# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 3 Literature Review  
In this section, we highlight the recent handover mechanisms in wireless networks, which mainly deploy prediction approaches (e.g., classification models). Then, we address the basic problems associated with UWSNs and list efforts directed to their applications. Finally, we narrow this literature review to describe handover problems in UWSNs. In the last few years, several studies have proposed solutions to enable smooth mobility in wireless networks by improving the handover process. In [12], authors proposed two prediction mechanisms to estimate mobile user handovers using user-network association patterns. They conducted a series of experiments using real data to compare the performance of such mechanisms against more advanced and complex prediction systems. In another study, the handover process of Wi-Fi networks was predicted using a novel technique [3]. The primary purpose of this technique is to maintain the connections between resources when they are reallocated or fill out the video buffer with low-definition video frames before the user loses the connection. In addition, a recent study proposed clustering and classifying algorithms to streamline the 5G handover procedure and enhance the network connectivity [4]. A UWSN consists of a set of sensor nodes placed in a specific marine area which communicate over an acoustic channel, as shown in Figure 1. This communication can be established under restricted circumstances with a possibility of long propagation delay and high bit error rates, considering the restrictions of batteries [13; 14]. In [15], the authors proposed a new technique to correct the bit error based on energy analysis and considering the network conditions. Recently, numerous research papers have developed solutions to improve underwater applications and ocean exploration needs. One of the most critical applications is the monitoring of the physical environment (e.g., battlefields, floods, volcanoes), and other applications that rely on data from these sensors [16; 17]. Additionally, interest in underwater communication systems for ocean observation has dramatically grown over the last decade. Through ocean monitoring, many applications are addressed, including military plans, marine weather forecasting, oceanographic data compilation, and environmental observation [18]. These applications involve autonomous underwater vehicle (AUV) communications, oil extraction, ocean pollution, and aquaculture [19; 20]. AUVs, sea buoys, and sensor nodes are positioned in a geographic region to collaboratively monitor and gather data in underwater sensor networks. Currently, a higher bandwidth with adequate communication schemes is required to accommodate the deployment of AUVs and sensors that continuously collect real-time data (i.e., images, videos, water temperature, and salinity). However, the lack of interactive communication among installed devices can impede data delivery and lead to a loss of the collected data. A new approach was presented in [21] to expand the handover protocol in clustered ad hoc diver networks to enhance the reliability and flexibility of the connections. Moreover, a new method was implemented and tested with different nodes to allow multi-hop connections using a low-cost solar-powered underwater modern in [22]. The proposed method checks whether the diver leaves the communication range; if so, the model applies the handover process by using the diver as a bridge between the receiver and the transmitter. The model presents a suggestion for the handover concepts to be applied only and does not provide an experiment or investigate the proposed idea. Additionally, the proposed  
Figure 1: Schematic diagram of the use of machine learning models to predict the handover of a sensor node in a UWSN. method does not consider placing any restrictions on the distance between nodes, since the model uses visual light. Nevertheless, efficient determination of the positions of the sensor nodes and their signal intensity is a crucial challenge in UWSNs.