Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing

Abstract EN

Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users.

However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources.

Besides, the data are also time labelled.

The heterogeneous and time sequential data raise new challenges for data analyzing.

Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information.

In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related.

In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context.

Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning.

We evaluate that our proposed method can adapt data generated by different orientations effectively.

Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors.

It achieves precisions of 98.6 % and 93.7 % in distinguishing cycling from walking and bus from driving, respectively.