New Multiparametric Similarity Measure and Distance Measure for Interval Neutrosophic Set With IoT Industry Evaluation

In the epoch of the Internet of Things (IoT), we have confronted five challenges (Connectivity, Value, Security, Telepresence, and Intelligence) with complex structures. The IoT industry decision making is critically important for countries or societies to enhance the effectiveness and validity of leadership, which can greatly accelerate the industrialized and large-scale development. In the case of IoT industry decision evaluation, the essential problem arises serious incompleteness, impreciseness, subjectivity, and incertitude. Interval neutrosophic set, disposing of the indeterminacy portrayed by truth membership <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>, indeterminacy membership <inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula>, and falsity membership <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> with interval form, is a more viable and effective means to seize indeterminacy. The main purpose of this paper is to investigate the multiparametric distance measure and similarity measure. Meanwhile, some interesting properties of distance measure and similarity measure are proved. Then, the objective weights of diverse attributes are ascertained by the deviation-based method. Moreover, we explore the combination weight, which reveals both the objective preference and subjective preference. The validity of the algorithm is illustrated by an IoT industry decision-making issue, along with the effect of diverse parameters on the ranking. Finally, a comparison of the developed with the existing interval neutrosophic decision-making methods have been executed in the light of the counter-intuitive phenomena and unauthentic issue for displaying their effectiveness.