A genetic algorithm approach for multi-attribute vertical handover decision making in wireless networks

Abstract

Mobile terminals can typically connect to multiple wireless networks which offer varying levels of suitability for different classes of service. Due to the changing dynamics of network attributes and mobile users’ traffic needs, vertical handovers across heterogeneous networks become highly desirable. Multiple attribute decision making (MADM) techniques offer an efficient approach for ranking competing networks and selecting the best one according to specific quality of service parameters. In this paper, a genetic algorithm (GA) is applied to optimize network attributes’ weighting by emphasizing ranking differences among candidate networks, thereby aiding correct decision making by reducing unnecessary handovers and ranking abnormalities. The performance of the proposed GA-based vertical handover is investigated with typical MADM techniques including Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results show that the proposed GA-based weight determination approach reduces the abnormality observed in the conventional SAW and TOPSIS techniques substantially. The results of this paper will help ensuring the application of MADM methods to more dynamic and challenging decision making problems encountered in wireless network.