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Enabling distributed intelligence in Internet of Things: an air quality monitoring use case

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Abstract

Air pollution is worsening almost everywhere in the world. According to the Health Effects Institute (HEI), more than 95% of the world population breathe polluted air, toxic to their cardiovascular and respiratory health, which caused the death of 4.2 million people worldwide in 2016. As a result, the air pollution has become one of the leading causes of death worldwide. Therefore, an early cost-efficient warning system based on precise forecasting tools must be put in place to measure and avoid the harmful effects of exposure to the main air pollutants. Thus, it is essential to obtain reliable analytical information on air quality in a specific time and place. This paper focuses on monitoring air quality using a distributed intelligence which is a cost-efficient solution that enables a flexible prediction process distributed within a network of nodes and devices using a cross-platform solution. The suggested architecture enables collaborative learning along with collective knowledge graph building and knowledge sharing using the state of the art in Internet of Things, distributed machine learning, and ontologies. The proposed architecture suggests a flexible prediction system personalized for each node based on its need of information. Similar nodes get together for collective learning which allows for resource optimization, knowledge reusability, and device interoperability. The paper describes the modeling framework of distributed intelligence monitoring and analysis system designed for urban regions.

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Authors and Affiliations

  1. Cadi Ayyad University, Marrakesh, Morocco

    Noussair Lazrak, Jamal Ouarzazi, Jihad Zahir & Hajar Mousannif

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  1. Noussair Lazrak
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  2. Jamal Ouarzazi
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  3. Jihad Zahir
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  4. Hajar Mousannif
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Correspondence to Noussair Lazrak.

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Lazrak, N., Ouarzazi, J., Zahir, J. et al. Enabling distributed intelligence in Internet of Things: an air quality monitoring use case. Pers Ubiquit Comput 27, 2043–2053 (2023). https://doi.org/10.1007/s00779-020-01483-3

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  • DOI: https://doi.org/10.1007/s00779-020-01483-3

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