Smart cities air pollution monitoring system - Developing a potential data collecting platform based on Raspberry Pi
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Date
2019
Authors
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Publisher
University of the Western Cape
Abstract
Air pollution is becoming a challenging issue in our daily lives due to advanced industrialization.
This thesis presents a solution to collection and dissemination of pollution data. Most of the
devices that monitor air quality are costly and have limited features. The aim of this study is to
revisit the issue of pollution in cities with the aim of providing a cheaper and scalable solution
to the challenge of pollution data collection and dissemination. The solution proposed in this
paper uses Raspberry Pi and Arduino micro-controller boards as the foundation, combined with
specific sensors to facilitate the collection and transfer of pollution data reliably and effectively.
While most traditional air pollution monitoring equipment and similar projects use memory cards
as a medium for data storage, the system proposed in this research is built around a new network
selection model that transfers data to the server by using either Bluetooth, Wi-Fi, GSM, or the
LoRa protocol. The connectivity protocol is selected automatically and opportunistically by the
network selection algorithm defined in the micro-controller board. The final data will be
presented to the user through a mobile application and website interface effectively and
intuitively after being processed in the server. This data transfer system can effectively reduce
the cost and input of human resources. It is a viable solution. For other environmental research,
this system can provide an air quality data support for analysis and reference. Modularity and
cost-effectiveness are fully considered when designing the system. It is a viable solution. We can
generalize the system by slightly changing the data transmission modules. In other case, it can
be used as a platform for similar data transmission and offer help for other research directions.
Description
>Magister Scientiae - MSc
Keywords
Air pollution, Raspberry Pi, Arduino, Networking Selection, Industrialization