Prof. Dr. Michael Beigl
CESL, Tsinghua University (China)
Prof. Dr. Lin Zhang
What is the innovation and what is it about?
PiMi Airbox, a low-cost particulate monitor for indoor air quality, measures PM2.5 and transmits measurements via the user’s mobile phone to the PiMi online community. The PiMi online community visualises, analyses and shares sensing data as well as users’ know-how based on PiMi cloud service.
The innovation is a new approach of indoor air quality sensing. The developed PiMi Air Community system addresses the dynamic feature of air pollution in an innovative participatory manner, which consists of 3 information presentation layers: smart device, mobile Apps and online community. This combination provides a novelty in terms of personalised air quality service.
The mobile wearable devices for the development of PiMi Airbox were prototyped at Tsinghua independently. KIT in turn has developed wearable air quality sensors. The low-cost sensors that are employed in both modalities have issues in terms of noise and calibration stability that were addressed in the joint research between KIT and Tsinghua.
What are the key impacts?
KIT and Tsinghua expect that their massive crowd-sourced deployment of PM monitors can easily gain threefold higher resolution in terms of magnitude to investigate the spatio-temporal distribution of fine dust in urban areas.
The research platform based on the innovation will enhance the development of big data technologies, with which the dynamic PM2.5 data with high spatio-temporal resolution will allow to gain more in-depth understanding of air quality effects on the human health.
What are the added values and what were the biggest challenges of your cooperation?
The most added value of the cooperation is the established research platform for air quality data analytics. Whether the PiMi Airbox being sold individually or incorporated into existing devices or systems, these sensors and the processing systems could enable dense dynamic pollution mapping in smart cities, especially when networked or deployed in participatory sensing scenarios. The outcome of this is a huge amount of data.
The long-term biggest challenge for both KIT and Tsinghua are joint research activities in the field of participatory sensing and big data analytics, which can be continuously conducted based on the PiMi platform through structuring Big Data results to create new information.