AN IOT BASED APPROCH FOR MEASURING AIR POLLUTION LEVEL

Computer Science And Engineering

Traditional air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from the monitoring stations may not be easily accessible. With the advent of Internet of things(IoT) technology, millions of smart devices will be connected to each other as well as to the internet enabling easier air pollution monitoring. Low cost portable sensors along with IoT can overcome the above two issues of traditional monitoring systems. This thesis focuses mainly on three aspects. Firstly, research, development and deployment of IoT-based dense air pollution monitoring using network low cost sensor nodes in an Indian urban setting which is first of kind in India in terms of dense deployment and spatial coverage. Second, it deals with improving the energy consumption of these sensor nodes developed and finally, reducing the redundancy of the nodes. More specifically, an adaptive sensing algorithm and hierarchical clustering based spatial sampling paradigm have been introduced. The results of the proposed implementations have been evaluated upon real-time deployment of the nodes. In total, ten low-cost IoT nodes monitoring particulate matter(PM), which is one of the most dominant pollutants, are developed and deployed in a small educational campus in Indian city of Hyderabad. New data sets were created in the process which is thoroughly investigated by applying various preprocessing, signal processing and clustering techniques. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators.

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