Mobile Sensing in Metropolitan Area: Case Study in Beijing

During the vast trend of urbanization, mobile sensing in metropolitan area has become an emerging fashion and prevailing technology to monitor the environmental changes and human activities in the city scale. In this paper, we propose a novel framework, namely, the Context-Aware Metropolitan Sensing (CAMS), to rise to the increasing challenges in context acquisition, context fidelity, context dynamics and context complexity. CAMS is an high level framework that focus on knowledge discovery among distributed or mobile users, and loose coupled with specific communication and networking technology. By a case study of Beijing road roughness evaluation, we propose decision-tree based machine learning algorithm to gain knowledge from 3-axis accelerometers and GPS receivers. The results show how the CAMS framework can be used to develop city-scale mobile sensing applications. Author