Abstract Data quality and budget are two major concerns in large-scale urban Mobile Crowdsensing (MCS) technologies. Traditional MCS research primarily measures data quality based on sensing coverage, without fully considering the importance of the data for downstream tasks. As a result, in Sparse Mobile Crowdsensing (SMCS), existing studies often focus on selecting the subregions most valuable for data inference, overlooking the ultimate goal of data collection, which is to support subsequent decision-making. With the rise of Embodied AI, a core challenge is how to actively collect key information that is most helpful for decision-making under conditions of sparse data and high costs. For example, in urban air quality monitoring, insufficient sensor deployment may cause critical polluted regions to go unobserved, undermining public health decisions. Similarly, sparse traffic data can lead to errors in autonomous driving systems, such as flawed route planning or hazard detection failures. As a feasible data collection paradigm for Embodied AI, existing SMCS subregion selection methods often ignore the relevance of data to tasks, thereby affecting decision-making in Embodied AI. To address this, we design a Sparse Mobile Crowdsensing active perception framework that selects the most valuable subregions for decision-making, considering the context of downstream tasks. Furthermore, existing SMCS subregion selection methods usually focus only on selecting the optimal subregion, whereas Embodied AI requires selecting a set of subregions. Choosing only the optimal subregion may overlook information overlap between subregions, leading to wasted data collection costs and reducing the perception efficiency of Embodied AI. This paper proposes an active acquisition strategy that takes into account parameter uncertainty and probabilistic margins, enabling the selection of an optimal set of subregions. Since this is an NP-hard problem, we approximate the proposed strategy using a greedy algorithm and provide performance guarantees for this approximation through theoretical proofs. Finally, we conduct experiments on three real-world datasets to validate the effectiveness of our proposed method. Experimental results show that when the data missing rate exceeds 60%, MBBALD starts to outperform other algorithms. On the traffic flow dataset, it achieves over 4% higher accuracy than the second-best algorithm at a 50% missing rate. Its advantage becomes more evident as the number of selected points increases, surpassing the second-best method by over 3% with more selected points.
Huang et al. (Thu,) studied this question.