Wisdom of Crowds: A Human-Machine-Things Cooperative Scheduling Method for Heterogeneous Mobile Crowdsensing

Relying on the development of crowdsourcing ideas and mobile crowd sensing (MCS) technology, many tasks that originally required a lot of manpower and material resources have been solved efficiently. However, with the development of urbanization, the traditional MCS systems have gradually been unable to cope with the demands of massive sensing tasks and high spatio-temporal sensing coverage. The challenges are as follows: 1) The scarcity of participants and the limitation of human motion rules lead to the existence of spatio-temporal blind spots in the process of collecting sensing data; 2) The single type of participants limits the sensing ability of the system and the types of data that can be collected, which affects sensing precision and quality. With the emergence of various intelligent sensing terminals in the city, the heterogeneous crowd sensing that integrates human, machine and things participants has become a new generation of sensing mode. In the spatio-temporal related sensing scenarioes, this article designs a new human-machine-things cooperative scheduling (HMT-CS) algorithm framework by comprehensively considering the diverse sensing skills, spatio-temporal trajectories, sensing costs of heterogeneous participants and system total budget constraints. The algorithm can match suitable heterogeneous participants for each task, which greatly improves the sensing quality, sensing fairness and overall utility of heterogeneous MCS systems. We combined multiple public real urban datasets to conduct an in-depth comparative analysis and comprehensive evaluation of the algorithm, and the results show that our method is superior to other baselines in all indicators.