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<p>With the aid of efficient network selection and routing, this research suggests a unique vertical handoff/handover (VHO) mechanism for Heterogeneous wireless networks (HWN). The three stages of this paradigm are handover execution, handover decision-making, and handover triggering. Initially, the handover triggering phase, which calculates the vehicle's Travelling Distance (TD) using Enhanced Extended Kalman Filter (E<sup>2</sup>KF) and RSS of the currently serviced network in the coverage area using Long Short-Term Memory (LSTM) is initiated. Secondly, the handover decision-making system is performed optimally by selecting the appropriate network for handover using the proposed XGBoost-Deep Neural Network Fusion Model (XGDN-FM). Finally, after the handover decision phase, the handover execution is carried out by selecting optimal Vehicle 2 Vehicle (V2V) routing using the proposed Trend factor Smoothing Brown Bear Optimization Algorithm (TS-BBOA). The outcomes showed that the model attains the lowest PLR of 0.18%, highest throughput of 4.89Mbps, low energy consumption of 2.4&#xa0;J, and highest handover success probability with less handover failure and unnecessary handovers.</p>

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XGBoost-deep neural network fusion model for network selection-based vertical handover decision making and optimized routing for heterogeneous VANET

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • J. Vijila,
  • A. Albert Raj

摘要

With the aid of efficient network selection and routing, this research suggests a unique vertical handoff/handover (VHO) mechanism for Heterogeneous wireless networks (HWN). The three stages of this paradigm are handover execution, handover decision-making, and handover triggering. Initially, the handover triggering phase, which calculates the vehicle's Travelling Distance (TD) using Enhanced Extended Kalman Filter (E2KF) and RSS of the currently serviced network in the coverage area using Long Short-Term Memory (LSTM) is initiated. Secondly, the handover decision-making system is performed optimally by selecting the appropriate network for handover using the proposed XGBoost-Deep Neural Network Fusion Model (XGDN-FM). Finally, after the handover decision phase, the handover execution is carried out by selecting optimal Vehicle 2 Vehicle (V2V) routing using the proposed Trend factor Smoothing Brown Bear Optimization Algorithm (TS-BBOA). The outcomes showed that the model attains the lowest PLR of 0.18%, highest throughput of 4.89Mbps, low energy consumption of 2.4 J, and highest handover success probability with less handover failure and unnecessary handovers.