Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing

TL;DR

This paper introduces a neural network framework for continuous-time data inference in sparse urban crowd sensing, improving accuracy over traditional discrete-time methods by capturing temporal dynamics across unequal intervals.

Contribution

It proposes TIME-DMF, a novel neural network model that captures continuous temporal data, along with a Q-G strategy to handle infinite states, advancing sparse crowd sensing inference.

Findings

TIME-DMF outperforms existing methods in accuracy.

The models effectively capture temporal correlations.

Time-continuous completion reduces inference errors.

Abstract

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.