## 7 Simulation Parameters
### Analysis of BIN-TANVEER and PL-ROBINA with Number of Dead Nodes and Packet Transmitted
\begin{table}
\begin{tabular}{c c c c} \hline
**Schemes** & **Features** & **Achieved Parameters** & **Trade-Offs** \\ \hline \multirow{6}{*}{_PSOA_[43]} & Decision feedback equalizer (DEF), ambient noise, Laplace noise, Raley Fading channel, and distribution for frequency selection in the acoustic environment is used & The computational complexity of LMS, RLS, and PSO are achieved & PSO has the highest computational complexity as compared with PSO-DEF \\ \hline \multirow{6}{*}{_Underwater OFDM Receiver_[44]} & Design the simplifier receiver for the UWA channel due to deep neural network-based orthogonal frequent division multiplexing & Thus the signal is suitable for UWA and also for other similar schemes and channels due to better bit error over traditional ones & A general receiver that is already used for other modulation schemes, not fit for water currents, particularly when the water is deep and shallow \\ \hline \multirow{6}{*}{_EACQ for Underwater_[45]} & Environment-aware communication channel quality prediction (ML-ECQP) method for UACNs is proposed & A logistic regression algorithm is used to predict the communication channel quality between the sender and receiver side & Highly energy waste caused transmission that reduces the packet transmission ratio \\ \hline \multirow{6}{*}{_OCRPC for IoUTs_[46]} & Try to evaluate the optimal transfer power node for deleting of nodes to maximize the PDR and other relevant parameters, for example, Q-Network based underwater relay section as well & Achieved equal transmit power under the same condition using Markov Model and reinforcement learning & Improve the communication strategy, but lots of iterations are needed to cover the learning & Improve the communication strategy, but lots of iterations are needed to cover the learning & maximum area \\ \hline \multirow{6}{*}{_RCACR_[47]} & Same as used reinforcement learning mechanism as per [46], the only difference is that accelerating the convergence algorithm strategy is introduced for the first time in literature in underwater settings & Due to the proposed modified MAC layer in Underwater to optimal routing decision, the optimal RCACR pour performed HHVBF, DQELR, and GEADR in terms of convergence and energy \\ \hline \multirow{6}{*}{_DQELR_[48]} & adaptive Deep & \multirow{2}{*}{Achieved less limitation in latency and increased energy efficiency with superior network lifetime} & The method of selecting Q-value is not optimized; it is proactive and does not fit in all scenarios that the case is covered \\ \cline{1-1}  & Adaptive modulation and coding mechanism is used for & Boosted regression tree and four ML algorithms are good for channel characteristics & SNR and BER constraints are rich; especially signal characteristics are used \\ \hline \multirow{6}{*}{_Fuzzy decision Making in Underwater_[50]} & Packet forwarding scheme using fuzzy logic is introduced, with RSSI indicator for adaptive and non-adaptive transmission & Several hops with fuzzy constraints are used that dynamically affect the performance of the underwater networks, especially energy tax & Hop count is an old parameter, but coins with fuzzy logics decision making structures are good to introduce acoustic communication \\ \hline \multirow{6}{*}{_Miscellaneous Underwater Routing Schemes_[51, 52, 53, 54, 55, 56]} & All are working for underwater scenarios to avoid and solve the void hole problems & Proposed atomic path, watchman-based nodes, optimal scheme, angle adjustment, diagonal and vertical routing ideas, and different comparative studies work well in this domain & All the routing schemes were well performed in and deployed in a network scenario with some trade-off’s relationships \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Figure 16: Analysis of the number of dead nodes compared with BIN-TANVEER and PL-ROBINA. Figure 17: Analysis of packet transmission compared with BIN-TANVEER and PL-ROBINA.