###### Abstract  
Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs. I 2024 24 243 2024 24 243 2024 24 erratic radio signals, constrained bandwidth, low transmission rate, sluggish propagation speed, inborn noise, node mobility, lesser resources, and restricted battery capacity present obstacles for USSNs. Channel modeling, optimal routing, security, privacy, communication overhead, congestion control, packet error rate, packet latency, energy consumption, and other problems are brought on by these difficulties [4; 5]. By making such information systems' design phase less complex, this article seeks to address some of the aforementioned issues, such as the complexity of modeling USSNs. Thus, we can conclude that the USSN deployment phase is different from the non-underwater phase since during this phase the experts have to take into account certain underwater environmental constraints. In general, an environmental constraint is a restriction on a physical (human/device such as sensor) object's ability to act, perform, construct, or do anything else. In relation to USSNs, environmental constraints that need to be taken into account include the salinity and depth at which an underwater sensor should be positioned for optimal performance. Failure to do so could have a negative impact on the performance of the entire SSN and result in the improper underwater deployment of all SSN components, including servers, sensors, cables, and other physical and telecommunication components [6; 7]. USSNs are composed of artificial intelligence (AI) and several specialized sensors [8]. This demonstrates how Smart Sensors are connected to various Smart Fusion Servers with AI Databases [9] through the use of Distributed Fusion Architecture (Figure 1) [10] to carry out multiple tasks like collecting, processing, and storing data continuously and permanently from various sensors dispersed throughout various locations within the same network.