Consensus-Based Service Selection Using Crowdsourcing Under Fuzzy Preferences of Users

Different evaluator entities, either human agents (e.g., experts) or software agents (e.g., monitoring services), are involved in the assessment of QoS parameters of candidate services, which leads to diversity in service assessments. This diversity makes the service selection a challenging task, especially when numerous qualities of service criteria and range of providers are considered. To address this problem, this study first presents a consensus-based service assessment methodology that utilizes consensus theory to evaluate the service behavior for single QoS criteria using the power of crowdsourcing. To this end, trust level metrics are introduced to measure the strength of a consensus based on the trustworthiness levels of crowd members. The peers converged to the most trustworthy evaluation. Next, the fuzzy inference engine was used to aggregate each obtained assessed QoS value based on user preferences because we address multiple QoS criteria in real life scenarios. The proposed approach was tested and illustrated via two case studies that prove its applicability.