Spatio-temporal Feature Based Multi-participant Recruitment in Heterogeneous Crowdsensing

Abstract

Mobile crowdsensing (MCS) collects sensing data by recruiting task participants to realize large-scale sensing tasks in cities. However, due to the limitations of human activity range and sensing mode, relying only on human participants to achieve this process will lead to sensing blind areas, ultimately affecting the integrity and validity of sensing data. With the rise of unmanned vehicles (UVs) and sensor-assisted MCS research, it provides new inspirations for solving complex sensing tasks in smart cities. In this article, we propose heterogeneous crowdsensing, which includes heterogeneous participants such as human participants, UVs, and fixed sensors. Our goal is to accomplish large-scale, high-quality urban sensing tasks by collaborating with these three types of heterogeneous participants. To solve the collaborative sensing problem, we propose an algorithm called spatio-temporal PPO (STPPO). We first define the capability and cost attributes of the heterogeneous participants and then divide the large-scale sensing area into a set of subregions by a subgraph construction method. Based on the spatio-temporal characteristics of the subregions and the attributes of the heterogeneous participants, we finally solve the cooperative scheduling problem of the subregions using proximal policy optimization (PPO) algorithms to maximize the overall POI collection rate and collection fairness. Finally, extensive experiments are conducted based on real datasets. The overall results of STPPO outperform other baselines, with a 30.19% performance improvement compared to the PPO algorithm.