## 6 Underwater Object Localization Service Case Study Relying on Multi-Sensor Data Fusion  
The data fusion analysis theory [10] is utilized in this case study to locate things, and it primarily demonstrates how we apply our method to combine underwater sensor networks with the information system employing IP technology and IMS. We use the localization service as an illustration of one of these uses. The underwater moving object localization service seeks to pinpoint an object's location following its detection by a sensor or group of underwater sensors. For underwater applications where electromagnetic waves cannot travel great distances, acoustic hydrophones are suitable sensors. The topology of the Fusion Servers and sensor network in the context of data fusion is represented by information graphs (centralized and distributed architecture). According to [10], information graphs (Figure 1) provide \"convenient means to understand how fusion process flows impact a network system\". To offer a thorough and comprehensive picture of an environment or process of interest, data fusion [52] integrates information from sets of disparate sources. Sensors [53] are the sources of data used in the MeDON project. These data are then merged using multi-data fusion techniques, as described in [10]. The target location is determined and updated by the localization algorithms applied in the server (Figure 1) node (e.g., fusion node).  
Our newly proposed UECs, which are introduced in this article, were validated by using the most recent version of the ArchiMO design tool, which includes the UECs and the previously added MO concepts. We built a model that localizes any underwater moving object (in our case, MeDON).  
In order to validate this, the newly added UECs and their appropriate functioning are being used in the following manner while creating a MO model: upon tapping the SS icon inside the ArchiMO palette, our extended ArchiMO design tool leverages the AI Database to retrieve pertinent UEC values and the associated constraints of the selected SS, contingent upon a legitimate primary key that the designers must designate. In order to check and compare the input UEC values with the retrieved values from the AI Database, ArchiMO now asks the SSN designer to enter the correct architecture, salinity level, and  
Figure 10: Inappropriate underwater environmental constraints.  
operational depth for the intended SS. The SSN designer will now receive confirmation from the framework that he can deploy the Smart Sensor if he performs one of the following two scenarios (Figure 9): (Architecture: 3D, Salinity Level: Sea, Operable Depth: 10,920) or (Architecture: 2D, Salinity Level: Sea, Operable Depth: 110). In contrast, the SSN designer will be notified that one or more incorrect UEC prevents him from deploying the Smart Sensor (Figure 10). As a result, these constraints stop the SSN designers from entering an incorrect UEC for Fixed Nodes, SS, or SFS.  
The defined model is then used to run various error checks and automatically produce simulation code for NS-3 using a model compiler (see to Section 6.4) that we have created in [2; 3], and [21]. A standard and classical networking simulator, the NS-3 tool, is used to run this simulation code.  
The design model comprises three views pertaining to the ArchiMate layers (Figure 11): technology, application, and business. To guarantee interoperability across levels, this figure demonstrates the use of certain relationships like \"Used by\" and \"Realization\" to associate the many produced models according to the various ArchiMate layers. By utilizing the inter-relationships (shown by the red lines in Figure 11) among these layers, the various designers involved can combine their separately produced business, application, and technology models into a single, coherent MO model.