A Task Assignment Approach with Maximizing User Type Diversity in Mobile Crowdsensing

摘要

Mobile crowdsensing (MCS) employs numerous mobile users to perform sensing tasks, in which task assignment is a challenging issue. Existing researches on task assignment mainly consider spatial-temporal diversity and capacity diversity, but not focus on the type diversity of users, which may lead to low quality of tasks. This paper formalizes a novel task assignment problem in MCS, where a task needs the cooperation of various types of users, and the quality of a task is highly related to the various types of the recruited users. Therefore, the goal of the problem is to maximize the user type diversity subject to limited task budget. This paper uses three heuristic algorithms to try to resolve this problem, so as to maximize user type diversity. Through further simulations, the proposed algorithm UR-GAT (Unit Reward-based Greedy algorithm by type) obviously improves the user type diversity.