Goudarzi Shidrokh, Haslina Hassan Wan, Abdalla Hashim Aisha-Hassan, Soleymani Seyed Ahmad, Anisi Mohammad Hossein, Zakaria Omar M
Communication System and Network (iKohza) Research Group, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Semarak, Kuala Lumpur 54100, Malaysia.
Faculty of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur, Kuala Lumpur, Malaysia.
PLoS One. 2016 Jul 20;11(7):e0151355. doi: 10.1371/journal.pone.0151355. eCollection 2016.
This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.
本研究旨在设计一种垂直切换预测方法，以尽量减少移动节点（MN）在垂直切换过程中不必要的切换。这依赖于一种预测接收信号强度指示符（RSSI）的新方法，称为IRBF-FFA，它是通过利用帝国主义竞争算法（ICA）训练径向基函数（RBF），并与萤火虫算法（FFA）混合以预测最优解而设计的。通过将所提出的IRBF-FFA模型与支持向量机（SVM）和多层感知器（MLP）模型进行比较，验证了该模型的预测准确性。为了评估模型的性能，我们测量了决定系数（R2）、相关系数（r）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）。所得结果表明，与不同的人工神经网络（ANN），即支持向量机（SVM）和多层感知器（MLP）相比，IRBF-FFA模型提供了更精确的预测。通过模拟和实时RSSI测量对所提出模型的性能进行了分析。结果还表明，IRBF-FFA模型可作为一种有效的技术用于垂直切换的准确预测。
