# Designing a Vertical Handover Algorithm for Security-Constrained Applications
## 2 Related Works  
Many recent research works have been considered in the literature [4; 8; 17; 18; 23; 25; 34; 36; 39; 43; 44; 45; 46; 47; 48; 49; 50; 51]. In [4], the authors presented a VHO algorithm using simple additive weighting (SAW) and multiplicative exponential weighting (MEW) techniques, taking into account the following parameters: RSS; packet loss; delay; jitter; network coverage; and bandwidth. The results of the simulation show that the parameters are normalized, and appropriate weights are assigned to them based on their relative importance in the VHO decision process. In [8], the authors presented a VHO algorithm based on fuzzy logic (FL), taking into account the following parameters: RSS; QoS (latency, jitter, packet loss); network coverage; and bandwidth. The result of the simulation shows the best decision as to which RAT should be used to conduct VHO. In [17], a framework of the next generation VHO approach for a high-speed environment was proposed, taking into account RSS. It shows that minimizing the number of unnecessary VHOs and VHO failures leads to a reduction in the power consumption of the applications and maximizes the usage of microcell networks. In [18], the authors designed and simulated a new method based on a simple additive weighting (SAW) algorithm to combine several decision-making criteria to provide seamless VHO, taking into account the following parameters: RSS; signal to noise Ratio (SNR); and bandwidth. In [23], a framework was proposed to provide an effective strategy with the VHO process of initiation and the decision phases, taking into account the following parameters: large resource usage; packet loss; packet overhead; throughput; and time complexity. In [36], the authors presented a VHO of the link going down (LGD) mechanism, which is a predictive event that occurs when link quality is expected to deteriorate in the near future, taking into account the following parameters: packet loss; delay; and throughput. The simulation results show different influences for different packet sizes and data rates for throughput, packet loss rate, and delay. In [39], a VHO algorithm based on an improved k-nearest neighbor (KNN) algorithm was proposed, taking into account the parameters of throughput and drop rate. The simulation results show that the proposed algorithm improves the resource utilization of the system. In [43], the authors designed a new algorithm for network selection using principal component analysis (PCA) to reduce multi-dimensional data into one-dimensional data, taking into account the following parameters: RSS; bandwidth; cost; and MU's speed. In [44], the authors designed and simulated a VHO algorithm based on the highest value of network fitness function (NFF), taking into account the following parameters: RSS; bandwidth; cost; and MU's speed. In [45], the authors proposed hybrid artificial neural networks (HANNs), taking into account the following parameters: RSS; bandwidth; and MU's speed. The simulation results show improvements in the number of VHOs, the number of blocked calls, the throughput, and the data latency compared with previous works. In [46], the authors presented a utility function based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm, which computes VHO decisions, taking into account the following parameters: (1) network-related (RSS, bandwidth, security, network condition, network performance, time to trigger, delay, QoS, speed); (2) terminal-related (power); and (3) service-related (cost, quality). The simulation results are better and use less energy than the previous works. In [47], the authors proposed a VHO algorithm, taking into account the following parameters: average number of VHOs; quality of experience (QoE); and packet loss. The simulation results show a fewer number of VHO, while maintaining a higher QoE and a lower packet loss.