LoRa Air Pollution Monitoring Network Using Data Fusion Algorithm
The success of the LoRa network deployment is entirely dependent on the quality of services (QoS), that
considered the issues of data redundancy, delays, and network lifetime. The level of air pollution has increased over time
due to several factors such as an increase in population, increased vehicle use, industrialization, and urbanization, which
results in harmful effects on human well-being by directly affecting the people exposed to it. This paper presents a LoRa Air
Pollution Monitoring system developed to monitor pollution using the LoRa networking components. The network system is
based on the sensor connectivity to the LoRa modules. These are directly linked to The Things Network server to obtain
results from the sensor nodes, and data is displayed/stored on the cloud with a graphical application using ubidots. The
ubidots website generates a graphical user interface for online real-time monitoring of the air pollutants, carbon dioxide,
particular matter and temperature/humidity values. The network generates continuous data, which can cause redundancy in
the network and affect the network life cycle. To overcome the issue of the LoRa network to reduce data redundancy, it
requires implementing a data fusion algorithm.
Keywords: LoRa, sensors, data fusion algorithm.