Exploring fuzzy set consensus analysis in IoT resource ranking

The complexity of the procedures for discovering, classifying, and selecting suitable resources to meet customer demands is related to the growing resource offers connected to the Internet. This proposal takes into account the uncertainties in the specification and processing of customer preferences, via consensual analysis. In this work we study the relationship between restricted equivalence functions, more generally, consensus measures, and the possibility of building the latter using the former. Thus, consensus measures of fuzzy values and consensus measures on fuzzy sets are both defined by aggregations, such as the arithmetic mean and the exponential mean. Based on the interval-valued fuzzy logic we consider inaccuracies related to the measurements beyond the uncertainties, modeling the imprecision of expertise in classifying a set of resources in the IoT based on the IT2FL-EXEHDA-RR Model. Several results arise from these methods. Firstly, the methodology L[0,1]-FCM, which is performed to measure how similar are the corresponding fuzzy values of a fuzzy set on [0,1]. In particular, it is applied to the superior and the inferior limits of each interval-valued membership function, modeling linguistic variables related to the attributes of the IT2FL-EXEHDA-RR Model. And the second one, based on LFχ-FSCM, providing the consensus analysis among a family of fuzzy sets. In the reported case study, this is applied to obtain the consensus measure between corresponding upper and lower bounds of each interval-valued function. This research also investigates the conditions under which we can build convex sum based on LFχ-FSCM, including the analysis of their main properties.