Performance Measurement towards Crowd-Workers Reliability in Crowd Computing using Bayesian Probability Model - Hussin, Masnida; Aliya Rozlan, Nur (International Journal of Engineering and Technology, 2018)
Crowd computing becomes increasingly popular nowadays to Internet users. It has been chosen as distributed processing mechanism to solve computing problems which offer less policy but better business. Some of advantages in using the crowd computing are significant in time and cost used. In the crowd computing, the users’ tasks are completed by crowd workers. These workers have different skill, knowledge and style in finishing the task that allocated to them. Satisfaction on the completing the tasks is really challenging to measure in the crowdsourcing due to its dynamic environment. Furthermore, it is raised when behaviour or commitments of crowd-workers in providing services are started to query, either it can be trusted or not.  In this work, a Bayesian probability model utilized to assess workers reliability in crowd computing is proposed. Specifically, we formulate trust factor by using the Bayesian model for indicating the reliability of the available workers in crowdsourcing platform. The process of prediction and hypothesis of workers’ commitment are identified to relate with the crowd-sourced computing system. We designed the significant behavioural factors to measure the workers performance towards user satisfaction. We then developed an automation measurement system that used to verify Bayesian formulation towards workers performance. The web-based automation system is able to identify the workers’ reliability values according to the input/response from the users. Optimistically, by using Bayesian probability model provides guidelines for designing trustworthy system in crowd sourcing platform.