# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 4 Intelligent Edge Computing
### Slices Classification Techniques  
Network selection in a heterogeneous network environment represents a difficult problem, since there is no solution or technique accepted in this field yet, due to the number of variables and existing scenarios. This can be evidenced with the solutions that either consider or do not consider the process of inter-network mobility. The implementation of new techniques for network selection thus becomes quite feasible, mainly due to the growing demand for its use in vehicular networks, patient monitoring, and smart cities, among other technologies and scenarios involving network convergence [14; 15; 48]. The most common methods reported in the literature for solving the problem of network selection include the use of fuzzy logic, Multiple Attribute Decision Making (MADM) or MCDM, Genetic Algorithms, and Artificial Neural Networks and ML. Among the MADM methods used, there exits the Analytic Hierarchy Process (AHP), Simple  
Figure 2: Edge computing and Fog computing interaction with some applications. Based on [46]. Additive Weighting (SAW), TOPSIS, Multiplicative Exponential Weight (MEW), Simple Multiattribute Rating Technique (SMART), VIKOR, COPRAS, Promethee II, and Grey Relation Analysis (GRA) [8, 13, 17, 18, 48, 49, 50, 51]. Regarding the models that consider hybrid solutions, a feature that has achieved significant results concerns techniques that include fuzzy logic, MADM methods, and ML [6, 16, 17, 34, 52, 53, 54, 55]. These models generally work in a similar way. After the process of data collection, according to the criteria outlined in the previous section, fuzzy logic processing occurs, followed by the classification method via a decision strategy. In this case, for each criterion, a certain weight is allocated to prioritize some services over others, guiding the choice of the new network in accordance with the application in use. Multicriteria Methods  
Multicriteria decision-making approaches consist of several techniques for solving problems. However, there are uncertainties, information conflicts, and disputes in the criteria that are required to evaluate the alternatives. These characteristics are inherent parts of the problem, and must therefore be mapped by the decision maker (person or system) to solve [56]. The objective is to evaluate a set of viable alternatives, through different criteria, considering for each a set of characteristics such as their respective weights; degrees of importance; or preferences of the decision maker [57]. The Promethe II method applies a concept called overclassification, which is a contribution proposed by the European School's multi-attribute decision-making methods. Based on this concept, the alternatives are compared in pairs in order to infer whether a given alternative is as effective as the other. If alternative \(a\) is better than alternative \(b\) considering a given criterion, \(a\) is said to overclassify \(b\). When all alternatives are compared, the final result is a classification (ranking) of all the alternatives in a given set, from best to worst. For this work, the second version of the method was chosen, as Promethee I includes situations where alternatives are incomparable. Promethee II inhibits judgments in which the alternatives are incomparable, always allowing for a complete classification of actions [13]. The TOPSIS method employs the principle of choosing an alternative that is closest to the positive ideal solution (best solution), and farthest from the negative ideal solution (worst solution). Thus, the method focuses on maximizing benefits and minimizing costs [35]. The method is widely used in the literature for different types of problems and has several extensions, in addition to hybrid models with other techniques [8]. The VIKOR method is based on the concept of compromise ranking, meaning it defines a measure of proximity with the ideal solution.