# Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services +
## 5 Evaluation and Discussion  
To evaluate the ranking computed by our approach (based on the technique form [58]), this section compares the ranking of the alternatives to two other popular techniques [59; 60] (see Appendices B and C) that also employ the fuzzy similarity method for solving given MCDA problems by capturing imprecision and inaccurate definition of a decision problem. Also, this section lists all the algorithms here for easy adoption by other research teams. For the evaluation, we adopted the linguistic variables for ranking the alternatives and evaluation criteria as shown in Tables 3 and 4 successively, and we repeated the same calculation steps described in the previous section for [59; 60]. Then, we conducted MATLAB simulation to test the results and compare them with [58] (see Appendices B and C). As shown in Figure 5, all the applied methods select A1 as the best alternative for processing the user request. However, the ranking order of the alternatives is not identical because of the difference in the Fuzzy positive ideal solution and closeness coefficient calculation (Figures 6 and 7). Yet, the ranking of the candidates is overall well aligned. Figure 5: Ranking the alternatives according to [58; 60; 59]. Figure 6: Calculation of Fuzzy positive ideal solution according to [58; 60; 59]. Now consider a use case, where a user does not want to use the best-selected alternative due to its high price, for example, and wants to select the second-best alternative according to the proposed ranking list. In this case, A2 would be the selected candidate according to [58, 60], while A3 would be selected by [59] (Figure 5). Yet, in this dilemma situation, how could the provider of services identify and determine the best-ranking list of the alternatives and, then, recommend another service to the end-client? and what will be the criteria to select the right one? In this case, it would be very interesting to provide more detailed guidance on the selection among distinct MCDA approaches, which would be an interesting topic for further study that could examine the uncertainty and imprecision issues in multicriteria decision-making for determining the best-ranking list of alternatives. In fact, the precision of results based on the application of an optimal cloud (or edge) service is the key to successful IoT indoor and outdoor applications. To do that, the selection of cloud or edge services-based MCDA approaches is an essential step that could aid millions of people, who are using IoT devices, for tracking their daily activities. Besides, it could help the providers of services to collect IoT data adequately and process it to extract useful information for improving and monitoring local conditions of IoT applications. Particularly, by 2025, it will be more challenging to process the collected IoT data, since IoT will remarkably exceed 100 billion connected devices [61]. Consequently, this big IoT innovation will create many significant challenges such as how to ensure the quality aspects of IoT data (i.e., selection, verification, and validation of data), save the battery of sensors, optimize routing and prediction of nodes, etc. To face these issues, the providers of services must now be experts not only in the development of applications for IoT paradigm but also in the investigation of the user's feedback for the available services that would be accounted for effective utilization of cloud/edge services. In this context, the MCDA approaches could help the providers of services to understand and clarify the users' preferences, consider each decision criterion, and support communication between them. However, the current shift from service orientation to user orientation in recent innovations, like in the smart city domain, will keep introducing decision-support challenges (i.e., a lack of connection between criteria scales [62]) in designing recommendation services in the edge and cloud environments, stemming from diverging recommendations by different approaches.