An IoT Solution for Air Quality Monitoring and Hazard Identification for Smart City Development

Many IoT solutions are focused on air quality monitoring. The applicability of IoT to the entire process of hazard identification has not been fully harnessed. This paper conceptualized a smart IoT architecture for air quality monitoring and hazard identification. Notably, a data governance layer is...

Full description

Saved in:
Bibliographic Details
Main Authors: Ogu, Reginald Ekene, Achumba, Ifeyinwa E., Okoronkwo, Chinomso D., Chukwudebe, Gloria A., Chukwuchekwa, Nkwachukwu
Format: Conference Proceeding
Language:English
Subjects:
Air pollution
Air Pollution Control
Crowdsourcing
Crowdsourcing Web Application
Data governance
Data visualization
Environmental monitoring
Hazards
IoT Architecture
Risk Management
Service-oriented architecture
Smart cities
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many IoT solutions are focused on air quality monitoring. The applicability of IoT to the entire process of hazard identification has not been fully harnessed. This paper conceptualized a smart IoT architecture for air quality monitoring and hazard identification. Notably, a data governance layer is part of the proposed cloud centric database architecture, to tackle the concerns about data governance. For the architecture, JavaScript Object Notation was adopted as a standard for data exchange to enable interoperability among components from different service providers and device manufacturers. As a proof of concept, an IoT node for air quality monitoring was prototyped. A web application with crowdsourcing capability was developed to provide a platform through which users can report and be notified of hazardous incidents. The results obtained during the testing phases of the two architectural components developed herein shows that the proposed architecture is characterized with adequate interoperability. Specifically, the air quality node logged air quality data to two different cloud databases: Google Firebase and Thingspeak platform. Subsequently, the air quality data was tunneled from the databases and visualized on the Web Application developed herein. By developing a solution for hazard identification, this research has contributed towards smart city development.
ISSN:2377-2697
DOI:10.1109/NIGERCON54645.2022.9803129