A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian Network

Spatial Crowdsourcing (SC) is a new paradigm of crowdsourcing applications. Unlike traditional crowdsourcing, SC outsources tasks to distributed potential workers, and those who accept the task are required to travel to a predefined location to complete it. Currently, the primary aim of SC is to maximize the number of matched tasks or to minimize the travelling distances of the workers. However, less focus is given in matching the right tasks to the right workers, particularly in a heterogeneous tasks environment. To address this lacking, our work provides an efficient framework for selecting optimal workers for every task with various specification (geographical proximity, domain types, and expiration times), based on workers' attributes (task domain-specific knowledge, expertise or performance history, distance to task location, and task workload distribution). We introduce the use of Bayesian Network in modelling and selecting optimal workers, and use k-medoids partitioning technique for tasks clustering and scheduling. Our experimental results on both synthetic and real-world large datasets have shown that our proposed approach has outperformed other baseline approaches, in terms of low average error rate and fast execution time.