Enabling distributed intelligence in Internet of Things: an air quality monitoring use case
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ORIGINAL ARTICLE
Enabling distributed intelligence in Internet of Things: an air quality monitoring use case Noussair Lazrak1
· Jamal Ouarzazi1 · Jihad Zahir1 · Hajar Mousannif1
Received: 21 April 2020 / Accepted: 10 November 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Distributed learning · Distributed intelligence · Shared knowledge · Distributed machine learning · Internet of Things · Air quality monitoring · Air quality prediction
1 Introduction Air quality is one of the major concerns among policymakers and the public because of its impacts on humans and environment. Air pollution can be one of the reasons behind civilization’s diseases. Actually, more than 95% of the world’s population are breathing polluted air according to the Health Effects Institute (HEI) [1], which has contributed to the deaths of more than six million people worldwide in 2016 alone. As a result, air pollution is ranked fourth among the world’s leading causes of death which come behind smoking, blood pressure, and diet. Therefore, an early warning system based on accurate forecasting tools must be put in place to avoid the adverse effects of exposure to major air pollutants. Air quality information systems
Noussair Lazrak
[email protected] 1
Cadi Ayyad University, Marrakesh, Morocco
are increasingly being used to predict air pollution levels, thereby minimizing exposure to polluted air to prevent adverse effects [2, 3]. Many research projects have been employing observation-based
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