Existing air quality (AQ) monitoring and management (AQMS) methods and evolving modelling practices across Norwegian and European cities have achieved significant improvements of AQ but further progress is needed due to some quality-driven requirements, such as low-latency AQ prediction. This can only be achieved by intelligent data processing at multiple levels of granularity. To this end, affordable, effective and intelligent tools are needed that utilize the current advances in digitization of all spheres of society, providing radical innovation of air quality management. The AirQMan project promises autonomous computational methods and techniques that can be used to develop such solutions, and has the potential for opening up a new era in air quality management. Our strong belief is that such a system can be realized across the Edge-Fog-Cloud continuum, extending data processing and computational intelligence from the Cloud to multiple levels of Fog nodes towards the edge of the network. The project will develop AirQDM – a novel data processing design model that will autonomously determine the optimal data fusion processing flow, the right data sources, and the right trained deep learning (DL) model for maximizing the accuracy of a prediction related to an AQ request. A second innovation of the project, AirQWare will determine (predict) the optimal distributed deployment for an efficient computation of the DL model while satisfying requirements on accuracy and latency, and adapt the deployment of the DL model during runtime as necessary to maintain accuracy and latency requirements. By applying the AirQMan approach, the new generation of AQMS will provide: i) low-latency data validation and fusion to increase the accuracy of air quality evaluation, and to support intelligent services, respectively, and ii) cognitive decision making with various degrees of autonomy enabling low-latency actuations of AQ mitigations.
Project leader: Amir Taherkordi
Institution: Institutt for informatikk