Task Assignment For Small Budget Tasks In Mobile Crowdsourcing

With the popularity of smart mobile devices,a new way to provide people with sensory data has emerged.Mobile Crowd Sensing is a model in which people perceive the current location of environmental information through sensors of smart devices to provide services for social construction,and mobile crowdsourcing provides a platform for such data information acquisition modes.There are many difficult challenges in the task allocation problem of mobile crowdsourcing.Since the labor resources on the platform are limited,how to use these limited resources to accomplish more tasks and create greater value is the focus of our research.However,in existing research,it is often overlooked that a simple task is in a cold start state because its lower budget is difficult to meet the high quotation of workers,which also makes the allocation of small budget tasks more difficult.Then,according to the different attributes of the task,the paper designs three kinds of grouping methods: task cohesion grouping(TGC)and task association grouping(TGR)task similarity grouping(TGS).In addition,based on the differences in personal ability and job price,the paper proposes two workers' selection mechanisms,which effectively solves the problem of selecting workers in mobile crowdsourcing.Finally,according to the work situation of the workers,the reputation value is updated to ensure the fairness of the platform.At the same time,the user is urged to perform the task actively and conscientiously,which indirectly guarantees the quality of the result of the task.This paper evaluates the proposed models and algorithms through a large number of experiments.The experimental results show that the model and algorithm proposed in this paper have obvious performance for the assignment of simple tasks in mobile crowdsourcing,and the participants in the platform can also get better returns.