## 4 Concerns about and Encounters with Underwater Wireless Sensor Network
### Underwater Acoustic Communications Characteristics
#### 4.3.4 Propagation Delay Models  
A UWSN node must simulate the transmission of the acoustic wave in order to send data to another node. A wide range of models, from the most straightforward ones founded on the theory of wide circulation to additional intricate and composite ones that established the physics of audio sound transmission, are published in the literature [47; 48; 49; 50]. In this section, we will discuss many auditory broadcast replicas that, although offering different degrees of complexity and accuracy, reflect various approaches to the same problem. So that we are aware of the parameters that are taken into account for each approach's prediction of propagation acoustics, we will list them in descending order of complexity. The Monterey-Miami Parabolic Equation model is based on Fourier analysis. The prediction of an underwater sound transmission using a parabolic equivalence is given by the Helmholtz wave equation. The little, broadminded changes in the range and depth of the sound pressure are arranged in a grid. It makes approximations of unpredictability and wave motion, employing a vigorous transmission loss computation [51; 52; 53; 54; 55]. The writers show how even minor variations in penetration and node distance can significantly impact route loss due to the influence of ocean wave motions on audio propagation. \[pl\ (t)=A=\pi r^{2}+w(t)+e(0) \tag{10}\]  
where \(pl(t)\) is the propagation loss when sending data from node \(A\) to node \(B\). When using MMPE data for regression, the expression m (0) represents propagation loss devoid of random and periodic components. F is the frequency of the audio waves being broadcast (in kHz). dA is the depth of the sender (meters). The receiver complexity in dB is in meters. In the MMPE model, the range refers to the parallel coldness among A and B bulges (mts). S is the Euclidean distance separating the nodes (mts). w(t) is the periodic function to simulate the wave motion-induced signal loss. e() is the random noise or error-related signal loss. \[\mathrm{m}\big{(}\mathrm{fs},\;\mathrm{d}_{\mathrm{a}},\mathrm{d}_{\mathrm{p}} \big{)}=\log\Big{(}\Big{|}\frac{\mathrm{s}}{0.914}\Big{(}(\mathrm{d}_{\mathrm{ a}}-\mathrm{d}_{\mathrm{b}})2^{0}\Big{)}\mathrm{a}^{0}\mathrm{a}^{10}\mathrm{S}\; \mathrm{a}^{7}(\mathrm{d}_{\mathrm{a}})\mathrm{a}^{9}\;\Big{|}\Big{)}(\mathrm{s} ^{*}\mathrm{d}_{\mathrm{b}})10^{\mathrm{e}5} \tag{11}\]  
\[\hat{F}^{2}=\frac{\mathrm{a}1}{1+\hat{F}^{2}}+\frac{40}{4100+\hat{F}^{2}}+ \Big{(}0.00275+0.003*\Big{(}\frac{\mathrm{s}}{914}\Big{)}+\;\mathrm{a}^{6}* \mathrm{d}_{\mathrm{b}}+\mathrm{a}^{8}*\mathrm{s}\Big{)} \tag{12}\]  
The w() function contemplates the velocity of an element, which will vacillate sinusoidally all over the place. The circular oscillations that get smaller in radius as the particle's depth increases serve to represent this motion. The wave energy has an impact on the radius's size, which is proportional to the wave height. The common waves can have an impact on things up to 50 m below the surface and have a wavelength of hundreds of meters [56,57]. We shall take into account the following while calculating the effects of the waves:  
\[\mathrm{w(t)=h(l_{w},\;d_{b\mathrm{t}},\;h_{w},\;T_{w})E(t,\;T_{w})} \tag{13}\]  
where the lost signal by the movement of the waves is estimated using the periodic function w(t). h (meters) is the lw of an ocean wave and a function for scaling factors. dB stands for the complexity of the telephone node. The upsurge stature in HW is in meters. T is the wavelength (seconds). E() is the node's wave consequence function. This function includes the components that resemble the movement of a node, first calculating the scale factor h() and then the wave effect at a specific point in the movement.