Fine-Grained Air Pollution Inference with Mobile Sensing Networks
Prof. Lin ZHANG Professor, Electronic Engineering Department, Tsinghua University
Abstract
Due to the widely spreading concerns on hazardous air pollutions in many cities, air quality monitoring is becoming a priority for city residents and urban administrators. Besides statically deployed official air quality observatories, mobile sensing systems, consisted of low-cost gas sensors and mobile carriers, are deployed to achieve greater temporal-spatial granularity and broader coverage. These systems enable higher possibilities to capture detailed environmental variations, but also bring numbers of new challenges on data processing and analyzing. This talk will focus on the air pollution inference algorithms over mobile sensing systems we deployed in several cities in China. The algorithms addressed unique problems incurred by mobile sensing, including, how to merge the data driven methodologies with domain knowledges, how to deal with irregularly distributed mobile sensed data, and how to reduce costs on investigating the pollution causes. Results of several case studies in north China will also be presented.
Biography
Lin Zhang (B.Sc. '98, M.Sc. '01, Ph.D. '06, all from Tsinghua University, Beijing, China) is currently a professor at Tsinghua University and the director of Tinghua-Berkeley Shenzhen Institute. His current research focuses on sensor networks, data and knowledge mining, and information theory. He is a co-author of more than 100 peer-reviewed technical papers and 11 U.S. or Chinese patents. Lin and his team were also the winner of IEEE/ACM SenSys 2010/IPSN 2014 Best Demo Awards, 2013 IEEE CASE Best Paper awards, 2015 EU Dragon Award, and 2016 IEEE PES Best Student Paper Award.