Multi-Task Assignment in Sparse Crowdsensing Using Intra-Task and Inter-Task Data Correlations

Sparse Mobile Crowdsensing (Sparse MCS) is an emerging paradigm for data collection that significantly reduces the cost of smart city data acquisition while ensuring data quality by collecting data only from a subset of sensing cells and inferring data from the remaining unsensed cells using spatial and temporal correlations. Existing research has primarily focused on single sensing task scenarios, yet practical applications often involve multiple types of sensor data. These data types exhibit correlations that can be leveraged to optimize task allocation, thereby reducing sensing costs. This paper addresses the task assignment problem in multi-task scenarios, proposing the MDIA method based on information entropy theory and similarity matrices to finely assess the importance of data points. Building upon this, we further propose the MTA method, which employs a hybrid sensing mode (combining participatory sensing and opportunistic sensing) to select appropriate participants for sensing critical areas. Extensive experiments conducted on two real-world sensing datasets containing multiple data types validate the effectiveness of the proposed methods.