Task quality optimization in budget-limited spatial crowdsourcing
Jiali Weng · 2024
Spatial crowdsourcing demonstrates its efficient data collection capability and drives the development of numerous related applications, such as geographical environmental monitoring, food delivery, and traffic management. Task assignment is a core concern in spatial crowdsourcing, which requires a balance between efficiency (i.e., reducing the cost of assigned tasks) and effectiveness (achieving a greater number of tasks covered for execution). Therefore, we define the problem of optimal task quality in spatial crowdsourcing networks. In contrast to traditional task quality problems, we select only a subset of mobile workers to maximize the task quality with limited budgets. This problem is proven to be NP-hard. We first define the task quality function and prove that it is submodular and non-decreasing. By leveraging the properties of the function, we propose a (1-(1/e)) heuristic algorithm with a time complexity of 𝑂(nk+2), where integer 𝑘≥3. Ultimately, experimental findings demonstrate that our method surpasses both random selection and a state-of-the-art technique in terms of overall quality.