## 2 Existing Studies and Challenges in Underwater Wireless Sensor Networks (UWSNs)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SnNo.**} & **Routing Schemes** & **Multi-Channel Channel** & **Static-** & **Architecture** & **Channel Sensing** & **Dynamic Channel** & **Channel Capacity** & **Packet Delivery** & **Energy Consumption** & **Throughput** & **Congestion** & **Packet Error Rates** \\ \hline
1 & MCUW [17] & ✓ & Flat & & & & & ✓ & & & & \\
2 & DSDR [12] & ✓ & Flat & & & & ✓ & ✓ & & & & \\
3 & AEDG [18] & ✓ & Tree & & & ✓ & ✓ & & & & \\
4 & CAKP [19] & ✓ & Flat & & & ✓ & ✓ & & & & \\
5 & ERCGS [20] & ✓ & Clustering & & ✓ & ✓ & ✓ & & & & \\
6 & GDER [13] & ✓ & Flat & & & ✓ & ✓ & & & & \\
7 & HyMcSat & & & & & ✓ & ✓ & & & & \\
15 & ✓ & Flat & & & ✓ & ✓ & ✓ & & & & \\
8 & LKP [21] & ✓ & Clustering & & ✓ & ✓ & ✓ & ✓ & & & \\
9 & ENQR [23] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
10 & AREP [23] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
11 & E-RDCP & ✓ & Clustering & & ✓ & ✓ & & & & \\
12 & EDVO [14] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
13 & GERF [24] & ✓ & Clustering & & ✓ & ✓ & ✓ & & & \\
14 & E-FBR [21] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
15 & MFP [30] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
16 & SDCS [25] & ✓ & Hybrid & & ✓ & ✓ & ✓ & & & \\
17 & RECRF [26] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
18 & CACR [21] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
19 & DVOR [16] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
20 & DGER [22] & ✓ & Flat & & & ✓ & ✓ & ✓ & & \\
21 & RACA [31] & ✓ & Flat & & ✓ & ✓ & ✓ & & & \\
22 & MERP [10] & ✓ & Clustering & & ✓ & ✓ & ✓ & ✓ & & \\
23 & QSRP & ✓ & & Flat & ✓ & ✓ & ✓ & ✓ & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of routing schemes in underwater wireless sensor networks (UWSNs). The main purpose of the aforementioned routing protocols is to provide reliable data delivery at a low cost in UWSNs. These static-channel based schemes significantly help in designing and development of advanced routing solutions for UWSNs. However, they suffer from major disadvantages. First, most of the schemes employ probabilistic values to estimate the link quality between ASNs in highly dynamic UWEs. Thus, the poor link quality due to the false estimation of the channel is facing severe reliability issues lead to excessive rerouting during monitoring time-critical events in UWSNs. The new routes finding or repairing broken links require a significant amount of control message overheads, which consumes the sensor's energy and increases the chance of packets collision and brings latency issues in the network. On the other hand, the interference issues further increase the corrupted data packets in UWSNs. Second, during conveying packets these schemes consider shortest paths routing with excessive hop counts, which may balance the network residual energy; however, it consumes more ASNs energy and increases the probability of invalid data packets because of data path loops in UWSNs. In addition, the shortest path routing result in high routing table management cost and congestion problems because of quickly draining the residual energy frequently used by ASNs nearer to the sink. Third, most of these routing protocols are stuck in void regions and instead of finding an alternative route just discards the packets and thus subjected to packet loss in UWSNs. Fourth, the existing static-channel based routing solutions due to lack of dynamic channel adaptation cannot mitigate the inference effects in order to provide high packet delivery rates and network throughput with low latency and corrupted packets in UWSNs. These facts motivate researchers to propose a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs to mitigate the effects of the underwater environments and improve the overall data collection in UWSNs. The entire routing problem has been modelled using mixed integer linear programming (MILP) in UWSNs.