A Complex Multidimensional Task Allocation Method for Vehicular Crowdsensing Based on the Collaborative of Nodes

With the development of intelligent driving and communication technology, tasks in the vehicular crowdsensing (VCS) system develop in a large-scale and complex direction, requiring higher data dimensions and more data types to be collected. Due to the diversity of sensing capabilities of vehicle nodes and the uneven spatial and temporal distribution in the road network, it is difficult for a single vehicle to complete such complex data collection tasks individually. This paper proposes a task allocation method based on cooperative sensing of vehicle nodes for complex multidimensional tasks in the VCS system. The sensing ability of vehicle nodes and the spatio-temporal correlation between nodes and tasks are considered to allocate single-dimensional subtasks after dimensionality reduction of complex tasks. Simulation experiments are conducted to analyze and evaluate the algorithm in this paper. Results show that our algorithm can reduce node energy consumption and data collection cost while ensuring task completion rate.