With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant’s exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human’s micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the exact geolocation (which is privacy invasive), or simply applying some a priori rules (which is privacy friendly). The experimental results, applied to real MCS data, not only confirm the power of MCS and air quality (AQ) data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, and during the training phase, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific hardware. However, during the application of models on new data, the deep learning algorithm fails to outperform our proposed models.

随着传感器技术和移动计算的快速发展，移动人群感知（MCS）已成为收集大规模丰富轨迹数据的新范式。游牧传感器使人和物体能够报告和分享对其状态、行为和/或周围环境的观察结果。在 MCS 中处理和挖掘多源传感器数据由于其多维性质而提出了若干挑战，其中测量的参数（即维度）在质量、可变性和时间尺度方面可能不同。我们考虑空气质量 MCS 的背景，并专注于从 MCS 数据中挖掘微环境的任务。将这些措施与他们的微观环境联系起来对于解释它们和正确分析参与者的暴露至关重要。在本文中，我们专注于调查在环境 MCS 场景中识别人类微环境的可行性问题。我们提出了一种新颖的方法，用于从用户的轨迹中学习和预测用户的微环境，这些轨迹富含以多维时间序列和 GPS 轨迹表示的环境数据。我们提出了一种适应我们上下文的多视图学习方法，并与其他时间序列分类方法一起实施。我们将所提出的方法扩展到一种混合方法，该方法采用轨迹分割来发挥两种方法的最佳效果。我们通过分析确切的地理位置（侵犯隐私）或简单地应用一些先验规则（对隐私友好）来优化提出的方法。实验结果，应用于真实的 MCS 数据，不仅证实了 MCS 和空气质量 (AQ) 数据在表征微环境方面的作用，而且还显示了移动数据整合在这一认识中的适度影响。此外，在训练阶段，多视图学习显示出与参考深度学习算法相似的性能，而不需要特定的硬件。然而，在将模型应用于新数据期间，深度学习算法未能超越我们提出的模型。