  The increasing demand for sensing, collecting, transmitting, and processing
vast amounts of data poses significant challenges for resource-constrained
mobile users, thereby impacting the performance of wireless networks. In this
regard, from a case of mobile crowdsensing (MCS), we aim at leveraging
attention mechanisms in machine learning approaches to provide solutions for
building an effective, timely, and secure MCS. Specifically, we first evaluate
potential combinations of attention mechanisms and MCS by introducing their
preliminaries. Then, we present several emerging scenarios about how to
integrate attention into MCS, including task allocation, incentive design,
terminal recruitment, privacy preservation, data collection, and data
transmission. Subsequently, we propose an attention-based framework to solve
network optimization problems with multiple performance indicators in
large-scale MCS. The designed case study have evaluated the effectiveness of
the proposed framework. Finally, we outline important research directions for
advancing attention-enabled MCS.
