Design of a Decentralized and Predictive Real-Time Framework for Air Pollution Spikes Monitoring

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dc.contributor.author Nizeyimana, Eric
dc.contributor.author Hanyurwimfura, Damien
dc.contributor.author Shibasaki, Ryosuke
dc.contributor.author Nsenga, Jimmy
dc.date.accessioned 2021-07-08T05:05:58Z
dc.date.available 2021-07-08T05:05:58Z
dc.date.issued 2021-06-02
dc.identifier.uri https://repository.rsif-paset.org/xmlui/handle/123456789/96
dc.description Conference paper presented at the 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, China: https://doi.org/10.1109/ICCCBDA51879.2021.9442611 en_US
dc.description.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. en_US
dc.publisher IEEE en_US
dc.subject Decentralized and Predictive Real-Time Framework, Air Pollution Spikes Monitoring en_US
dc.title Design of a Decentralized and Predictive Real-Time Framework for Air Pollution Spikes Monitoring en_US
dc.type Presentation en_US

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