Paper Title
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.