Existing decision-making methods cannot work under the probabilistic uncertain linguistic environment where the decision makers give different uncertain linguistic terms as their assessments and the weights of assessments are different. In this paper, a novel concept called probabilistic uncertain linguistic term set is proposed, which is composed of some possible uncertain linguistic terms associated with the corresponding probabilities. Then, the normalization process, comparison method, basic operations, and aggregation operators are studied for probabilistic uncertain linguistic term sets. After that, an extended technique for order preference by similarity to an ideal solution method and an aggregation-based method are developed to rank the alternatives and then select the best one for multi-attribute group decision-making with probabilistic uncertain linguistic information. Finally, a practical case concerning the selection of Cloud storage services is shown to illustrate the applicability of probabilistic uncertain linguistic term sets.

Evaluating the Internet of Things (IoT) platforms is a crucial step in the development and deployment process of IoT applications. It is still an open issue. In this paper, evaluating the IoT platforms is formulated to be a multicriteria decision making (MCDM) problem since it involves multiple considerations and a novel integrated MCDM method is put forward for handling this problem. To this end, an evaluation criteria system is established to characterize these considerations for evaluating IoT platforms and then the concept of probabilistic linguistic term sets (PLTSs) is introduced to express the group preference information of IoT platforms with respect to the criteria. Then, a novel probabilistic linguistic best-worst (PLBW) method based on score value is put forward for calculating and analyzing the importance degrees of criteria. Based on the PLBW method, two-tuple distance measure, and two-level possibility degree, a novel integrated probabilistic linguistic MCDM model based on TODIM method is proposed to rank IoT platforms. Finally, a practical case is provided to show the procedure of evaluating IoT platforms and the comparative analysis is performed to verify the advantages of the probabilistic linguistic TODIM method.