## 5 Simulation and Discussion  
Monitoring aquatic environmental factors in real time is crucial. So, underwater wireless sensor network integration with positioning systems under IoT technology has the potential to transform the real-time monitoring of underwater ecosystems. In the implementation of the proposed network assumptions, the International Resource Identifiers System (IRIS) requirements supported by the communication modem M64a transceiver, ESP8266-01 Wi-Fi module, and positioning system Underwater GPS G2 (Global Position System Generation 2) were used. The M64a transceiver is designed to operate in low-power, low-bitrate, and long-range modes. M64a provides a dependable acoustic communication link between two subsea locations with limited space, weight, or power. Data are exchanged at 64 bps across a robust two-way half-duplex acoustic link that is employed to assure data transfer reliability, and the GPS G2 gives a single locator with reliable, accurate, and robust acoustic locating. To execute the proposed network assumptions, the constructed network involved (\(N\)) sensor nodes distributed in clusters. Each cluster in the manufactured network uses GA to select the head node. In the evolutionary algorithm, each node in the cluster sends enclosed signals about its energy and geographic region to a hub. The planned network has (N) sensor nodes that are distributed in clusters. Each cluster in the manufactured network uses GA to select the head node. In every deploying round, the elected node receives a hello message from the base station, which allows it to declare itself as a cluster head (CH) to the other cluster members. In the algorithm's implementation, any sensor node can choose to be a cluster head if it has not received an announcement message from the head. In this situation, this node sends out an announcement about the head selection to the cluster nodes to promote itself as the group leader. Cluster members are sent a join message to the node providing the announcement, which announces their connection with the cluster. Each GA chromosome serves as a designating map for the cluster leaders. A gene on a chromosome controls whether the appropriate node appears as the group's leader. Cluster members are sent a join message to the node providing the announcement, which announces their connection with the cluster. Each GA chromosome serves as a designating map for the cluster leaders. A gene in a chromosome controls whether the relevant node appears as the group's leader. The closest neighbour rule is then used to build the cluster's nodes, and the importance of a chromosomal WSN structure is assessed by analyzing all groups. During the steady-state phase, the nodes in each cluster can begin sensing information and communicating it to their cluster head for the predetermined transmission time. Because the cluster head (CH) is typically located a long distance from the base station (BS), communicating with it requires a significant amount of energy; therefore, the head must be elected each cycle. Following direct data sensing, each sensor node broadcasts the individual data to surrounding nodes as well as the cluster head across a specified period. Implementing sleep modes for sensors can significantly reduce energy consumption. In each cycle, every node in the cluster determines the energy consumption using the following equation:  
\[E_{OVERALL}=\text{E}_{I}-\sum E_{IDLE},E_{SLEEP},E_{TRANS}E_{REC} \tag{15}\]  
Table 2 displays the simulation results for embedded underwater sensors. As a feature, we selected pH (the potential of hydrogen), Ammonia (NH\({}_{3}\)), temperature, turbidity, and fish as the goal attributes. The practical results are divided into two sections, sensory filtration and prediction learning. In our investigation, we used pH, temperature, and turbidity as aquatic environment factors. It created an IoT platform for sensor-based real-time aquatic environment monitoring.