# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 2 Related Work  
In this section, an overview of existing techniques is given. The types of routing schemes used in UWSNs are depth-based routing, probabilistic routing, data gathering schemes using AUVs, _etc._ Many techniques introduced CNs in depth-based routing to minimize the load on forwarding nodes. [PERSON] and [PERSON] in [6] investigate UWSN localization-free routing schemes that minimize energy consumption. In this scheme, nodes at a high depth forward their data to nodes that are at a low depth. During the data forwarding process, it considers the residual energy of the node as a routing metric. Nodes with a high depth only forward their data to low depth nodes that have residual energy greater than a threshold value. [PERSON] _et al._[7] proposed depth-based routing schemes; they empowered them through a delay-efficient priority factor and holding time (also delay sensitive). This scheme targets minimizing end-to-end delay at the cost of the decreased throughput of the network. They addressed delay sensitivity through choosing optimal data forwarders in a low depth region. Furthermore, to avoid the energy holes and data loss, they selected those nodes as forwarders that had a high number of neighboring nodes. They also introduced the adaptive mobility of CNs. This scheme is localization free. The authors proposed a directional flooding-based routing protocol in [8]. This scheme checks the link quality of nodes that are participating in the flooding. The number of nodes participating in flooding is controlled to avoid packet flooding in the whole field. If there are flooding nodes with poor link quality, then directional flooding-based routing (DFR) allows more nodes to forward data. This is done for achieving reliable data delivery. On the other hand, a few nodes are enough to forward the data. The authors proposed a scheme for an AUV-aided underwater routing protocol (AURP) in [3]; it uses the controlled mobility of multiple AUVs, as well as heterogeneous acoustic communication channels. The role of relay nodes is replaced by AUVs; they collect data from gateway nodes and send these to the sink. In this way, the data transmissions are minimized. AURP used the controlled mobility of AUVs, which results in short-range high data rates. By simulation, AURP shows an improved delivery ratio and minimizes the energy consumption of nodes. Mobicast [9] addressed the energy problem in the underwater environment for achieving maximum throughput. Mobicast considered a network that is divided into three-dimensional zones. Data gathering paths are predefined; the AUV moves in the zones for receiving data from the nodes. There are two phases of the protocol: the first is data collection within the three-dimensional zone; the second phase is awaking nodes in the next zone where the AUV is heading for data collection. When the AUV enters the next three-dimensional zone, the sensors go into active mode and deliver the sensed data. Due to the division of the field into different zones and introducing sleep/awake modes, this protocol minimizes the energy consumption of the nodes. This leads to the maximized throughput of the system. Underwater deployed nodes have movement due to water currents, which affect the energy efficiency of the network. In the underwater environment, the water medium causes long propagation delays; to avoid these, multi-hop transmission is preferred. While relaying the data of far away nodes, the nodes closer to the sink have a greater burden and dissipate energy early; however, the overall energy consumption is minimized. In the adaptive power controlled routing protocol (APCR), in [10], the authors jointly addressed the mobility of both the surface sink and nodes. Furthermore, the coverage is through the power control capabilities. The authors presented a scheme of an adaptive sink redeployment that reduces the total energy consumption in the APCR. This strategy does not compromise the average end-to-end delay and delivery ratio. In [11], the authors analyzed a heterogeneous underwater network.