Task combination optimization via dual-view heterogeneous graph contrastive learning for mobile crowdsensing

Mobile crowd sensing is an intelligent sensing technology that relies on mobile devices and user participation. Its core is to use widely distributed user groups to complete various sensing tasks. However, due to factors such as differences in user abilities, geographical locations and user participation motivations, task allocation often becomes imbalanced, leading to the reduction of the task completion ratio. To address the above problem, a task combination optimization method based on dual-view heterogeneous graph contrastive learning is proposed. First, by constructing a heterogeneous graph model, the task features and external dependencies (such as user behavior and scene conditions) are mapped to different nodes and edges in the graph structure. Second, from the perspectives of multi-path and edge features, the heterogeneous graph contrastive learning method is used to learn the representation of task nodes. The node classes are predicted based on these learned representations, and task nodes of the same class are combined to form a task cluster. Finally, based on the similarity of sensing users, multiple collaboration groups are formed. Task clusters are allocated according to the satisfaction of group members. Experiments using the Yelp, Freebase, Epinions and DBLP datasets show that our proposed method demonstrates relatively strong generalization ability and significantly improves the relevance of tasks in the combination. In addition, in the task allocation experiments, our proposed method successfully enhances user satisfaction and task completion ratio.