Participatory sensing has become an effective way of sensing urban dynamics due to the widespread availability of smartphones among citizens. Traditionally, separate urban sensing applications are designed to monitor different urban dynamics like environment, transportation, mobility, etc. However, combining these applications to aggregate information can lead to various new inferences. The main objective of this work is to improve urban sensing applications by overcoming their individual limitations. A unified framework called JUSense (Judicious Urban Sensing) is proposed that can derive benefits from these applications by combining their functionalities. JUSense provides the opportunity for applications to tackle the challenges associated with data collection, aggregation of data in cloud, calibration, data cleaning, and prediction. A multi-view fusion model is proposed for spatiotemporal urban air and noise pollution map generation. Further, a random forest classifier is built to classify the driving events. Here, large scale experiments are performed to evaluate the efficacy of JUSense on real-world dataset. Both the fusion model and the random forest classifier yield better accuracies compared to the baseline methods. Additionally, case studies are conducted to show the advantages that can arise out of the mutual interactions among the applications.

中文翻译：

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JUSense：基于参与的城市传感系统的统一框架

由于公民中智能手机的广泛普及，参与式传感已成为传感城市动态的有效方法。传统上，单独的城市传感应用程序被设计为监视不同的城市动态，例如环境，交通，出行等。但是，将这些应用程序组合起来以汇总信息可能会导致各种新的推断。这项工作的主要目的是通过克服城市个体应用的局限性来改善其应用。提出了一个统一的框架，称为JUSense（明智的城市感知），可以通过组合这些应用程序的功能来从这些应用程序中受益。JUSense为应用程序提供了解决与数据收集，云中的数据聚合，校准，数据清理和预测相关的挑战的机会。提出了一种用于时空城市空气和噪声污染图生成的多视图融合模型。此外，构建了随机森林分类器以对驾驶事件进行分类。在这里，进行大规模实验以评估JUSense在真实数据集上的功效。与基线方法相比，融合模型和随机森林分类器均具有更好的准确性。此外，还进行了案例研究，以显示应用程序之间的相互影响所产生的优势。与基线方法相比，融合模型和随机森林分类器均具有更好的准确性。此外，还进行了案例研究，以显示应用程序之间的相互影响所产生的优势。与基线方法相比，融合模型和随机森林分类器均具有更好的准确性。此外，还进行了案例研究，以显示应用程序之间的相互影响所产生的优势。