# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 3 Motivation and Contributions  
Designing an underwater network architecture is a hard task due to the drastic underwater conditions. The communication model used underwater is different compared to the TWSN as given in Table 1. We proposed a scheme, 3D-SM, which has better NLT and throughput. Nodes deployed in the field transmit their sensed data to the MS as these come in its transmission range. We compare it to the existing protocol DDRP [13], which is two-dimensional with the radio model for transmissions. For a fair comparison, we convert DDRP into a three-dimensional underwater environment and applied the acoustic model for transmissions. The mobility pattern of the MS is random in DDRP, and there is no restriction that it visits all of the regions for data gathering. Due to the random mobility, it may visit a single region more than once while leaving other regions unattended, because the round trip time and number of visits are fixed. In AUV-PN [5], the AUV visits pre-identified locations for gathering data. The AUV travels in the network where nodes are deployed, partitions the network into clusters and provides the partition information to nodes through the control packet. Nodes in each cluster select the CH, then the CH further divides the cluster into sub-clusters. The CH nominates a PN for each sub-cluster for receiving data from the MNs. The AUV then starts its tour to receive data and acquires the list of PNs from the CHs to collect data. In Figure 1, a comparison of three schemes (two existing and one proposed) is given. From the figure, it is obvious that existing schemes have multi-hoping and route finding, which consume energy.  
In these routing protocols, DDRP has a coverage problem: all of the nodes are not able to send their data to the MS, as it has a random trajectory. Furthermore, it is using direct communication, and the nodes have to wait for their turn. AUV-PN has sink mobility, as well as clustering. Nodes have limited battery power, and for prolonging the network lifetime, balanced energy consumption is required. Nodes consume energy in cluster formation and PN selection. Furthermore, when the PN forwards the data gathered from the MN, it consumes more energy. This leads to quick energy depletion.  
We have addressed the following challenges in underwater acoustic networks.  
* Network energy consumption minimization.
* End-to-end delay minimization.  
In the proposed scheme, nodes directly transmit data to the MS and do not relay the data of far away nodes. The scheme is designed in such a way that each node gets a chance to deliver the sensed data. In this way, nodes save their energy, which leads to prolonged NLT. To cope with the limitationsof underwater transmissions, the MS gathers data from the RC where it is deployed. CNs collect data from the remaining RCs that are not considered to be in the trajectory of the MS.  
Let us consider three different cases: (i) the network field is not logically divided; (ii) the network field is divided into two logical sub-regions; and (iii) the network field is logically divided into four sub-regions. The trajectories of the MS(s)/CN(s) are kept the same for all of the three cases. As the next step, we test the three mentioned cases via simulations; results are shown in Figure 2. From this figure, it is clear that the network lifetime increases as the number of logically-divided sub-areas is increased. However, the installation cost of the MS/CN prohibits further division (one AUV costs approximately 50,000 USD [14]). From our discussion till now, the solution seems to be static. Rather than static, our solution is adaptive, _i.e._, we use the following formula for finding the number of region \"_RCs_\".  
\[RCs=\lceil\frac{x+y+x}{2n}\rceil\]  
where \(x\), \(y\) and \(z\) are the corresponding network field coordinates and \(n\) is the number of nodes.  
Figure 1: Comparison of the proposed and the counter schemes.  
Figure 2: Impact of sub-regions on NLT.