Research on Multi-task Assignment Model Based on Task Similarity in Crowdsensing
YuPing Liu, Nan Chen, Xingcai Zhang, Xiaodong Liu, Yunhui Yi, Nan Zhao · 2021
In actual multitask assignment scenarios in the crowdsensing(CS) systems, the sensing task can be divided into several subtasks. However, the similarity between the sub tasks is not considered in the existing methods, and its overall utility needs to be improved. To solve this problem, we propose TSMA, a double auction model based on task similarity for multitask crowdsensing systems. First, a filtering algorithm based on similarity analysis is developed. We use the LDA model to analyze tasks, group subtasks by cosine similarity and select the applicant's set of requesters and providers. Then, we design double auction mechanism for solving multitask assignment problem. In this model, we developed a matching and pricing algorithm, the screening algorithm to carry out the tasks.