Fine-grained Urban Prediction via Sparse Mobile CrowdSensing

Abstract

Mobile CrowdSensing (MCS) has recently emerged as a practical paradigm for large-scale and fine-grained urban sensing systems. To reduce sensing cost, Sparse MCS only senses data from a few subareas instead of sensing the full map, while the other unsensed subareas could be inferred by the intradata correlations among the sensed data. In certain applications, users are not only interested in inferring the data of other unsensed subareas in the current sensing cycle, but also interested in predicting the full map data of the near future sensing cycles. However, the intradata correlations exploited from the historical sparse sensed data cannot be effectively used for predicting full data in the temporal-spatial domain. To address this problem, in this paper, we propose an urban prediction scheme via Sparse MCS consisting of the matrix completion and the near-future prediction. To effectively utilize the sparse sensed data for prediction, we first present a bipartite-graph-based matrix completion algorithm with temporal-spatial constraints to accurately recover the unsensed data and preserve the temporal-spatial correlations. Then, for predicting the fine-grained future sensing map, with the historical full sensing data, we further propose a neural-network-based continuous conditional random field, including a Long Short-Term Memory component to learn the non-linear temporal relationships, and a Stacked Denoising Auto-Encoder component to learn the pairwise spatial correlations. Extensive experiments have been conducted on three real-world urban sensing data sets consisting of five typical sensing tasks, which verify the effectiveness of our proposed algorithms in improving the prediction accuracy with the sparse sensed data.