NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning

Daniela Alexandra Embus,Oscar Mauricio Caicedo,Armando Ordóñez,Fulvio Yesid Vivas,Andres Julián Castillo

doi:10.3390/app10124382

Daniela Alexandra Embus, Oscar Mauricio Caicedo + Show 3 more

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https://doi.org/10.3390/app10124382

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Abstract

Network selection plays a pivotal role in ensuring efficient handover management. Some existing approaches for network selection may use one criterion, such as RSSI (Received Signal Strength Indicator) or SINR (Signal to Interference Noise Ratio). However, these approaches are reactive and may lead to incorrect decisions due to the limited information. Other multi-criteria-based approaches use techniques, such as statistical mathematics, heuristics methods, and neural networks, to optimize the network selection. However, these approaches have shortcomings related to their computational complexity and the unnecessary and frequent handovers. This paper introduces NetSel-RF, a multi-criteria model, based on supervised learning, for network selection in WiFi networks. Here, we describe the created dataset, the data preparation and the evaluation of diverse supervised learning techniques (Random Forest, Support Vector Machine, Adaptive Random Forest, Hoeffding Adaptive Tree, and Hoedding Tree techniques). Our evaluation results show that Random Forest outperforms other algorithms in terms of its accuracy and Matthews correlation coefficient. Additionally, NetSel-RF performs better than the Signal Strong First approach and behaves similarly to the Analytic Hierarchy Process–Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS) approach in terms of the number of handovers and throughput drops. Unlike the latter, NetSel-RF is proactive and therefore is more efficient regarding Quality of Services (QoS) and Quality of Experience (QoE) since the end-devices perform the handover before the network link quality degrades.

Highlights

Handover Management (HM) plays a fundamental role in wireless communications
We introduce NetSel-Random Forest (RF): a multi-criteria model based on supervised learning which is intended to overcome the shortcomings mentioned above and achieve efficient network selection in
We evaluated Hoedding Tree (HT), Hoeffding Adaptive Tree (HAT), RF, Adaptive Random Forest (ARF), and Support Vector Machine (SVM) by using the confusion matrix, which is a fundamental tool to evaluate the performance of classification algorithms; this matrix allows us to determine quickly if the model is confusing different classes

Summary

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]

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