An Overview of Vertical Handoff Decision Algorithms in NGWNs and a new Scheme for Providing Optimized Performance in Heterogeneous Wireless Networks

Because the increasingly development and use of wireless networks and mobile technologies, was implemented the idea that users of mobile terminals must have access in different wireless networks simultaneously. Therefore one of the main interest points of Next Generation Wireless Networks (NGWNs), refers to the ability to support wireless network access equipment to ensure a high rate of services between different wireless networks. To solve these problems it was necessary to have decision algorithms to decide for each user of mobile terminal, which is the best network at some point, for a service or a specific application that the user needs. Therefore to make these things, different algorithms use the vertical handoff technique. Below are presented a series of algorithms based on vertical handoff technique with a classification of the different existing vertical handoff decision strategies, which tries to solve these issues of wireless network selection at a given time for a specific application of an user. Based on our synthesis on vertical handoff decision strategies given below, we build our strategy based on solutions presented below, taking the most interesting aspect of each one.Vertical Handoff, Genetic Algorithms, Fuzzy Logic, Neural Networks, AHP

An Overview of Vertical Handoff Decision Algorithms in NGWNs and a new Scheme for Providing Optimized Performance in Heterogeneous Wireless Networks

Because the increasingly development and use of wireless networks and mobile technologies, was implemented the idea that users of mobile terminals must have access in different wireless networks simultaneously. Therefore one of the main interest points of Next Generation Wireless Networks (NGWNs), refers to the ability to support wireless network access equipment to ensure a high rate of services between different wireless networks. To solve these problems it was necessary to have decision algorithms to decide for each user of mobile terminal, which is the best network at some point, for a service or a specific application that the user needs. Therefore to make these things, different algorithms use the vertical handoff technique. Below are presented a series of algorithms based on vertical handoff technique with a classification of the different existing vertical handoff decision strategies, which tries to solve these issues of wireless network selection at a given time for a specific application of an user. Based on our synthesis on vertical handoff decision strategies given below, we build our strategy based on solutions presented below, taking the most interesting aspect of each one

The arithmetic of triangular Z-numbers with reduced calculation complexity using an extension of triangular distribution

This work was supported by project PID2019-103880RB-I00 funded by MCIN/AEI/10.13039/501100011033, by FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-590-UGR20, by the China Scholarship Council (CSC) , and by the Andalusian government through project P2000673. Funding for open access charge: Universidad de Granada/CBUA.Information that people rely on is often uncertain and partially reliable. Zadeh introduced the concept of Z-numbers as a more adequate formal construct for describing uncertain and partially reliable information. Most existing applications of Z-numbers involve discrete ones due to the high complexity of calculating continuous ones. However, the continuous form is the most common form of information in the real world. Simplifying continuous Z-number calculations is significant for practical applications. There are two reasons for the complexity of continuous Z-number calculations: the use of normal distributions and the inconsistency between the meaning and definition of Z-numbers. In this paper, we extend the triangular distribution as the hidden probability density function of triangular Z-numbers. We add a new parameter to the triangular distribution to influence its convexity and concavity, and then expand the value's domain of the probability measure. Finally, we implement the basic operations of triangular Z-numbers based on the extended triangular distribution. The suggested method is illustrated with numerical examples, and we compare its computational complexity and the entropy (uncertainty) of the resulting Z-number to the traditional method. The comparison shows that our method has lower computational complexity, higher precision and lower uncertainty in the results.MCIN/AEI PID2019-103880RB-I00FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades/Proyecto B-TIC-590-UGR20China Scholarship CouncilAndalusian government P2000673Universidad de Granada/CBU

Data fusion using expected output membership functions

Multi-sensor systems can improve accuracy, increase detection range, and enhance reliability compared to single sensor systems. The main problems in multi-sensor systems are how to select sensors, model the sensors, and combine the data;This dissertation proposes a new data fusion method based on fuzzy set methods. The expected output membership function (EOMF) method uses the fuzzy input set and the expected fuzzy output. This method uses the intersections of the fuzzy inputs with the expected fuzzy output in order to find relationships between the given inputs and the estimate of the output. The EOMF method creates a fuzzy confidence distance measurement by assessing the fusability of the data. The fusability measure is used for finding the best position of the EOMF and the best estimate of the system output. Adaptive methods can help deal with occasional bad measurements and set the EOMF to the proper width. The EOMF method can be used for both homogeneous and heterogeneous sensors, which give redundant, cooperative or complementary information. In addition, the EOMF method is robust in the sense that it can eliminate sensor measurements that are outliers. The EOMF method compares favorably with other methods of data fusion such as the weighted average method. An example from the control of automated vehicles shows the effectiveness of the adaptive EOMF method, compared to the fixed EOMF method and the weighted average method in the presence of Gaussian and impulsive noise. This method can also be applied to nondestructive evaluation (NDE) images from heterogeneous sensors