# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 2 Related Work  
An adaptive multiple attribute vertical handover decision algorithm has been developed by [PERSON] and [PERSON]. This algorithm allows for the selection of a wireless access network at a mobile terminal using fuzzy logic and a genetic algorithm. The vertical handover decision algorithm will be used to choose the best access network when a handover is needed. The chosen network can be tailored to network conditions including QoS specifications, mobile terminal state, service cost, and customer expectations [17]. [PERSON] and [PERSON] [18] have proposed a solution for improving the QoS during vertical handovers between UMTS and WiMAX networks for real time video applications. The analyses had shown that performance parameters, such as delay and throughput, are strongly dependent upon the speed of the mobile terminals, showing higher delays and bigger throughput gap as the velocity increases. The proposed optimized solution for vertical handovers between WiMAX and UMTS networks for video traffic has been tested for different video traffic types. This approach will increase the control and signaling traffic in the wireless networks. In [19], a fuzzy based handover algorithm is proposed to improve the capacity as well as QoS of the network using the ratio of declination of normal signal loss to the actual signal loss. The problem is supported by the analytical solution which is based on a fuzzy network. Various systems are formulated based on these mobility models, but the majority of them suffer from high packet loss, repeated handovers, too early and late handovers, insufficient network selection, and so on. To address these issues, a generalized vertical handover control system for heterogeneous wireless networks is introduced in [20]. In paper [21], the research initiatives are described under Mechanism of vertical handover decision for heterogeneous networks with cellular, with a good mix of unconventional and conventional. Recent approaches have been developed to assist in the decision-making process for vertical handover. The authors of paper [22] increased the network performance in terms of the number of handovers and the handover delay by using an LTE-SDN architecture and a novel handover decision algorithm focused on predicting the potential locations of a moving car. The suggested algorithm divides the handover process into two stages: preparation and implementation. Paper [23] proposes fuzzy logic-based vertical handover decision making to solve the smooth vertical handover problem. The paper uses the network simulator ns-3 and the fuzzylite library to conduct the required performance evaluation and facilitate the development of real-time scenarios. The suggested solution is quicker than the average rank for the setup in question, eliminates ping pong handovers, and boosts overall performance. For handover optimization, article [24] introduces an intelligent mobility management system based on the Enhanced Multi-Objective Optimization Method by Ratio Analysis (E-MOORA) and the Q-learning method. The E-MOORA approach incorporates vector normalization by combining the modified entropy weighting methodology with the Multi-Objective Optimization Method by Ratio Analysis (MOORA). When selecting a HO target cell, the suggested E-MOORA technique judiciously utilizes the performance factors and thereby decreases ranking abnormality. The best triggering locations are selected using a Q-learning technique to reduce the effect of frequent unwanted handovers while meeting user QoS requirements. The article [25] proposes a 5G Ultra-Dense Networks handover decision solution for connected cars (5G-UDN). Its main goal is to treat high vehicular mobility and provide performance gains in vehicle handover, because cellular network densification causes difficulties in cell selection, a higher number of failed and unnecessary handovers (ping-pong effect), longer delays and energy consumption, and high packet losses, despite the expected capacity gain. Virtual Cells are the foundation of the solution (V-Cells).