Robust Data Inference and Cost-Effective Cell Selection for Sparse Mobile Crowdsensing

Abstract

Sparse Mobile CrowdSensing (MCS) aims to reduce sensing cost while ensuring high task quality by intelligently selecting small regions for sensing and accurately inferring the remaining areas. Data inference and cell selection are crucial components in Sparse MCS. However, cell division, which is a prerequisite for cell selection, has received insufficient attention. The existing uniform division method disregards the correlation of the sensing area. In addition, the impact of sparse noise on both data inference and cell selection has been ignored, potentially undermining the effectiveness of Sparse MCS. To address these issues, we propose a novel scheme termed R obust data I nference and C ost- E ffective cell S election for Sparse MCS (Rices). Specifically, we first design an adaptive region division strategy that captures the correlation of sensing regions. Subsequently, we tackle the robust data inference problem in the presence of sparse noise by formulating it as a dual-objective optimization. Furthermore, we optimize the cell selection strategy to dynamically adjust the set of sampled cells under the constraints of data inference quality. Extensive experiments on large-scale real-world datesets are conducted to evaluate the proposed scheme. The results demonstrate that Rices can accurately recover missing data with 20% sparse noise and significantly reduce sensing costs compared to baseline models.