# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 2 Related Work
### Localization-Free Routing Protocols
The Cross-layer Mobile Data gathering (CLMD) [32] protocol is also proposed to improve the network performance by creating interaction between MAC layer and routing layers. An Autonomous Underwater Vehicle (AUV) is used to visit each cluster collecting data from the neighborhood. The communications are managed through a distributed cross-layer solution. To prolong the network lifetime, the cluster heads are replaced with other cluster heads at the end of each operational phase. This protocol outperforms in terms of energy optimization, network lifetime, and PDR of the network. Similarly, [PERSON] et al. [33] presented an Energy-Efficient Regional Base Cooperative Routing protocol (EERBCR) with sink mobility by dividing the network field into 12 regions. Four mobile sinks are deployed at equal distance in the randomly deployed 100 nodes. Each sink travels on a predefined route to cover all 3 regions while all other nodes are in sleep mode. A sink enters the region and broadcasts a hello message in its range to activate the sleeping nodes. At the time of departure, it leaves another message to put them back to an indolent state. This protocol performs well in terms of energy optimization and prolonging the network lifetime. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol Category** & **Protocol** & **Advantages** & **Drawbacks** \\ \hline \multirow{5}{*}{VHF [4]} & \multirow{2}{*}{Limiting the direction and} & Cause duplicate packets in \\  & & forwarding range & dense and void holes in \\  & & & sparse network \\ \hline \multirow{5}{*}{HH-VBF [5]} & \multirow{2}{*}{Improved PDR} & Fails to provide energy \\  & & & fairness and void holes \\  & & & avoidance \\ \hline \multirow{5}{*}{Localization-Based Routing} & Energy efficiency is & \multirow{2}{*}{Imbalanced energy} \\  & achieved due to power & & consumption \\  & & adjustment & \\ \cline{1-1} \cline{2-3}  & AVN-AHHVBF [7] & Energy efficiency & High packet loss \\ \cline{1-1} \cline{2-3}  & ESEVBF [8] & PDR and energy & \\  & optimization & & Fails in sparse network \\ \cline{1-1} \cline{2-3}  & NEFP [9] & Energy efficiency & does not perform well in \\  & & & sparse network \\ \hline \multirow{5}{*}{MEEES [11]} & Energy efficiency & Network throughput is lower \\  & & & for fewer nodes \\ \cline{1-1} \cline{2-3}  & MEES [12] & Energy optimization, & packets are dropped by nodes \\  & & energy balancing & for out-of-range sinks \\ \hline \multirow{5}{*}{UTMR [12]} & Network throughput is & Unreliable for \\  & higher, lower delay & direct transmission \\ \cline{1-1} \cline{2-3}  & FVBF [13] & Energy optimization, & Lower network lifetime due \\  & delay tolerant and & to rapid dying of nodes \\  & & higher throughput & in the pipe \\ \hline \multirow{5}{*}{DBR [14]} & Increase in PDR in & Bad performance in \\  & dense network & sparse network \\ \cline{1-1} \cline{2-3}  & DSRP [15] & High throughput & High energy consumption \\  & & and end-to-end delay \\ \cline{1-1} \cline{2-3}  & ODBR [16] & prolong network life, & Not good for in-depth water \\  & & energy balancing & regions as the nodes located \\ \cline{1-1} \cline{2-3}  & & & at the bottom needs higher \\ \cline{1-1} \cline{2-3}  & EBECRP [17] & Energy efficiency and & Higher packet drop ratio \\  & network balancing & because of the cluster heads & mobility or death \\ \hline \multirow{5}{*}{Hydrocast [18]} & \multirow{2}{*}{Energy efficiency} & Does not perform well in \\  & & sparse network, increased \\ \cline{1-1}  & & load because of using \\ \cline{1-1} \cline{2-3}  & & opportunistic routing \\ \hline \multirow{2}{*}{WDFAD-DBR [19]} & Increase in network & energy consumption E2 ED \\ \cline{1-1}  & lifetime and decreased & is increased \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing UWSNs protocols. In addition to the above studies, some recent studies in UWSNs include a comprehensive survey [34] describing the requirements, taxonomy, recent advances, and open research challenges in the field of Underwater Sensor Networks.