Quality-aware multi-task allocation based on location importance in mobile crowdsensing

Abstract

Mobile crowdsensing (MCS) is a new data acquisition mode, which recruits the appropriate mobile users to complete the sensing tasks based on each task’s relevant attributes. With the budget constraints, each task can only be allocated to a limited number of users. To improve the total sensing quality, the MCS platform should employ more users for important sensing tasks. Location information is a crucial parameter for evaluating the task’s importance. Previous works have only considered location as an attribute of tasks without fully examining the impact of location information on task allocation, which is extremely significant. In this paper, we study the problem of quality-aware multi-task allocation based on location importance (QMLI) in mobile crowdsensing, which considers the impact of location information on task allocation to maximize the sensing quality. Moreover, we convert the analysis of location importance into a graph theory problem and propose a location importance evaluation method, which can analyze the importance of each subarea based on different location information. The QMLI problem is proved to be NP-hard, and two task allocation algorithms are proposed to obtain near-optimal solutions. We conduct the performance evaluation based on both the simulation and real-world dataset to illustrate the effectiveness of the proposed approaches.