Probabilistic Linguistic VIKOR Method Based on TODIM for Reliable Participant Selection Problem in Mobile Crowdsensing

In the mobile crowdsensing systems, the participants of great variety and diversity voluntarily submit their sensing data. Evaluating the participants and ranking them is a critical problem that should be solved to ensure the data quality. In this paper, we introduce the concept of probabilistic linguistic term sets (PLTSs) to model the group preference information during the process of ranking candidate participants and then propose novel VIKOR methods based on TODIM for solving the process of ranking reliable participants and selecting the best one in the mobile crowdsensing system. This proposed methods combine the advantages from the VIKOR method and TODIM method. To show the implementation process of evaluating participants and selecting the best one under the PLTS information context, a practical case is given to verify the feasibility of the proposed methods. Compared with the existing decision making methods, the proposed methods show their effectiveness.