Environment-Driven Task Allocation in Heterogeneous Spatial Crowdsourcing

In the era of mobile computing and sharing econ-omy, spatial crowdsourcing has become an emerging paradigm where participants who meet the spatial requirements are actively joined in various tasks. However, previous work did not fully consider the heterogeneous nature of tasks with both spatio-temporal and sensing requirements. And for the participants, they contribute different amounts and types of sensing data due to the fact that their devices usually have various sensing capabilities. In light of this, it is desired to study the task allocation problem in such a heterogeneous spatial crowdsourcing scenario. Specifically, to accommodate the dynamic and complex features of this scenario, we design a Multi-Agent Soft Actor-Critic algorithm (TA-DSAC) that relies on spatio-temporal con-straint. Firstly, we construct available task allocation regions according to the spatio-temporal characteristics of the tasks and participants, as well as the matching degree and aggregate sensing quality, and then set an agent for each region. Next, the agents are trained based on discretized Soft Actor-Critic (SAC) and Centralized Training with Decentralized Execution (CTDE), making the agents self-adaptive to changes in this crowdsourcing environment. Extensive evaluations with real datasets justify the effectiveness of our proposed algorithm.