Research Article
Environment 4.0: An IoT-Based Pollution Monitoring Model
@INPROCEEDINGS{10.1007/978-3-031-06374-9_19, author={Nathan Mbayo and Hloniphani Maluleke and Olasupo Ajayi and Antoine Bagula}, title={Environment 4.0: An IoT-Based Pollution Monitoring Model}, proceedings={e-Infrastructure and e-Services for Developing Countries. 13th EAI International Conference, AFRICOMM 2021, Zanzibar, Tanzania, December 1-3, 2021, Proceedings}, proceedings_a={AFRICOMM}, year={2022}, month={5}, keywords={Environment monitoring Air pollution Noise pollution Internet-of-things Fourth industrial revolution}, doi={10.1007/978-3-031-06374-9_19} }- Nathan Mbayo
Hloniphani Maluleke
Olasupo Ajayi
Antoine Bagula
Year: 2022
Environment 4.0: An IoT-Based Pollution Monitoring Model
AFRICOMM
Springer
DOI: 10.1007/978-3-031-06374-9_19
Abstract
While professional pollution monitoring stations are used worldwide to measure levels of pollution, they are usually costly and sparsely deployed across cities, hence leading to a visibility gap in pollution maps that need to be filled through alternative solutions. This paper proposes a pollution monitoring model designed within the “Environment 4.0” project to showcase how fourth industrial revolution technologies such as the Internet-of-Things (IoT) can fill such visibility gap using low cost off-the-shelf devices. The validation of our approach was done by developing two prototypes of a pollution monitoring system. These include a system built upon a Raspberry Pi and an android based IOIO micro-controller. Using a testbed experimentation approach, the two systems were validated through a number of scenarios, where both air and noise pollution levels were measured in certain locations of the city of Lubumbashi in the Democratic Republic of Congo. The relative values obtained from the two IoT devices validate the designed systems as they revealed that i) heavy traffic locations experienced higher air pollution ii) the average level of PM2.5 outside buildings over one day of observation was lower in less densely occupied suburbs compared to the city center and iii) high noise levels were observed in locations referred to in our experiments as “red light districts” which were expected to be more noisy because of the type of activities carried in such locations. The experimental results revealed that the designed system will indeed make it possible to address the visibility gap problem in the near future at an affordable cost.
