# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 3 Methodology
### Sink Mobility Patterns  
To ensure unbiased results towards any mobility model, the authors of this study chose to compare statics sinks outside the RoI with four random mobility models:  
1. Random WayPoint Mobility Model (RWP) [53; 54]: A model that includes pause times between changes in directions and speed. 2. Random Walk Mobility Model (RW) [53; 54]: A simple mobility model based on random directions and speeds. 3. Random Direction Mobility Model (RD) [53; 54]: A model that forces MNs to travel to the edge of the simulation area before changing direction and speed. 4. Gauss Markov Mobility Model (GM) [53; 54]: A memory model that uses one tuning parameter to vary the degree of randomness in the mobility pattern. The basis for selecting these models for evaluating network Quality of Service (QoS) metrics is that they support unpredictable and random changes, like real-time scenarios. Subsequently, we will delve into how each of these models operates. The functioning of each mobility model is described in the article [21]. Figure 1a shows an example of a traveling path of a sink, which begins in the center of the RoI, using the RW Mobility Model. At each point, the sink randomly chooses a direction between 0 and 2 and a speed between 0 and 10m/s. At every 60, the sink changesdirection and speed. This Model is a memoryless mobility pattern because it retains no knowledge concerning its past locations and speed values [14]. Figure 1b shows an example of a traveling path of a sink, which begins in the center of the RoI, using the RWP Mobility Model. The movement pattern of a sink using the RWP \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **LEACH (2000)** & **P-LEACH (2014)** & **EA-CRP (2018)** \\ \hline Key features & A uniform CH selection process in which every node gets an equal chance for a CH job & The network is divided into partition clusters and uses the prediction techniques with mobile sink tracking & The sensing area is alienated into various layers, and certain clusters are created inside each layer to minimize the communication cost. \\ \multirow{3}{*}{Aims} & Maximizing the lifetime of the network & & Reducing the energy expenditure in the network and minimizing the cost of communication between nodes \\  & & The prediction technique helps in minimizing the energy expenditure. & Reducing the energy expenditure in the network and minimizing the cost of communication between nodes \\  & & The prediction technique helps in dividing the sensing area into layers and sub-layers. & Reducing the energy expenditure. \\ \multirow{3}{*}{Strengths} & An equal chance is given to every node to become the CH, which divides the workload among all the nodes & The task of the cluster center is handled by the four gateway nodes, which improve the network lifetime & Minimizes the communication cost amid the nodes as the width of the layer decreases towards the BS \\  & & The prediction technique helps in minimizing the energy expenditure. & The multilayered structure in the network can cause delays in data transfer. \\ \multirow{3}{*}{Cth selection method} & Select the random number between zero and one and compare it with the threshold to select the CH & The node with the highest battery capacity becomes the cluster center CH & The weight function of energy and function value becomes the cluster \\ \multirow{3}{*}{Data transmission} & Single Hop & Multi-Hop & Multi Hop \\ \multirow{3}{*}{CH rotation} & YES & NO & YES \\ \multirow{3}{*}{Sensor node mobility} & Static & Static & Static \\ \multirow{3}{*}{topology} & Distributed & Distributed & Distributed \\ \multirow{3}{*}{Deployment policy} & Random & Random & Random \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief Comparison between cluster-based routing protocols. Figure 1: Traveling patterns of a Mobile Sink using (**a**) the Random Walk MM (RW), (**b**) the Random WayPoint MM (RWP), (**c**) the Random Direction MM (RD) and (**d**) the Gauss Markov MM (GM).