# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scheme** & **Features** & **Performance Achieved** & **Cost Paid** \\ \hline
**AURP**[3] & Heterogeneous acoustic channel where multiple AUVs are gathering data from nodes through gateway nodes. & Maximized delivery ratio, minimized energy consumption. & Increased delay. \\ \hline
**Data collection**[5] & The AUV collects data from the nodes. The network is logically divided into four sub-regions, and each region selects a CH. Each sub-region is further divided into clusters, and each cluster selects a PN, which collects the data from MNs and forwards these to the AUV. & Minimized total energy consumption, maximized throughput and minimized overhead. & End-to-end delay increases. \\ \hline
**Delay-sensitive routing schemes for underwater acoustic sensor networks**[7] & Depth-based localization-free routing. Energy consumption is balanced. Sender-based approach, where the sender selects a limited number of suitable forwarding nodes. & Minimized total energy consumption, minimized average end-to-end delay and minimized transmission loss. & Decreased delivery ratio. \\ \hline
**Energy deficit length based routing (EDBR)**[6] & The growth-based localization-free routing. Energy consumption is balanced. Sender-based approach, where the sender selects a limited number of suitable forwarding nodes. & Extended NLT, minimized energy consumption and minimized end-to-end delay. & Decreased delivery ratio. \\ \hline
**DFR**[8] & Nodes are location and neighbor aware. Replaces the forwarder in the case of a weak link. Forwarding activity is performed hop by hop. Delivers packets through flooding. & Increased packet delivery ratio and less communication overhead. & Increased delay. \\ \hline
**Mobicast**[9] & Nodes’ positions can be changed due to water currents. The AUV moves on a user-defined trajectory and collects data from 3D zones. Nodes record their location in a specific time period to calculate the drift and also observe the sleep/awake mechanism. Capable of covering the drift distance of a node. & Minimized delay, increased throughput and increased successful delivery rate. & Increased message overhead and increased power consumption. \\ \hline
**Adaptive surface sink replacement strategy**[10] & The surface sink is capable of self-configuration and has no energy constraint. The sink updates the table of node IDs and their energies, and the next time, when receiving data, it compares to the existing record either the same node or it changes. If the nodes’ power level is changed, then the sink will change its position in the \((x,y)\) plane to reduce the distance between nodes to minimize the energy consumption. & Minimizes energy consumption and end-to-end delay. & Decreased throughput. \\ \hline
**Remote retrieval strategy**[11] & Head nodes receive data from the neighbor nodes and forward these to the AUV in a grid topology. & Increased packet delivery ratio, decreased end-to-end delay and minimized energy consumption. & Increased packet delivery ratio, decreased end-to-end delay. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of existing protocols. AURP, AUV-aided underwater routing protocol; PN, path node; MN, member node; NLT, network lifetime; DFR, directional flooding-based routing. [PERSON] _et al._[PERSON] _et al._ (2016) presented an analysis of energy consumption in shallow and deep water. Moreover, they applied three different communication types to analyze energy consumption. These three types are: relaying, direct transmission and clustering. The results show that direct communication in the underwater environment performs poorly, because when the distance increases between the node and sink, the acoustic interference causes packet drops. This results in the low throughput of the network. The relaying scheme performs well in deep water, as it minimizes the overall network energy consumption; however, it increases the network complexity.