# A Robust UWSN Handover Prediction System Using Ensemble Learning
###### Abstract  
The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical. s 2021275772021  
ensure continuous and consistent timely data transmission over the network and achieve uninterrupted communication. Several proposed handover prediction methods utilize machine learning models (e.g., [3,4]) to obtain better communication on terrestrial sensor networks. Other works applying machine learning models to improve the handover prediction problem in UWSNs include [1,5]. The authors in [3] developed a new technique to predict blind spots and handover associated with the movement of Wi-Fi devices, where video buffers are stuffed with low-resolution video frames before resources are reallocated or the connection is lost. The proposed prediction method is based on a machine learning system, where the pattern of the received signal strength indicator (RSSI) is used to determine a future handover over time. In [4], the authors proposed two different methods to predict the handovers of a mobile user using the user's behavior information given by the networks. To validate their method, the authors compared the performance of the proposed handover prediction mechanism to decision tree classification and K-nearest neighbors. In the UWSN model, sensor nodes usually move by themselves (i.e., autonomous underwater vehicles) [6] or passively move due to flows and other natural impacts [7]. It is very difficult to define and control the locations of passive nodes. The authors in [5] proposed machine learning models to predict the flow directions of the sensor nodes that flowed in the surface layers of the ocean. In [1], the authors proposed a new model based on machine learning techniques for handover prediction problems in the Internet of Underwater Things (IoUT). The prediction model removes the overhead associated with channel measurement to reduce the power consumed by underwater things. In [1,5], the obtained prediction accuracy rates were very low (approximately 56%). In addition to the low prediction accuracy of the existing methods, the authors did not make the dataset publicly available. Thus, it is impossible to reproduce the results reported in [1,5]. The dynamicity and continuous flow of sensor nodes make them shift away from their locations toward other sectors.