Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing
<|reference_start|>Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing: Mobile Crowd Sensing (MCS) is a promising paradigm that leverages mobile users and their smart portable devices to perform various real-world tasks. However, due to budget constraints and the inaccessibility of certain areas, Sparse MCS has emerged as a more practical alternative, collecting data from a limited number of target subareas and utilizing inference algorithms to complete the full sensing map. While existing approaches typically assume a time-discrete setting with data remaining constant within each sensing cycle, this simplification can introduce significant errors, especially when dealing with long cycles, as real-world sensing data often changes continuously. In this paper, we go from fine-grained completion, i.e., the subdivision of sensing cycles into minimal time units, towards a more accurate, time-continuous completion. We first introduce Deep Matrix Factorization (DMF) as a neural network-enabled framework and enhance it with a Recurrent Neural Network (RNN-DMF) to capture temporal correlations in these finer time slices. To further deal with the continuous data, we propose TIME-DMF, which captures temporal information across unequal intervals, enabling time-continuous completion. Additionally, we present the Query-Generate (Q-G) strategy within TIME-DMF to model the infinite states of continuous data. Extensive experiments across five types of sensing tasks demonstrate the effectiveness of our models and the advantages of time-continuous completion.<|reference_end|>
@article{sun2024toward,
    title={Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing},
    author={Ziyu Sun, Haoyang Su, Hanqi Sun, En Wang, Wenbin Liu},
    journal={arXiv preprint arXiv:2408.16027},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16027},
    primaryClass={cs.LG cs.AI cs.NI}
}