# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 5 Related Works
The use of a fuzzy logic approach led to the ability to manage inaccurate data, which is a useful enhancement to this approach. This enables it to make multi-criteria decisions. The authors then created an adaptive HO management approach based on a fuzzy logic system that works in conjunction with an existing cross-layer HO protocol. Based on the compression between the performance of both the existing and proposed approaches, the suggested technique outperforms the traditional method with noticeable intra-system and inter-system HO. In 2007, 2008, and 2014 [91; 92; 93], MATLAB was employed to develop a vertical HO scheme. Since MATLAB was the platform used, the proposed approach is suitable for wireless wide area networks (WWAN) and cellular networks. To construct the fuzzy logic quantitative decision algorithm (FQDA), eight factors were considered. There were 81 rules used for the eight factors, which were then compared to 6561 rules. Algorithm models were developed and implemented according to various criteria based on vertical HO. These vertical HO methods were demonstrated in the heterogeneous wireless networks (HetNets) WWAN and WLAN environments. The methods are per the IP-based workforce automation sector, which allows unrestricted mobility across networks while connecting via IP mode using a single device on multiple networks. The vertical HO approach was used to integrate Wi-Fi (IEEE 802.11) and WiMAX. The signal-to-noise ratio, moving speed, and signal strength were all factors considered in this study. NS2 and NS3 were used to create the simulation. In 2010, 2013, and 2019 [94; 95; 96], two approaches to vertical HO were introduced in a HetNets environment. The fuzzy interface system was used, as well as subtractive clustering techniques. According to the simulation, the approach enables the HO procedure to become easier and faster for different protocol users. The authors proposed a method for conserving energy and battery life by using fuzzy logic. Mobile phones with LTE and Wi-Fi capabilities can also be useful in reducing battery consumption. Researchers proposed a method for performing HO in 3D space by considering speed and coverage constraints. A fuzzy interaction system was created to make HO decisions. In 2010 and 2014 [35; 97], a speed adaptive system with a knowledge method was created to enhance the rate of the network's candidate set. The decision algorithm was developed as a collaboration between vertical handoff, fuzzy logic, and pre-HO decision in order to generate effective and efficient judgments. A performance study was conducted to compare the proposed work with the typical RSS. According to its findings, the suggested method improved performance in terms of reducing unnecessary HO and the rate of call blocking or dropped calls. Many HO algorithms were measured and used to reduce HO in the network. According to the results of this survey, popular algorithms were developed to address complex challenges, which sometimes lack clarity or sufficient detail. In 2011 and 2018 [56; 98; 99], the authors listed several problems that mobile aerial users face. In most recent static cellular deployments, the sidelobes of the antenna design assist aerial users. As a result, the connection pattern is broken. Due to the fragmented connection and low SINR, a higher risk of radio connection and HO failures are present. The uneven connection pattern, in which a user is returned to its original cell within a set time limit, will lead to more ping-pong HOs. While LTE is designed to allow users to travel at speeds of up to 350 km/h, it is based on large cell areas rather than the sidelobe-based cell attachment patterns seen in UAVs. The 3 GPP research item has identified cell selection, HO efficiency, and robustness as critical performance criteria for aerial users in cellular networks. In 2013, 2015, and 2016 [75; 100; 101], the authors implemented a machine learning technique for the UAV network as a potential solution.