduct adaptive sampling in two phases, exploration and exploitation, in a standard grid-world map. In an obstacle-free underwater/surface environment they succeeded in obtaining the parameters for water pollution in two different locations and in various depths. The issue emerged when the number of autonomous underwater vehicles was increased above 10, making it difficult to observe their behavior. Motivated by the need to reach unexplored, unsafe, and hazardous environments in deep waters, the authors of [65] present an adaptive visual information (AVIG) framework for autonomous exploration using AUVs [65], which coordinates the parallel execution of four modules: navigation, data processing, map estimating and planning. Differentiating the basic autonomous behavior of an AUV, by adding two high level modules, one for data processing and another for replanning, they used high resolution images from a bottom-looking stereo camera that used a stereo camera driver node. After applying semantic segmentation using a convolutional neural network (CNN), these images were converted into 3D coordinates. In addition, they developed a strategy for decision-time adaptive replanning; having the AUV in constant motion, neither needing to stop, nor being forced to complete the commanded mission paths, in order to replan. The developed strategy for adaptive replanning based on coordination is simulated into a node network inside of a given target area and outside of obstacle areas. Simulations have been performed using the Turbot AUV. A very crucial step to achieve secure movement and automate underwater operations of self-driving underwater vehicles is object detection and classification, as well as its avoidance, during their trajectory. This can be done either by predefined objects as obstacles in the database of the vehicle, or by real-time image processing, which is more realistic due to the dynamic environment of IoUT. Nevertheless, in a risky and unexplored environment with low visibility, as with deep-sea waters, object recognition is challenging. In related work [66], motivated by the challenges emerging under realistic underwater conditions when collecting real data from an AUV, a combination of 3D object recognition and semantics is proposed by utilizing the Bayesian estimation. Testing was conducted by a lightweight and very stable AUV (Girona 500) consisting of various sensors and systems such as the Global Positioning System (GPS) and acoustic modem. The aim of this test was to successfully explore an underwater industrial structure made of pipes and valves and label them. After successful results, the review concludes that the combination of semantics and Bayesian estimation improves further the object recognition issue. Another interesting method related to underwater object detection has been recently proposed [67]. In this work, a sonar simulator identifies the semantic information of a viewed scene, and then realistic sonar images are generated from the simulated images. This method can also work vice-versa, something very useful for exchanging information with a UUV/AUV. The procedure of this operation is relatively simple, using a sonar sensor which consists of a transmitter and a receiver. At first, the receiver measures the time-of-flight from a transmitted beam and its intensity to create the image. Next, the image is simulated by omitting physical phenomena of the medium, the water.