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A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly

Shidrokh Goudarzi¹, Wan Haslina Hassan¹, Aisha-Hassan Abdalla Hashim²

¹Communication System and Network (iKohza) Research Group, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Semarak, Kuala Lumpur 54100, Malaysia.

|July 21, 2016

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This study introduces the IRBF-FFA model for accurate vertical handover prediction, minimizing unnecessary network transitions. This novel method improves received signal strength indicator (RSSI) forecasting for mobile nodes.

Area of Science:

Wireless communication networks
Mobile computing
Signal processing

Background:

Vertical handover is crucial for seamless mobile node connectivity.
Minimizing unnecessary handovers enhances user experience and network efficiency.
Accurate prediction of received signal strength indicator (RSSI) is key to effective handover decisions.

Purpose of the Study:

To design a novel vertical handover prediction method.
To minimize unnecessary handovers for mobile nodes.
To improve the accuracy of received signal strength indicator (RSSI) prediction.

Main Methods:

Developed the IRBF-FFA model, combining imperialist competition algorithm (ICA) with radial basis function (RBF) and firefly algorithm (FFA).
Trained the RBF using ICA and optimized with FFA for RSSI prediction.

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Validated the IRBF-FFA model against Support Vector Machines (SVMs) and Multilayer Perceptron (MLP).

Main Results:

The IRBF-FFA model demonstrated superior prediction accuracy compared to SVM and MLP.
Performance was assessed using R2, r, RMSE, and MAPE metrics.
Simulated and real-time RSSI measurements confirmed the model's effectiveness.

Conclusions:

The IRBF-FFA model offers a highly accurate technique for vertical handover prediction.
This method can significantly reduce unnecessary handovers in wireless networks.
The model shows potential for efficient application in real-world mobile environments.