Intelligent Vertical Handover Model Based on Neuro-Fuzzy for 4G and 802.11 Networks

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dc.contributor.authorHamamreh, Rushdi
dc.contributor.authorJaffal, Niveen
dc.contributor.authorNassereldin, Safa
dc.date.accessioned2026-01-21T19:29:47Z
dc.date.available2026-01-21T19:29:47Z
dc.date.issued2025-11-12
dc.description.abstractThis chapter introduces an intelligent Vertical Handover (VHO) model that combines the capabilities of Fuzzy Logic and Neural Networks within the Adaptive Neuro-Fuzzy Inference System (ANFIS), based on the Takagi-Sugeno Fuzzy Inference System. The proposed framework, termed IVH_NF, assists User Equipment (UE) in selecting the most suitable network between 4G LTE and IEEE 802.11 WiFi by utilizing multiple input parameters, including Received Signal Strength (RSS), Mobile Speed (MS), Distance (D), and Packet Cost (PC). Simulation results of LTE-to-WiFi handover scenarios show that IVH_NF enhances Quality of Service (QoS) by improving throughput, sustaining acceptable speed and bitrate, minimizing End-to-End Delay (EED), and reducing handover frequency. When compared to the existing algorithms, IVH_NF achieves a 58% decrease in EED over RSS-based methods, a 33.3% reduction in handovers compared to prior ANFIS approaches, and a 60% reduction relative to Fuzzy Logic-based models.
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/10401
dc.publisherIntechopen
dc.titleIntelligent Vertical Handover Model Based on Neuro-Fuzzy for 4G and 802.11 Networks
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