KNN-ST: Exploiting Spatio-Temporal Correlation for Missing Data Inference in Environmental Crowd Sensing

Sparse mobile crowdsensing is a new crowdsensing paradigm which leverages the spatial and temporal correlation between data sensed at different locations over time to reduce the overall sensing cost by significantly reducing the number of sensing tasks. Consequently, only sparsely selected spatio-temporal cells would be reporting the sensed data, whereas data for the rest of the cells would have to be inferred from the sensed data. This process, which is largely known as missing data inference is the focus of this study. We examine the KNN (K-Nearest Neighbor) approach, which is known to be relatively faster and simpler. However, it is generally accepted to perform poorly when the sensed data is sparse. In the context of environmental crowd sensing, we examine whether it is a viable missing data inference approach if we incorporate the spatio-temporal correlation of data in the algorithm, instead of just exploiting either the spatial or the temporal correlation independently. Thus, we examine three variants of KNN: KNN-ST (KNN-Spatio-Temporal), KNN-S (KNN-Spatial), and KNN-T (KNN-Temporal) on sparse data. Besides, we find that voxelization is a natural way of exploiting the spatio-temporal properties of sensed data and thereby the spatio-temporal correlation between them. Interestingly, we find that KNN-ST indeed shows good performance (normalized absolute error of about 0.1) even when the loss probability is as high as 0.9. Additionally, we implement an existing method on the same experimental datasets and present corresponding comparative simulation results.