Design of a Decentralized and Predictive Real-Time Framework for Air Pollution Spikes Monitoring
Publication Type
Conference Proceeding
Publication Date (Issue Year)
2021
Journal Name
2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA),
Abstract
Exposure to air pollution spikes cause health problems to regularly exposed organisms, raising the need to monitor them in real-time. Existing air pollution monitors mainly use a cloud-centric design considering relatively constant pollution, therefore duty-cycling sensors with long sleep periods to save their batteries. Such design is however inefficient for monitoring pollution spikes. Furthermore, since spikes vanish rapidly, integrity of monitoring data is very important. This paper presents a framework integrating edge-centric design and blockchain in monitoring air pollution spikes, while using short-term prediction artificial intelligence to timely alert pollution emitters about exceeding long-term average pollution limits defined by standards
Keywords
Decentralized, Predictive, Real-Time Framework, Air Pollution Spikes, Monitoring
Rsif Scholar Name
Eric Nizeyimana
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Rwanda (UR), Rwanda
Recommended Citation
Nizeyimana, E., Hanyurwimfura, D., Shibasaki, R., & Nsenga, J. (2021). Design of a Decentralized and Predictive Real-Time Framework for Air Pollution Spikes Monitoring. 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA),, 2021, 501-504. https://doi.org/10.1109/ICCCBDA51879.2021.9442611