# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 1 Introduction  
Handover Management (HM) plays a fundamental role in wireless communications. HM is the process by which a mobile device maintains an active connection when the user roams from the coverage area of one network to another [1]. HM comprises three phases [2, 3]: initiation, selection, and execution. Handover Initiation collects all information required to identify and determine the neighbor networks, their parameters, and their available services. Network Selection chooses the best available network by taking into account diverse parameters and evaluation metrics. Handover Execution establishes the connection and releases resources. This paper focuses on the selection phase, which is crucial to ensuring service continuity, providing Quality of Services (QoS), and satisfying the Quality of Experience (QoE) [4, 5]. Traditional approaches for Network Selection use a single criterion to decide the next network (e.g., Access Point (AP) or Base Station) to which devices must connect as they move [6]. The Signal Strong First (SSF) is the most widely used traditional approach, which performs the selection by comparing the RSSI of the current network with available adjacent networks [1]. Other approaches based on a single criterion use the available bandwidth [7], load in the network [8], or Signal to Interference Noise Ratio (SINR) [9] to select the network. As these approaches only use one criterion, they ignore important information, leading to throughput drops, unnecessary handovers, and even service disruption [6; 10; 11]. Other approaches use multiple criteria to address the shortcomings mentioned above. The selection criteria can be classified into network, user, device, and application criteria [12]. Network criteria include parameters that describe the wireless network characteristics (e.g., coverage area and Received Signal Strength Indicator (RSSI)). User criteria comprise parameters related to user preferences. Device criteria encompass parameters that provide information about the mobile device, such as battery consumption, and location. Application criteria cover parameters related to the QoS requirements of real or non-real time applications, such as the bandwidth consumed by such applications. Some multi-criteria approaches (also known as Multi-Attribute Decision-Making (MADM)) include the Simple Additive Weighting (SAW), Gray Relational Model, and the Technique for Order Preferences by Similarity to the Ideal Solution (TOPSIS) [4; 13]. These approaches have some shortcomings related to skipping diverse selection criteria, which results in incorrect network selection [6]. Other relevant multi-criteria approaches use techniques such as mathematical models [14], Neural Networks (NN) [12; 15], Fuzzy Logic (FL) [16; 17], and Game Theory [6] to perform network selection. However, these techniques can decrease the throughput during the handover [18]. Hybrid solutions combine multi-criteria approaches [19]; however, their major problem is their high computational complexity. In this paper, we introduce NetSel-RF: a multi-criteria model based on supervised learning which is intended to overcome the shortcomings mentioned above and achieve efficient network selection in WiFi networks. To create this model, we constructed a dataset, performed data preparation, carried out feature selection, and applied different supervised Machine Learning (ML) techniques. Our evaluation results reveal several facts. First, to build up NetSel-RF, the Random Forest (RF) technique [20] performs better than the Hoedding Tree (HT) [20], Adaptive Random Forest (ARF) [21], Hoeffding Adaptive Tree (HAT) [21], Support Vector Machine (SVM) [22] techniques regarding accuracy [23] and the Matthews correlation coefficient (MCC) [24]. Second, NetSel-RF outperforms the SSF approach in terms of the number of handovers, ping-pong, and throughput drops. Third, the proposed method behaves similarly to the AHP-TOPSIS approach regarding the number of handovers and throughput drops.