Handover Management (HM) is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in wireless communications. Current HM approaches based on a single criterion may lead to unnecessary and frequent handovers due to a partial network view that is constrained to information about link quality. In turn, HM approaches based on multicriteria may present a failure of handovers and wrong network selection, decreasing the throughput and increasing the packet loss in the network. This paper proposes SIM-Know, an approach for improving HM. SIM-Know improves HM by including a Semantic Information Model (SIM) that enables context-aware and multicriteria handover decisions. SIM-Know also introduces a SIM-based distributed Knowledge Base Profile (KBP) that provides local and global intelligence to make contextual and proactive handover decisions. We evaluated SIM-Know in an emulated wireless network. When the end-user device moves at low and moderate speeds, the results show that our approach outperforms the Signal Strong First (SSF, single criterion approach) and behaves similarly to the Analytic Hierarchy Process combined with the Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS, multicriteria approach) regarding the number of handovers and the number of throughput drops. SSF outperforms SIM-Know and AHP-TOPSIS regarding the handover latency metric because SSF runs a straightforward process for making handover decisions. At high speeds, SIM-Know outperforms SSF and AHP-TOPSIS regarding the number of handovers and the number of throughput drops and, further, improves the throughput, delay, jitter, and packet loss in the network. Considering the obtained results, we conclude that SIM-Know is a practical and attractive solution for cognitive HM.
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.
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