Task Assignment Algorithm Based on Social Influence in Mobile Crowd Sensing System

Abstract

In the mobile crowd sensing (MCS) system, task assignment is a core and common research issue. Based on the traditional MCS platform, there is a cold start problem. This paper introduces social networks and communication networks to solve the cold start problem. Therefore, this paper draws on social influence to propose a greedy task assignment algorithm H-GTA. The core idea of the algorithm is to first use the communication network to select seed workers in a heuristic manner according to the recruitment probability and then the seed workers spread the task on social networks and communication networks simultaneously. The publisher selects workers to assign the task in a greedy way to maximize the task’s spatial coverage. When calculating the probability of recruitment, this paper considers various factors such as worker’s ability, stay time and worker’s movement to improve the accuracy of recruitment probability. Considering the influence of worker’s movement on recruitment probability, a worker’s movement prediction algorithm based on meta-path is proposed to analyze worker’s movement. The experimental results show that compared with the existing algorithms, the algorithm in this paper can guarantee the time constraint of the task, and have better performance in terms of spatial coverage and running time.