## 3 The Proposed Model
### System Model  
The system contains \(N\) dynamic bulges, which are sparse and arbitrarily dispersed during an \(L\times L\times L\) process. The data source is water-medium-sensed data. The data were gathered utilizing an underwater sensor. The current flow, pressure, and temperature are the identified parameters. The underwater sensor was prepared with an acoustic modem, which enables them to communicate with other nodes in the aquatic environment [42; 43; 44; 45]. An SN is equipped with both a radio frequency (RF) and an acoustic modem for communication with the base station (BS) on the surface landmass; the SN's acoustic modem receives data from underwater sensors, while the RF modem communicates with the base station (BS) by transmitting data through the base station. Despite the fact that it has a short transmission distance, the BS is capable of traversing a sensing field and collecting data from sensor nodes on the field. Each sensor node's power consumption is lowered, since fewer relays are required to transmit the sensor's message to the BS. It can be considered that the network condition is related to the networks. The topology varies rapidly due to the fact that underwater sensors are transferable, depending on water current velocities of around 1-3 m/s [46; 47; 48; 49; 50]. The network condition can be considered as follows:  
* The bulges identify their place and the place of the SN in a primary situation.
* The node can develop the \(CH\) and the CM/relay.
* The \(CH\) rotates amongst the sensors to conserve energy.  
Acoustic waves in an underwater broadcast medium have distinct characteristics from radio waves; hence, a WSN cannot be employed for underwater broadcasting networks. For the present study, we used the power consumption strategy of an underwater acoustic channel [51; 52; 53; 54; 55]. The energy required to transport \(k\) bits of information across a reserve, \(d\), at an information amount, \(R\), is computed as follows:  
\[E_{Tx}(k,d)=k\times E_{elec}+\frac{k}{R}P_{tx} \tag{1}\]  
Figure 1: Overall process of the CSRO-MHDT technique.  
where \(E_{elec}\) implies the power utilization for routing 1 bit of information and \(P_{Tx}\) stands for the transmitted power.  
In order to received \(k\) bits of information, the receiver radio power utilization is provided under the following:  
\[E_{Rx}(k)=kP_{r} \tag{2}\]  
Assume that \(P_{r}\) refers the constants dependent upon the devices. In order to fuse \(k\) bits of data, the power utilization is formulated as:  
\[E_{DA}(k)=k\times E_{DA0} \tag{3}\]  
where \(E_{DA0}\) refers the energy used for fusing 1 bit of data, for instance, in use, as 5 nJ/bit. Fusing data is a frequent and successful method for removing data redundancy, shrinking data size, and lowering energy consumption. The data fusion is implemented in this research using an upgraded back propagation neural network (BPNN). Sensor nodes in UWSNs may collect information with great dismissal. When superfluous information is directed to the SN, wasteful energy ingesting occurs, resulting in the node's premature death and the network's lifespan being shortened. In comparison, if the CHNs integrate the data and send it to the SN, then significant energy savings may be realized [57; 58]. While the node is mobile, caused by the water current, it can be located according to the random motion of the node under the functioning time. The current velocity is 1-3 \(\frac{\text{m}}{\text{s}}\).