Evaluating IoT Platforms Using Integrated Probabilistic Linguistic MCDM Method

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 article, 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 the 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 the 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.