# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 4 The Proposed System
### Dataset Generation  
The Korea Hydrographic and Oceanographic Agency collects a public real-time dataset for information observation, which is updated every 30 min, as previously mentioned. The underwater network is located at 34.223611 latitude and 128.420555 longitude ([[https://www.khoa.go.kr/eng/](https://www.khoa.go.kr/eng/)]([https://www.khoa.go.kr/eng/](https://www.khoa.go.kr/eng/)) (accessed on 26 August 2021)). The region is divided into sectors by centroid sea buoys, which relay data from nearby underwater sensors to the aboveground base station. Prior to transmission, relevant information is processed by the buoys, which includes measurements of the time, water salinity, water temperature, wave height, sea surface direction, and flow speed. The aforementioned dataset is extremely rich with vital marine information. This information can help to predict the movement of the sensor nodes. Several existing research works have discussed the mobility of devices and their role in predicting handover events [25; 26]. Most of these existing methods target on-land handover prediction. A few exceptions discuss UWSN mobility models [27]. Underwater sensor nodes are not stationary. Instead, they travel with water currents a result of various activities and conditions present underwater. Thus, in this work we use real marine data from the Korea Hydrographic and Oceanographic Agency to simulate the sensor node movements. Then, the captures the movement of these sensor nodes, which is used as a history of handover events. Finally, the captured handover events is utilized to train several machine learning models to predict the next covering cell of a sensor node in a handover event. Underwater sensor network nodes can be static or non-static, similar to ground-based sensor network nodes [28]. The latter type moves with water currents due to different activities and circumstances in the underwater environment. This work focuses on non-static sensor nodes. Figure 3 depicts a buoy that is surrounded by six different cells (i.e., buoys). Each buoy of the Korea Hydrographic and Oceanographic Agency is represented by a hexagon that shows its coverage area. The simulation consists of a set of buoys, each of which has a number of sensor nodes (i.e., dots), as shown in Figure 4. The movement of each sensor node is based on the marine data collected by each buoy. The simulation starts by initially placing the sensor nodes in the coverage area of each buoy. Then, these sensor nodes wag in specific directions with the guidance of the velocity and direction of the water currents. As a result, the data in the dataset aid in sensor guidance by successfully recommending a nearby buoy, which results in an efficient handover operation. Uninterrupted data transmission from sensor nodes is assured in this manner. During the movements of the sensor nodes, the simulation captures several pieces of information, that is, the sensor node location in terms of latitude and longitude, the ID of the buoy that provides the coverage, and the signal strength. During the simulation, the sensor node location is updated based on the water speed and direction using Equations (5) and (6) [27]. \[X_{t+1} =X_{t}+\alpha\times s\times\sin(dir) \tag{5}\] \[Y_{t+1} =Y_{t}+\alpha\times s\times\cos(dir) \tag{6}\]where \(X_{i+1}\) and \(Y_{i+1}\) are the next latitude and longitude of the sensor node, respectively, and \(X_{t}\) and \(Y_{t}\) are the current latitude and longitude of the sensor node, respectively. \(\alpha\) is a parameter to tune the speed of the sensor node based on the level at which the sensor node is located under the water surface; if \(\alpha=1\), the sensor node moves at the full speed of the water. \(s\) and \(dir\) are the speed and direction of the water, respectively. Finally, the \(\sin\) and \(\cos\) functions are from [PERSON]'s second law of motion.