Multi-criteria Consensus-Based Service Selection Using Crowdsourcing

Different evaluator entities, either human agents (e.g., experts) or software agents (e.g., monitoring services), are involved to assess 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 as first step presents a consensus-based service assessment methodology that utilizes consensus theory to evaluate the service behavior for single QoS criteria using the power of crowd sourcing. For this purpose, 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.